Computational Structural Biology (CSB) is the scientific domain concerned with the development of algorithms
+ and software to understand and predict the structure and function of biological macromolecules. This research
+ field is inherently multi-disciplinary. On the experimental side, biology and medicine provide the objects
+ studied, while biophysics and bioinformatics supply experimental data, which are of two main kinds. On the one
+ hand, genome sequencing projects give supply protein sequences, and ~200 millions of sequences have been
+ archived in UniProtKB/TrEMBL – which collects the protein sequences
+ yielded by genome sequencing projects. On the other hand, structure determination experiments (notably X-ray
+ crystallography, nuclear magnetic resonance, and cryo-electron microscopy) give access to geometric models of
+ molecules – atomic coordinates. Alas, only ~150,000 structures have been solved and deposited in the Protein
+ Data Bank (PDB), a number to be compared against the
+
+ sequences found in UniProtKB/TrEMBL. With one structure for ~1000 sequences, we hardly know anything
+ about biological functions at the atomic/structural level. Complementing experiments, physical
+ chemistry/chemical physics supply the required models (energies, thermodynamics, etc). More specifically, let us
+ recall that proteins with
+
+ atoms has
+
+ Cartesian coordinates, and fixing
+ these (up to rigid motions) defines a conformation. As conveyed by the iconic lock-and-key
+ metaphor for interacting molecules, Biology is based on the interactions stable conformations make with each
+ other. Turning these intuitive notions into quantitative ones requires delving into statistical physics, as
+ macroscopic properties are average properties computed over ensembles of conformations. Developing effective
+ algorithms to perform accurate simulations is especially challenging for two main reasons. The first one is the
+ high dimension of conformational spaces – see
+
+ above, typically several tens of
+ thousands, and the non linearity of the energy functionals used. The second one is the multiscale nature of the
+ phenomena studied: with biologically relevant time scales beyond the millisecond, and atomic vibrations periods
+ of the order of femto-seconds, simulating such phenomena typically requires
+
+ conformations/frames, a (brute)
+ tour de force rarely achieved .
+
+
+
+ Computational Structural Biology: three main challenges.
+
The first challenge, sequence-to-structure prediction, aims to infer the possible
+ structure(s) of a protein from its amino acid sequence. While recent progress has been made recently using in
+ particular deep learning techniques , the models obtained so far
+ are static and coarse-grained.
+
The second one is protein function prediction. Given a protein with known structure i.e. 3D coordinates, the goal is to predict the partners of this protein, in terms of stability
+ and specificity. This understanding is fundamental to biology and medicine, as illustrated by the example of the
+ SARS-CoV-2 virus responsible of the Covid19 pandemic. To infect a host, the virus first fuses its envelope with
+ the membrane of a target cell, and then injects its genetic material into that cell. Fusion is achieved by a
+ so-called class I fusion protein, also found in other viruses (influenza, SARS-CoV-1, HIV, etc). The fusion
+ process is a highly dynamic process involving large amplitude conformational changes of the molecules. It is
+ poorly understood, which hinders our ability to design therapeutics to block it.
+
+ Figure 1: The synergy modeling - experiments, and challenges faced in CSB: illustration
+ on the problem of designing miniproteins blocking the entry of SARS-CoV-2 into cells. From . Of note: the first step of the infection by
+ SARS-CoV-2 is the attachment of its receptor binding domain of its spike (RBD, blue molecule), to a target
+ protein found on the membrane of our cells, ACE2 (orange molecule). A strategy to block infection is therefore
+ to engineer a molecule binding the RBD, preventing its attachment to ACE2. (A) Design of
+ a helical protein (orange) mimicking a region of the ACE2 protein. (B) Assessment of
+ binding modes (conformation, binding energies) of candidate miniproteins neutralizing the RBD.
+
+
Finally, the third one, large assembly reconstruction, aims at solving (coarse-grain)
+ structures of molecular machines involving tens or even hundreds of subunits. This research vein was promoted
+ about 15 years back by the work on the nuclear pore complex . It is often
+ referred to as reconstruction by data integration, as it necessitates to combine coarse-grain
+ models (notably from cryo-electron microscopy (cryo-EM) and native mass spectrometry) with atomic models of
+ subunits obtained from X ray crystallography. Fitting the latter into the former requires exploring the
+ conformation space of subunits, whence the importance of protein dynamics.
+
As an illustration of these three challenges, consider the problem of designing proteins blocking the entry of
+ SARS-CoV-2 into our cells (Fig. ). The first challenge is illustrated
+ by the problem of predicting the structure of a blocker protein from its sequence of amino-acids – a tractable
+ problem here since the mini proteins used only comprise of the order of 50 amino-acids (Fig. (A), ). The second
+ challenge is illustrated by the calculation of the binding modes and the binding affinity of the designed
+ proteins for the RBD of SARS-CoV-2 (Fig. (B)). Finally, the last challenge is
+ illustrated by the problem of solving structures of the virus with a cell, to understand how many spikes are
+ involved in the fusion mechanism leading to infection. In , the promising
+ designs suggested by modeling have been assessed by an array of wet lab experiments (affinity measurements,
+ circular dichroism for thermal stability assessment, structure resolution by cryo-EM). The hyperstable minibinders identified provide starting points for SARS-CoV-2 therapeutics . We note in passing that this is truly remarkable work, yet,
+ the designed proteins stem from a template (the bottom helix from ACE2), and are rather
+ small.
+
+ Figure 2: The main challenges of molecular simulation: Finding significant local minima of the energy
+ landscape, computing statistical weights of catchment basins by integrating Boltzmann's factor, and
+ identifying transitions. Practically,
+
+ .
+
+
+
+ Protein dynamics: core CS - maths challenges.
+
To present challenges in structural modeling, let us recall the following ingredients. First, a molecular model
+ with
+
+ atoms
+ is parameterized over a conformational space
+
+ of dimension
+
+ in Cartesian coordinates, or
+
+
+ in internal
+ coordinate–upon removing rigid motions, also called degree of freedom (d.o.f.). Second,
+ recall that the potential energy landscape (PEL) is the mapping
+
+ from
+
+ to
+
+ providing a
+ potential energy for each conformation , . Example potential energies (PE) are CHARMM, AMBER, MARTINI, etc. Such PE belong to the realm of
+ molecular mechanics, and implement atomic or coarse-grain models. They may embark a solvent model, either
+ explicit or implicit. Their definition requires a significant number of parameters (up to
+
+ ), fitted to reproduce
+ physico-chemical properties of (bio-)molecules .
+
These PE are usually considered good enough to study non covalent interactions – our focus, even tough they do
+ not cover the modification of chemical bonds. In any case, we take such a function for granted .
+
The PEL codes all structural, thermodynamic, and kinetic properties,
+ which can be obtained by averaging properties of conformations over so-called thermodynamic
+ ensembles. The structure of a macromolecular system requires the characterization of
+ active conformations and important intermediates in functional pathways involving significant basins. In
+ assigning occupation probabilities to these conformations by integrating Boltzmann's distribution, one treats
+ thermodynamics. Finally, transitions between the states, modeled, say, by a master
+ equation (a continuous-time Markov process), correspond to kinetics. Classical simulation
+ methods based on molecular dynamics (MD) and Monte Carlo sampling (MC) are developed in the lineage of the
+ seminal work by the 2013 recipients of the Nobel prize in chemistry (Karplus, Levitt, Warshel), which was
+ awarded “for the development of multiscale models for complex chemical systems”. However,
+ except for highly specialized cases where massive calculations have been used , neither MD nor MC give access to the aforementioned time
+ scales. In fact, the main limitation of such methods is that they treat structural, thermodynamic and kinetic
+ aspects at once . The absence of specific insights on these three
+ complementary pieces of the puzzle makes it impossible to optimize simulation methods, and results in general in
+ the inability to obtain converged simulations on biologically relevant time-scales.
+
The hardness of structural modeling owes to three intertwined reasons.
+
First, PELs of biomolecules usually exhibit a number of critical points exponential in the dimension ; fortunately, they enjoy a multi-scale structure . Intuitively, the significant local minima/basins are those
+ which are deep or isolated/wide, two notions which are mathematically
+ qualified by the concepts of persistence and prominence. Mathematically, problems are plagued with the curse of
+ dimensionality and measure concentration phenomena. Second, biomolecular processes are inherently multi-scale,
+ with motions spanning
+
+ 15 and
+
+ 4 orders of magnitude in time and amplitude respectively . Developing methods able to exploit this multi-scale
+ structure has remained elusive. Third, macroscopic properties of biomolecules i.e.
+ observables, are average properties computed over ensembles of conformations, which calls for a multi-scale
+ statistical treatment both of thermodynamics and kinetics.
+
+
+ Validating models.
+
A natural and critical question naturally concerns the validation of models proposed in structural
+ bioinformatics. For all three types of questions of interest (structures, thermodynamics, kinetics), there exist
+ experiments to which the models must be confronted – when the experiments can be conducted.
+
For structures, the models proposed can readily be compared against experimental results stemming from X ray
+ crystallography, NMR, or cryo electron microscopy. For thermodynamics, which we illustrate here with binding
+ affinities, predictions can be compared against measurements provided by calorimetry or surface plasmon
+ resonance. Lastly, kinetic predictions can also be assessed by various experiments such as binding affinity
+ measurements (for the prediction of
+
+ and
+
+ ), or fluorescence
+ based methods (for kinetics of folding).
+
+
+
+ Research program
+
Our research program ambition to develop a comprehensive set of novel concepts and algorithms to study protein
+ dynamics, based on the modular framework of PEL.
As noticed while discussing Protein dynamics: core CS - maths challenges, the integrated
+ nature of simulation methods such as MD or MC is such that these methods do not in general give access to
+ biologically relevant time scales. The framework of energy landscapes , (Fig. ) is much more
+ modular, yet, large biomolecular systems remain out of reach.
+
To make a definitive step towards solving the prediction of protein dynamics, we will serialize the discovery
+ and the exploitation of a PEL , , . Ideas and
+ concepts from computational geometry/geometric motion planning, machine learning, probabilistic algorithms, and
+ numerical probability will be used to develop two classes of probabilistic algorithms. The first deals with
+ algorithms to discover/sketch PELs i.e. enumerate all significant (persistent or prominent)
+ local minima and their connections across saddles, a difficult task since the number of all local
+ minima/critical points is generally exponential in the dimension. To this end, we will develop a hierarchical
+ data structure coding PELs as well as multi-scale proposals to explore molecular conformations. (Nb: in Monte
+ Carlo methods, a proposal generates a new conformation from an existing one.) The second focuses on methods to
+ exploit/sample PELs i.e. compute so-called densities of states, from which all thermodynamic
+ quantities are given by standard relations
+ . This is a hard problem akin to high-dimensional
+ numerical integration. To solve this problem, we will develop a learning based strategy for the Wang-Landau
+ algorithm –an adaptive Monte Carlo Markov Chain (MCMC) algorithm, as
+ well as a generalization of multi-phase Monte Carlo methods for convex/polytope volume calculations , , for
+ non convex strata of PELs.
As discussed in the previous Section, the study of PEL and protein dynamics raises difficult algorithmic /
+ mathematical questions. As an illustration, one may consider our recent work on the comparison of high
+ dimensional distribution , statistical tests /
+ two-sample tests , ,
+ the comparison of clustering , the complexity study of
+ graph inference problems for low-resolution reconstruction of assemblies , the analysis of partition (or clustering) stability
+ in large networks, the complexity of the representation of simplicial complexes . Making progress on such questions is
+ fundamental to advance the state-of-the art on protein dynamics.
+
We will continue to work on such questions, motivated by CSB / theoretical biophysics, both in the continuous
+ (geometric) and discrete settings. The developments will be based on a combination of ideas and concepts from
+ computational geometry, machine learning (notably on non linear dimensionality reduction, the reconstruction of
+ cell complexes, and sampling methods), graph algorithms, probabilistic algorithms, optimization, numerical
+ probability, and also biophysics.
While our main ambition is to advance the algorithmic foundations of molecular simulation, a major challenge
+ will be to ensure that the theoretical and algorithmic developments will change the fate of applications, as
+ illustrated by our case studies. To foster such a symbiotic relationship between theory, algorithms and
+ simulation, we will pursue high quality software development and integration within the SBL,
+ and will also take the appropriate measures for the software to be widely adopted.
+
+ Software in structural bioinformatics.
+
Software development for structural bioinformatics is especially challenging, combining advanced geometric,
+ numerical and combinatorial algorithms, with complex biophysical models for PEL and related
+ thermodynamic/kinetic properties. Specific features of the proteins studied must also be accommodated. About
+ 50 years after the development of force fields and simulation methods (see the 2013 Nobel prize in chemistry),
+ the software implementing such methods has a profound impact on molecular science at large. One can indeed
+ cite packages such as CHARMM, AMBER, gromacs, gmin, MODELLER, Rosetta, VMD, PyMol, .... On the other hand, these packages are goal oriented, each tackling a (small set
+ of) specific goal(s). In fact, no real modular software design and integration has taken place. As a result,
+ despite the high quality software packages available, inter-operability between algorithmic building blocks
+ has remained very limited.
+
+
+ The SBL.
+
Predicting the dynamics of large molecular systems requires the integration of advanced algorithmic building
+ blocks / complex software components. To achieve a sufficient level of integration, we undertook the
+ development of the Structural Bioinformatics Library (SBL, ) , a generic C++/python
+ cross-platform library providing software to solve complex problems in structural bioinformatics. For
+ end-users, the SBL provides ready to use, state-of-the-art applications to model
+ macro-molecules and their complexes at various resolutions, and also to store results in perennial and easy to
+ use data formats (). For developers, the SBL provides a
+ broad C++/python toolbox with modular design (). This hybrid status targeting both
+ end-users and developers stems from an advanced software design involving four software components, namely
+ applications, core algorithms, biophysical models, and modules (). This modular design makes it possible to optimize
+ robustness and the performance of individual components, which can then be assembled within a goal oriented
+ application.
+
+
+
+ Applications: modeling interfaces, contacts, and interactions
+
+
+ Protein interactions
+
+
+ protein complexes
+
+
+ structure/thermodynamics/kinetics prediction.
+
+
+
Our methods will be validated on various systems for which flexibility operates at various scales. Example such
+ systems are antibody-antigen complexes, (viral) polymerases, (membrane) transporters.
+
Even very complex biomolecular systems are deterministic in prescribed conditions (temperature, pH, etc),
+ demonstrating that despite their high dimensionality, all d.o.f. are not at play at the same
+ time. This insight suggests three classes of systems of particular interest. The first class consists of systems
+ defined from (essentially) rigid blocks whose relative positions change thanks to conformational changes of
+ linkers; a Newton cradle provides an interesting way to envision such as system. We have recently worked on one
+ such system, a membrane proteins involve in antibiotic resistance (AcrB, see . The second class consists of cases where relative
+ positions of subdomains do not significantly change, yet, their intrinsic dynamics are significantly altered. A
+ classical illustration is provided by antibodies, whose binding affinity owes to dynamics localized in six
+ specific loops , .
+ The third class, consisting of composite cases, will greatly benefit from insights on the first two classes. As
+ an example, we may consider the spikes of the SARS-CoV-2 virus, whose function (performing infection) involves
+ both large amplitude conformational changes and subtle dynamics of the so-called receptor binding domain. We
+ have started to investigate this system, in collaboration with B. Delmas (INRAe) .
+
In ABS, we will investigate systems in these three tiers, in collaboration with expert collaborators, to
+ hopefully open new perspectives in biology and medicine. Along the way, we will also collaborate on selected
+ questions at the interface between CSB and systems biology, as it is now clear that the structural level and the
+ systems level (pathways of interacting molecules) can benefit from one another.
+
+
+
+ Application domains
+
The main application domain is Computational Structural Biology, as underlined in the Research
+ Program.
+
+
+ Highlights of the year
+
In October 2021, Edoardo Sarti has joined ABS as Chargé de Recherche de Classe Normale. His
+ expertise comprises a diverse set of interests spanning from algorithmic questions about geometrical, functional
+ and evolutionary aspects of biomolecules (latest study: ), to
+ the collection and analysis of large collections of molecular structural data. From the very start, E. Sarti has
+ started taking part in several research and technical projects of ABS.
+
+
+ New software and platforms
+
See report on the Structural Bioinformatics Library.
+ Boosting the analysis of protein interfaces with Multiple Interface String Alignments: illustration
+ on the spikes of coronaviruses
+
+
+
+
+ F.
+ Cazals
+
+
+
+
+ In collaboration with S. Bereux and B. Delmas (INRAe, Jouy-en-Josas).
+
In this work , we introduce Multiple Interface
+ String Alignment (MISA), a visualization tool to display coherently various sequence and structure
+ based statistics at protein-protein interfaces (SSE elements, buried surface area,
+
+ , B factor values, etc). The
+ amino-acids supporting these annotations are obtained from Voronoi interface models. The benefit of MISA is to
+ collate annotated sequences of (homologous) chains found in different biological contexts i.e. bound with
+ different partners or unbound. The aggregated views MISA/SSE, MISA/BSA, MISA/
+
+ etc make it trivial to identify
+ commonalities and differences between chains, to infer key interface residues, and to understand where
+ conformational changes occur upon binding. As such, they should prove of key relevance for knowledge based
+ annotations of protein databases such as the Protein Data Bank.
+
Illustrations are provided on the receptor binding domain (RBD) of coronaviruses, in complex with their
+ cognate partner or (neutralizing) antibodies. MISA computed with a minimal number of structures complement and
+ enrich findings previously reported.
+
The corresponding package is available from the Structural Bioinformatics Library (
+
+
and ).
+
+
+ SARS-CoV-2 Through the Lens of Computational Biology: How bioinformatics is playing a key role in the
+ study of the virus and its origins
+
+
+
+
+ F.
+ Cazals
+
+
+
+
+ In collaboration with Samuel Alizon (MIVEGEC - Maladies infectieuses et vecteurs : écologie,
+ génétique, évolution et contrôle), Stéphane Guindon (MAB - Méthodes et Algorithmes pour la Bioinformatique,
+ LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier), Claire Lemaitre
+ (GenScale - Scalable, Optimized and Parallel Algorithms for Genomics, Inria Rennes – Bretagne Atlantique),
+ Tristan Mary-Huard (INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et
+ l’Environnement), Anna Niarakis (Lifeware - Computational systems biology and optimization, Inria Saclay - Ile
+ de France; GenHotel - Laboratoire de recherche européen pour la polyarthrite rhumatoïde), Mikaël Salson
+ (CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189), Celine Scornavacca
+ (UMR ISEM - Institut des Sciences de l'Evolution de Montpellier), Hélène Touzet (CRIStAL - Centre de Recherche
+ en Informatique, Signal et Automatique de Lille - UMR 9189).
+
On December 2019, the Chinese Center for Disease Control reported several cases of severe pneumonia that
+ resists usual treatments in the city of Wuhan. This was the beginning of the COVID-19 pandemic which caused
+ more than 80 millions infection cases and 1.7 millions deaths during the year 2020 alone1 . This major
+ outbreak has given rise to global public health responses as well as an international research effort of
+ unprecedented scope and speed. This scientific mobilization has led to remarkable results, which have enabled
+ a great deal of knowledge to be accumulated in just a few months on this novel pathogen: identification of the
+ virus, of its main proteins, analysis of its origin and its functionning. This basic biological knowledge is
+ mandatory to medical advances: design tests, find a vaccine or a cure.
+
In this document , one year after the beginning of the worldwide
+ spread of the disease, we wish to shed particular light on the contribution of bioinformatics in all this
+ work. Bioinformatics is a discipline at crossroads of computer sciences, mathematics and biology that has
+ taken on an inestimable importance in modern biology and medicine. It provides computational models,
+ algorithms and software to the scientific community, that are both operational and effective. The discovery
+ and study of the SARS-Cov-2 coronavirus is an emblematic example. The utilization of bioinformatics methods
+ has been at the heart of essential milestones : from the sequencing of the virus genome and its annotation to
+ the history of its origin, the modelisation of interacting biological entities both at the molecular scale and
+ at the network scale, and the study of the host genetic susceptibility. All these studies, as a whole, have
+ made it possible to elucidate the nature and the functionning of the novel pathogen and have greatly
+ contributed to the fight against COVID-19.
+
+
+ Gene prioritization based on random walks with restarts and absorbing states, to define gene sets
+ regulating drug pharmacodynamics from single-cell analyses
+
+
+
+
+ F.
+ Cazals
+
+
+
+
+ D.
+ Mazauric
+
+
+
+
+ A.
+ Sales de Queiroz
+
+
+
+
+ G.
+ Sales Santa Cruz
+
+
+
+
+ In collaboration with Alain Jean-Marie (Inria Neo) and Jérémie Roux (Inserm and CNRS and UCA).
+
Prioritizing genes for their role in drug sensitivity, is an important step in understanding drugs mechanisms
+ of action and discovering new molecular targets for co-treatment. In this work , we formalize this problem by considering
+ two sets of genes
+
+ and
+
+ respectively composing the predictive gene signature of sensitivity to a
+ drug and the genes involved in its mechanism of action, as well as a protein interaction network (PPIN)
+ containing the products of
+
+ and
+
+ as nodes. We introduce Genetrank, a method to prioritize
+ the genes in
+
+ for their likelihood to regulate the genes in
+
+ .
+
+ Genetrank uses asymmetric random walks with restarts, absorbing states, and a suitable
+ renormalization scheme. Using novel so-called saturation indices, we show that the conjunction of absorbing
+ states and renormalization yields an exploration of the PPIN which is much more progressive than that afforded
+ by random walks with restarts only. Using MINT as underlying network, we apply Genetrank to
+ a predictive gene signature of cancer cells sensitivity to tumor-necrosis-factor-related apoptosis-inducing
+ ligand (TRAIL), performed in single-cells. Our ranking provides biological insights on drug sensitivity and a
+ gene set considerably enriched in genes regulating TRAIL pharmacodynamics when compared to the most
+ significant differentially expressed genes obtained from a statistical analysis framework alone. We also
+ introduce gene expression radars, a visualization tool to assess all pairwise interactions
+ at a glance.
+
+ Genetrank is made available in the Structural Bioinformatics Library (). It should prove useful for mining gene sets in
+ conjunction with a signaling pathway, whenever other approaches yield relatively large sets of genes.
+
+
+
+ Modeling the dynamics of proteins
+
+
+ protein
+
+
+ flexibility
+
+
+ collective coordinate
+
+
+ conformational sampling dimensionality reduction.
+
+
+
+ Tripeptide loop closure: a detailed study of reconstructions based on Ramachandran
+ distributions
+
+
+
+
+ F.
+ Cazals
+
+
+
+
+ T.
+ O'Donnell
+
+
+
+
+ In collaboration with C. Robert (IBPC / CNRS, Paris, France).
+
Tripeptide loop closure (TLC) is a standard procedure to reconstruct protein backbone conformations, by
+ solving a polynomial system in a single variable yielding up to 16 real solutions.
+
In this work , we first show that multiprecision is required in
+ a TLC solver to guarantee the existence and the accuracy of solutions. We then compare solutions yielded by
+ the TLC solver against tripeptides from the Protein Data Bank. We show that these solutions are geometrically
+ diverse (up to
+
+ RMSD with respect to the data), and sound in terms
+ of potential energy. Finally, we compare Ramachandran distributions of data and reconstructions for the three
+ amino acids. The distribution of reconstructions in the second angular space
+
+
+ stands out, with a rather uniform distribution leaving a central void.
+
We anticipate that these insights, coupled to our robust implementation in the Structural Bioinformatics
+ Library (), will help understanding the
+ properties of TLC reconstructions, with potential applications to the generation of conformations of flexible
+ loops in particular.
+ Frechet mean and p-mean on the unit circle: characterization, decidability, and algorithm
+
+
+
+
+ F.
+ Cazals
+
+
+
+
+ T.
+ O'Donnell
+
+
+
+
+ In collaboration with B. Delmas (INRAe, Jouy-en-Josas).
+
The center of mass of a point set lying on a manifold generalizes the celebrated Euclidean centroid, and is
+ ubiquitous in statistical analysis in non Euclidean spaces.
+
In this work , we give a complete characterization of the weighted
+
+
+ -mean of
+ a finite set of angular values on
+
+ , based on a decomposition of
+
+ such that the functional of interest has at most one
+ local minimum per cell. This characterization is used to show that the problem is decidable for rational
+ angular values –a consequence of Lindemann's theorem on the transcendence of
+
+ , and to develop an effective
+ algorithm parameterized by exact predicates. A robust implementation of this algorithm based on
+ multi-precision interval arithmetic is also presented, and is shown to be effective for large values of
+
+
+ and
+
+
+ . We use
+ it as building block to implement the k-means and k-means++ clustering algorithms on the flat torus, with
+ applications to clustering protein molecular conformations. These algorithms are available in the Structural
+ Bioinformatics Library ().
+
Our derivations are of interest in two respects. First, efficient
+
+ -mean calculations are relevant to
+ develop principal components analysis on the flat torus encoding angular spaces–a particularly important case
+ to describe molecular conformations. Second, our two-stage strategy stresses the interest of combinatorial
+ methods for p-means, also emphasizing the role of numerical issues.
+
+
+ Improved polytope volume calculations based on Hamiltonian Monte Carlo with boundary reflections and
+ sweet arithmetics
+
+
+
+
+ F.
+ Cazals
+
+
+
+
+ A.
+ Chevallier
+
+
+
+
+ In collaboration with S. Pion IMS (Univ. Bordeaux / Bordeaux INP / CNRS UMR 5218).
+
Computing the volume of a high dimensional polytope is a fundamental problem in geometry, also connected to
+ the calculation of densities of states in statistical physics, and a central building block of such algorithms
+ is the method used to sample a target probability distribution.
+
This paper studies Hamiltonian Monte Carlo (HMC) with
+ reflections on the boundary of a domain, providing an enhanced alternative to Hit-and-run (HAR) to sample a
+ target distribution restricted to the polytope. We make three contributions. First, we provide a convergence
+ bound, paving the way to more precise mixing time analysis. Second, we present a robust implementation based
+ on multi-precision arithmetic, a mandatory ingredient to guarantee exact predicates and robust constructions.
+ We however allow controlled failures to happen, introducing the Sweeten Exact Geometric
+ Computing (SEGC) paradigm. Third, we use our HMC random walk to perform H-polytope volume calculations,
+ using it as an alternative to HAR within the volume algorithm by Cousins and Vempala. The systematic tests
+ conducted up to dimension
+
+ on the cube, the isotropic and the
+ standard simplex show that HMC significantly outperforms HAR both in terms of accuracy and running time.
+ Additional tests show that calculations may be handled up to dimension
+
+ .
+ These tests also establish that multiprecision is mandatory to avoid exits from the polytope.
+
+
+ Overlaying a hypergraph with a graph with bounded maximum degree, with application for low-resoluton
+ reconstructions of molecular assemblies
+
+
+
+
+ D.
+ Mazauric
+
+
+
+
+ In collaboration with F. Havet, T. V. H. Nguyen laboratoire I3S (CNRS, Université Côte d'Azur).
+
We analyze a generalization of the minimum connectivity inference problem (MCI) that models the computation
+ of low-resolution structures of macro-molecular assemblies, based on data obtained by native mass
+ spectrometry. The generalization studied in this work, allows us to consider more refined constraints for the
+ characterization of low resolution models of large assemblies, such as degree constraints (e.g. a protein has
+ a limited number of other proteins in contact).
+
More precisely, let
+
+ and
+
+ be respectively a graph and a hypergraph defined on a same set of vertices,
+ and let
+
+ be
+ a graph. We say that
+
+
+
+
+
+ -overlays a hyperedge
+
+
+ of
+
+
+ if the
+ subgraph of
+
+ induced by
+
+ contains
+
+ as a spanning subgraph, and
+ that
+
+
+
+
+
+ -overlays
+
+
+ if it
+
+ -overlays every hyperedge of
+
+
+ . For a
+ fixed graph
+
+ and a fixed integer
+
+ , the problem
+
+
+ -
+
+ -Overlay
+ consists in deciding whether there exists a graph with maximum degree at most
+
+ that
+
+ -overlays a given hypergraph
+
+
+ . In , we prove that for any graph
+
+ which is neither complete
+ nor anticomplete, there exists an integer
+
+ such that
+
+
+ -
+
+ -Overlay is
+
+
+ -complete for all
+
+ .
+
+
+ Conflict coloring problems: complexity and application to high resolution biological assembly
+ modeling
+
+
+
+
+ F.
+ Cazals
+
+
+
+
+ D.
+ Mazauric
+
+
+
+
+ In collaboration with F. Havet, T. V. H. Nguyen laboratoire I3S (CNRS, Université Côte d'Azur).
+
Given a graph
+
+ ,
+ a color set
+
+ for each vertex
+
+ , a bipartite graph between
+ color sets
+
+ and
+
+ for every edge
+
+ , Conflict
+ Coloring consists in deciding whether exists a conflict coloring, that is a coloring in which
+
+
+ is not an edge of the bipartite graph. Conflict Coloring is motivated by computational
+ structural biology problems, high resolution determination of molecular assemblies. The graph represents the
+ subunits and the interaction between them, the colors are the given conformations, and the edges of the
+ bipartite graphs are the incompatible conformations of two subunits.
+
In this work, we first establish the complexity dichotomies (polynomial vs
+
+ -complete)
+ for Conflict Coloring and its variants. We provide some experiments in which we build
+ instances of Conflict Coloring associated to Voronoi diagram in the
+ plane, and we then analyse the existences of a solution related to parameters used in our experimental
+ setup.
+
+
+
+
+ Partnerships and cooperations
+
+
+
+
+ F.
+ Cazals
+
+
+
+
+ D.
+ Mazauric
+
+
+
+
+
+ International research visitors
+
+ Visits of international scientists
+
+ Inria International Chair
+
+ David Wales, Cambridge University, is endowed chair within 3IA Côte d'Azur / ABS.
+
+
+
+
+
+
+ Dissemination
+
+ Promoting scientific activities
+
+ Scientific events: organisation
+
+
+
+ Frédéric Cazals was involved in the organization of:
+
+ Symposium Multidisciplinary approaches in cancer research, Organized at Inria Sophia
+ Antipolis Méditerranée. Web: .
+ Winter School Machine Learning Methods to Analyze and Predict Protein Structure, Dynamics
+ and Function, CIRM, Luminy, November 7-12, 2021. Web: .
+
+ Critical evaluation of methods for scoring interfaces of protein complexes, Online Elixir
+ 3D-Bioinfo meeting, organized by Emmanuel Levy (Elixir IL), Frederic Cazals (Elixir FR), Shoshana Wodak
+ (Elixir BE).
+
+
+
+ Scientific events: selection
+
+ Member of the conference program committees
+
Frédéric Cazals participated to the following program committees:
+
+ Symposium on Solid and Physical Modeling
+ Intelligent Systems for Molecular Biology (ISMB) / European Conference on Computational Biology
+ (ECCB)
+
+
+
+
+ Invited talks
+
+
+
+ Frédéric Cazals gave the following invited talks:
+
+
+ Mining protein flexibility: a new class of move sets; GDR BIM/GT MASIM, November 2021;
+ UCA, 5th Academy 4 Research Webinar - Mental Retardation and Protein Dynamics, October 2021.
+
+
+
+ Leadership within the scientific community
+
+
+
+ Frédéric Cazals
+
+ 2010-...: Member of the steering committee of the GDR Bioinformatique Moléculaire, for the Structure and
+ macro-molecular interactions theme.
+ 2017-...: Co-chair, with Yann Ponty, of the working group / groupe de travail (GT MASIM - Méthodes
+ Algorithmiques pour les Structures et Interactions Macromoléculaires), within the GDR de BIoinfor- matique
+ Moléculaire (GDR BIM, ).
+
+
+
+ Research administration
+
+
+
+ Frédéric Cazals
+
+ 2018-...: Member of the bureau du comité des équipes projets.
+ 2020-...: Member of the bureau of the EUR Life, Université Côte d’Azur.
+
+
+
+
+ Dorian Mazauric
+
+ 2019-...: Member of the comité Plateformes.
+
+
+
+
+ Teaching - Supervision - Juries
+
+ Teaching
+
+ 2014–...: Master Data Sciences Program (M2), Department of Applied Mathematics, Ecole Centrale-Supélec;
+ Foundations of Geometric Methods in Data Analysis; F. Cazals and M. Carrière, Inria
+ Sophia / (ABS, DataShape). Web: .
+ 2021–...: Master Data Sciences & Artificial Intelligence (M1), Université Côte d’Azur; Introduction to machine learning (course practicals); E. Sarti.
+ 2021–...: Master Data Sciences & Artificial Intelligence (M2), Université Côte d’Azur; Geometric and topological methods in machine learning; F. Cazals, J-D. Boissonnat and M. Carrière,
+ Inria Sophia / (ABS, DataShape, DataShape); Web: .
+ 2021–...: Master Cancérologie et Recherche Translationnelle (M2), Université Côte d’Azur; Binding affinity maturation and protein interaction network analysis: two examples of bioinformatics
+ applications in medicine; F. Cazals.
+ 2020–...: Master Sciences du Vivant (M2), parcours Biologie, Informatique, Mathématiques, Université Côte
+ d’Azur; Introduction to statistical physics of biomolecules; F. Cazals.
+ 2018–...: Master : Algorithmique et Complexité, 23h30 TD, niveau M1, Polytech Nice Sophia, Université Côte
+ d'Azur, filière Sciences Informatiques, France; Dorian Mazauric.
+
+
+
+ Supervision
+
PhD thesis:
+
+
+ PhD in progress, 3rd year: Timothée O'Donnel, Modeling the influenza
+ polymerase. Université Côte d'Azur. Thesis co-supervised by Frédéric Cazals and Bernard Delmas, INRA
+ Jouy-en-Josas.
+
+ Defended PhD: Thi Viet Ha Nguyen, Graph Algorithms techniques for (low
+ and high) resolution models of large protein assemblies. Université Côte d'Azur. Thesis co-supervised
+ by Frédéric Havet, Laboratoire I3S (CNRS, Université Côte d'Azur).
+
+
Interns:
+
+ Aarushi Gupta, intern from IIT Delhi, summer 2021. Modeling protein backbone flexibility
+ using solutions of the tripeptide loop closure.
+
+ Louis Goldenberg, intern from Ecole Polytechnique, summer 2021. Parametric models for
+ compact clusters.
+
+ Sebastián Gallardo Diaz, Universidad Técnica Federico Santa Marı́a, Valparaı́so, Chile. Advisors: Pierre
+ Kornprobst (Inria project-team Biovision), Dorian Mazauric. Algorithms for a new packing
+ problem : Towards Reading Accessible Newspapers.
+ Vivian Losciale, Université Côte d'Azur. Advisors: Jérémy Camponovo, Frédéric Havet, Buntheng Ly, Dorian
+ Mazauric, Maxime Sermesant.Jeux-vidéos de médiation : Intelligence artificielle pour
+ l’imagerie médicale.
+ Quentin Larose, Université Côte d'Azur. Advisors: Agnès Bessière, Carole Clastres, Jérémy Camponovo, Luc
+ Hogie, Dorian Mazauric, Eric Pascual, Sandrine Selosse, Brigitte Trousse. Portail des
+ ressources Terra Numerica.
+
+
+
+ Juries
+
Frédéric Cazals participated to the following committees:
+
+ Luke Dicks, Cambridge University, April 2021. Rapporteur for the PhD thesis K-means
+ landscapes: exploring clustering solution spaces using energy landscape theory. Advisor: David
+ Wales.
+ Manon Ruffini, Univ. of Toulouse, March 2021. Rapporteur on the PhD thesis Models and
+ Algorithms for Computational Protein Design. Advisor: Thomas Schiex.
+ Dorian Mazauric, Habilitation thesis, Université Côte d'Azur, November 2021. Committee member (president)
+ for the habilitation Algorithmique des graphes pour les réseaux et la biologie structurale
+ computationnelle.
+
+
Dorian Mazauric participated to the following committees:
+
+ Thi Viet Ha Nguyen, Université Côte d'Azur, December 2021. Committee member for the PhD thesis Graph Algorithms techniques for (low and high) resolution models of large protein
+ assemblies. Advisors: Frédéric Havet, Dorian Mazauric.
+
+
+
+
+ Popularization
+
+ Internal or external Inria responsibilities
+
Dorian Mazauric:
+
+ 2019–...: Head of Commission (Médiation et Animation des
+ MAthématiques, des Sciences et Techniques Informatiques et des Communications), Inria Sophia Antipolis -
+ Méditerranée.
+ 2019–...: Coordinator of , an ambitious scientific
+ popularisation project. Its main goal is to create a "Dedicated Digital space" in the south of France, (in
+ the spirit of the "Cité des Sciences" or "Palais de la découverte" in Paris). To do so, Terra Numerica is
+ developing and structuring popularisation activities, supports which are spread in different antennas
+ throughout the territory (e.g. Espace Terra Numerica - Valbonne Sophia Antipolis, MIA, in schools,
+ exhibition extensions...). This large-scale project involves (brings together) all the actors of research,
+ education, industry, associations and collectivities... It is actually composed of more than one hundred
+ people.
+ 2018–...: Member of the Conseil d'Administration de l'association les Petits Débrouillards.
+ 2017–...: Member of projet de médiation Galéjade : Graphes et ALgorithmes : Ensemble de Jeux À Destination
+ des Ecoliers... (mais pas que).
+
+
+
+ Articles and contents
+
Frédéric Cazals:
+
+ Podcast Investiga’Sciences
+ Vive la protéine: interview-discussion of
+ Thomas Schiex and myself by Valérie Ravinet, October 2021. .
+
+
Dorian Mazauric:
+
+ Participation to the development of .
+ Participation to the development of popularization videos games .
+
+
+
+ Interventions
+
Dorian Mazauric - Fête de la Science 2021:
+
+ Village des Sciences de Villeneuve-Loubet Avec Pobot. Samedi 02 octobre 2021 et dimanche 03 octobre 2021.
+ With Thomas Dissaux, Adrien Gausseran, Nicolas Nisse, Eric Pascual, Lucas Picassari-Arrieta, Brigitte
+ Trousse.
+ Village des sciences de la vallée de la Vésubie avec Les Apprentis Pas Sages Samedi 02 octobre 2021. Puzzle du nid d’abeilles – Graphes et algorithmes grandeur nature. With Samantha
+ Lanney-Ricci, Magali Martin-Mazauric.
+ Interventions au Campus International de Valbonne Lundi 04 octobre 2021. La magie du binaire
+ – Pas besoin de réfléchir, les ordinateurs calculent tellement vite ? Problèmes actuels en
+ algorithmique. With Estelle Zavoli.
+ Atelier scientifique à l’Espace d’Art Concret (EAC), Mouans-Sartoux Organisé par l’EAC (Amandine Briand,
+ Sabrina Lah, Martin Merle, Claire Spada, Brigitte Segatori, Roubaud). Du lundi 04 octobre 2021 au vendredi
+ 08 octobre 2021. Des reines sur une oeuvre d’art (Tenth Copper Corner une oeuvre minimaliste
+ de Carl André formée de 55 carreaux) : mathématiques et algorithmique. With Frédéric Havet, Nicolas
+ Nisse, Martine Olivi. En collaboration avec Geoffroy Aubry et Valérie Doya (atelier de Physique).
+ Intervention au collège La chênaie de Mouans-Sartoux Mercredi 06 octobre 2021. La magie des
+ graphes et du binaire – Pas besoin de réfléchir, les ordinateurs calculent tellement vite ? Problèmes
+ actuels en algorithmique. With Mylène Raibaudi, Brigitte Trousse.
+ Village des Sciences de Mouans-Sartoux Samedi 09 octobre 2021. Sabrina Barnabé, Martine Olivi, Brigitte
+ Trousse, Thierry Viéville.
+ Festival des sciences de Nice d’Université Côte d’Azur Samedi 09 octobre 2021 et dimanche 10 octobre 2021.
+ With Alexandre Bonlarron, Foivos Fioravantes, Victor Jung, Hicham Lesfari, Steve Malalel, Magali
+ Martin-Mazauric, Romain Michelucci, Nicolas Nisse, Marie Pelleau, Nina Singlan, Rudan Xiao.
+ Interventions au collège de Roquebillière Jeudi 07 octobre 2021. La magie des graphes et du
+ binaire – Jeux combinatoires – Pas besoin de réfléchir, les ordinateurs calculent tellement vite ?
+ Problèmes actuels en algorithmique – Ateliers Jeux Graphes et Algorithmes. With Samantha
+ Lanney-Ricci.
+ Conférence à la médiathèque de Biot Vendredi 08 octobre 2021.
+ Village des Sciences et de l’Innovation de la CASA à Antibes Juan-les-Pins Avec PoBot, SLV, @b4games.
+ Samedi 16 octobre 2021 et dimanche 17 octobre 2021. With Agnès Bessière, Armel Berceliot, Étienne Chaplain,
+ Thomas Dissaux, Thierry Lespinasse, Stéphane Mansour, Magali Martin-Mazauric, Nicolas Nisse, Eric Pascual,
+ Lucas Picassari-Arrieta, Frédéric Rallo, Sandrine Selosse, Brigitte Trousse.
+
+
Dorian Mazauric - Interventions at Maison de l'Intelligence Artificielle:
+
+ Ateliers Terra Numerica avec les étudiants du Master SmartEdTech. Mercredi 14 avril 2021. Journée
+ intensive de formation hybride et animation et co-création d’ateliers. With Saint-Clair Lefevre, Frédéric
+ Havet, Margarida Romero, Thierry Viéville.
+
+
Dorian Mazauric - Cordées de la réussite (coordonné par Université Côte d'Azur):
+
+ Deux classes du collège Henri Nans, Aups. Les sciences du numérique à portée de mains ! Découvrir,
+ Explorer, Expérimenter ! Pirates et trésor : des maths et des algorithmes à la programmation
+ Scratch et mBot. With Frédéric Havet, Eric Pascual, Brigitte Trousse.
+
+
Dorian Mazauric - Programme Chiche:
+
+ Intervention au lycée Apollinaire, Nice Jeudi 14 octobre 2021.
+ Intervention au lycée Estienne d’Orves, Nice Jeudi 21 octobre 2021.
+ Intervention au CIV, Valbonne Sophia Antipolis Jeudi 02 décembre 2021.
+
+
Dorian Mazauric - Formations:
+
+ Formation d’enseignants co-organisée par la DANE et Terra Numerica avec les ateliers Terra Numerica à la
+ Maison de l’intelligence Artificielle. Mardi 9 mars 2021, mardi 23 mars 2021, mardi 6 avril 2021, mardi 20
+ avril 2021, mardi 25 mai 2021. Machine d’apprentissage par renforcement pour gagner aux jeux,
+ Initiation à la reconnaissance d’images avec des drones, ateliers d’informatique débranchée. With
+ Jérémy Camponovo, Frédéric Havet, Eric Pascual, Brigitte Trousse.
+ Formation de personnels de médiathèques de la CASA. Vendredi 25 juin 2021, jeudi 23 septembre 2021.
+ Formation sur les fondements de l’informatique : Transmission de pensée – La magie du
+ binaire.
+ Présentation et formation au Fab’Ecole 06 de la DRANE, collège Bertone d’Antibes. Vendredi 26 novembre
+ 2021. Présentation et formation sur des ateliers Terra Numerica. With Brigitte Trousse.
+
+
Dorian Mazauric - In schools:
+
+ Collège Bechet d’Antibes Juan-les-Pins Lundi 8 mars 2021. Dans le cadre du projet pédagogique Ethique des
+ données et de l’information (1/3). Introductions aux algorithmes. With Sylvain Etienne, Frédéric Giroire,
+ Géraldine Rouard, Brigitte Trousse.
+ Centre International de Valbonne Sophia Antipolis Lundi 15 mars 2021. Dans le cadre de séances autour de
+ l’Intelligence Artificielle avec une classe de terminale du CIV organisées par Les
+ Petits Débrouillards. Intelligence Artificielle et reconnaissance d’images. With Marie Barbieux, Marine
+ Beaudet, Soledad Tolosa.
+ Collège Bechet d’Antibes Juan-les-Pins Vendredi 26 mars 2021 et 9 avril 2021. Dans le cadre du projet
+ pédagogique Ethique des données et de l’information (2/3). Modélisation d’un réseau social et
+ de contenus, et algorithmes de recommandation. With Sylvain Etienne, Frédéric Giroire, Géraldine
+ Rouard, Brigitte Trousse.
+ Collège Bechet d’Antibes Juan-les-Pins Lundi 7 juin 2021. Dans le cadre du projet pédagogique Ethique des
+ données et de l’information (3/3). Conférence Protection des données et métier de Déléguée à
+ la Protection des Données d’Inria (Anne Combe). With Anne Combe, Sylvain Etienne, Frédéric Giroire,
+ Géraldine Rouard, Brigitte Trousse.
+ Roquefort-les-Pins Dans le cadre des activités du centre aéré de la commune. Lundi 26 juillet 2021 et
+ mardi 27 juillet 2021. Trois demi-journées : ateliers d’informatique débranchée (pour les 3 à
+ 6 ans), ateliers pour découvrir les algorithmes de recommandation dans les réseaux sociaux (pour les
+ adolescents) et tours de magie pour découvrir comment l’ordinateur compte (pour les 6 à 10 ans). With
+ Frédéric Havet.
+ Lycée Internationale de Valbonne Jeudi 02 décembre 2021. Ateliers algorithmiques grandeur
+ nature. With Bérengère Abric, Perrine Le Dûs.
+
+
Dorian Mazauric - Internships:
+
+ Treize stagiaires de troisième au centre Inria d’Université Côte d’Azur Du lundi 13 décembre au vendredi
+ 17 décembre 2021.
+
+
+
+
+
+
+
+ {@titre}
+
+
+ Distributed Link Scheduling in Wireless Networks
+
+
+ V.
+ Vishal
+ Misra
+
+
+ P.
+ Philippe
+ Nain
+
+
+ hal-01977266
+ 10.1142/S1793830920500585%EF%BB%BF
+
+
+ Discrete Mathematics, Algorithms and Applications
+
+ 2020
+ 12
+ 5
+ 1-38
+
+
+
+
+
+ On the complexity of the representation of simplicial complexes by trees
+
+
+ J.-D.
+ Jean-Daniel
+ Boissonnat
+
+
+ hal-01259806
+ 10.1016/j.tcs.2015.12.034
+
+
+ Theoretical Computer Science
+
+ February 2016
+ 617
+ 17
+
+
+
+
+
+ Energy landscapes and persistent minima
+
+
+ J.
+ J.
+ Carr
+
+
+ D.
+ D.
+ Mazauric
+
+
+ F.
+ F.
+ Cazals
+
+
+ D. J.
+ D. J.
+ Wales
+
+
+ 10.1063/1.4941052
+
+
+ The Journal of Chemical Physics
+
+ 2016
+ 144
+ 5
+ 4
+
+
+
+
+
+ Conformational Ensembles and Sampled Energy Landscapes: Analysis and Comparison
+
+
+ F.
+ F.
+ Cazals
+
+
+ T.
+ T.
+ Dreyfus
+
+
+ D.
+ D.
+ Mazauric
+
+
+ A.
+ A.
+ Roth
+
+
+ C.
+ C.H.
+ Robert
+
+
+ 10.1002/jcc.23913
+
+
+ J. of Computational Chemistry
+
+ 2015
+ 36
+ 16
+ 1213--1231
+
+
+
+
+
+ The Structural Bioinformatics Library: modeling in biomolecular science and beyond
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ T.
+ Tom
+ Dreyfus
+
+
+ hal-01379635
+
+
+
+ October 2016
+ RR-8957
+
+
+
+
+
+ Beyond Two-sample-tests: Localizing Data Discrepancies in High-dimensional Spaces
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ hal-01245408
+
+
+ IEEE/ACM International Conference on Data Science and Advanced Analytics
+ IEEE/ACM International Conference on Data Science and Advanced Analytics
+
+ Paris, France
+
+
+ March 2015
+ 29
+
+
+
+
+
+ Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ hal-02425602
+
+
+ NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems
+
+ Vancouver, Canada
+
+
+ December 2019
+
+
+
+
+
+ Comparing Two Clusterings Using Matchings between Clusters of Clusters
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ D.
+ Dorian
+ Mazauric
+
+
+ R.
+ Romain
+ Tetley
+
+
+ R.
+ Rémi
+ Watrigant
+
+
+ hal-02425599
+ 10.1145/3345951
+
+
+ ACM Journal of Experimental Algorithmics
+
+ December 2019
+ 24
+ 1
+ 1-41
+
+
+
+
+
+ Complexity dichotomies for the Minimum F -Overlay problem
+
+ hal-01947563
+ 10.1016/j.jda.2018.11.010
+
+
+ Journal of Discrete Algorithms
+
+ September 2018
+ 52-53
+ 133-142
+
+
+
+
+
+ A Sequential Non-Parametric Multivariate Two-Sample Test
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ hal-01968190
+
+
+ IEEE Transactions on Information Theory
+
+ May 2018
+ 64
+ 5
+ 3361-3370
+
+
+
+
+
+ High Resolution Crystal Structures Leverage Protein Binding Affinity Predictions
+
+
+ S.
+ Simon
+ Marillet
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ hal-01159641
+
+
+
+ March 2015
+ RR-8733
+
+
+
+
+
+ Novel Structural Parameters of Ig–Ag Complexes Yield a Quantitative Description of Interaction
+ Specificity and Binding Affinity
+
+
+ S.
+ Simon
+ Marillet
+
+
+ M.-P.
+ Marie-Paule
+ Lefranc
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ hal-01675467
+ 10.3389/fimmu.2017.00034
+
+
+ Frontiers in Immunology
+
+ February 2017
+ 8
+ 34
+
+
+
+
+
+ Hybridizing rapidly growing random trees and basin hopping yields an improved exploration of
+ energy landscapes
+
+
+ A.
+ A.
+ Roth
+
+
+ T.
+ T.
+ Dreyfus
+
+
+ C.
+ C.H.
+ Robert
+
+
+ F.
+ F.
+ Cazals
+
+
+ 10.1002/jcc.24256
+
+
+ J. Comp. Chem.
+
+ 2016
+ 37
+ 8
+ 739--752
+
+
+
+
+
+ Studying dynamics without explicit dynamics: A structure‐based study of the export mechanism by
+ AcrB
+
+
+ I.
+ Isabelle
+ Mus‐Veteau
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ hal-03006981
+ 10.1002/prot.26012
+
+
+ Proteins - Structure, Function and Bioinformatics
+
+ September 2020
+
+
+
+
+
+ Boosting the analysis of protein interfaces with Multiple Interface String Alignments:
+ illustration on the spikes of coronaviruses
+
+
+ S.
+ Stéphane
+ Bereux
+
+
+ B.
+ B
+ Delmas
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ hal-03387889
+
+
+ Proteins - Structure, Function and Bioinformatics
+
+ November 2021
+
+
+
+
+
+ Improved polytope volume calculations based on Hamiltonian Monte Carlo with boundary
+ reflections and sweet arithmetics
+
+
+ A.
+ Augustin
+ Chevallier
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ hal-03048725
+
+
+ Journal of Computational Geometry
+
+ 2022
+
+
+
+
+
+ Tripeptide loop closure: a detailed study of reconstructions based on Ramachandran
+ distributions
+
+
+ T.
+ T
+ O'donnell
+
+
+ C. H.
+ C H
+ Robert
+
+
+ F.
+ F
+ Cazals
+
+
+ hal-03232851
+
+
+ Proteins - Structure, Function and Bioinformatics
+
+ 2022
+
+
+
+
+
+ Fréchet mean and <formula rend="inline">
+ <math xmlns="http://www.w3.org/1998/Math/MathML">
+ <mi>p</mi>
+ </math>
+ </formula>-mean on the unit circle: decidability, algorithm, and applications to
+ clustering on the flat torus
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ B.
+ B
+ Delmas
+
+
+ T.
+ Timothee
+ O'donnell
+
+
+ hal-03183028
+
+
+ SEA 2021 - 19th Symposium on Experimental Algorithms
+
+ Sophia Antipolis, France
+
+
+ June 2021
+
+
+
+
+
+ Graph Algorithm Techniques for Networks and Computational Structural Biology
+
+ tel-03506086
+
+
+
+ November 2021
+
+
+
+
+
+ Graph problems motivated by (low and high) resolution models of large protein
+ assemblies
+
+
+ V.-H.
+ Viet-Ha
+ Nguyen
+
+
+ hal-03510188
+
+
+
+ December 2021
+
+
+
+
+
+ SARS-CoV-2 Through the Lens of Computational Biology:How bioinformatics is playing a key role
+ in the study of the virus and its origins
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ hal-03170023
+
+
+
+ March 2021
+ 1-35
+
+
+
+
+
+ On the complexity of overlaying a hypergraph with a graph with bounded maximum degree
+
+
+ F.
+ Frédéric
+ Havet
+
+
+ D.
+ Dorian
+ Mazauric
+
+
+ V.-H.
+ Viet-Ha
+ Nguyen
+
+
+ hal-03368214
+
+
+
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diff --git a/RADAR2TEI/TestData/abs.xml b/RADAR2TEI/TestData/abs.xml
new file mode 100644
index 0000000..c29b4df
--- /dev/null
+++ b/RADAR2TEI/TestData/abs.xml
@@ -0,0 +1,2536 @@
+
+
+
+
+ ABS
+ Algorithms - Biology - Structure
+ Computational Biology
+ Digital Health, Biology and Earth
+ http://team.inria.fr/abs
+ Creation of the Project-Team: 2008 July 01
+ Project-Team
+
+ A2.5. - Software engineering
+ A3.3.2. - Data mining
+ A3.4.1. - Supervised learning
+ A3.4.2. - Unsupervised learning
+ A6.1.4. - Multiscale modeling
+ A6.2.4. - Statistical methods
+ A6.2.8. - Computational geometry and meshes
+ A8.1. - Discrete mathematics, combinatorics
+ A8.3. - Geometry, Topology
+ A8.7. - Graph theory
+ A9.2. - Machine learning
+
+
+ B1.1.1. - Structural biology
+ B1.1.5. - Immunology
+ B1.1.7. - Bioinformatics
+
+
+
+
+
+ Frédéric
+ Cazals
+ Chercheur
+ Sophia
+ Team leader, Inria, Senior Researcher
+ oui
+
+
+ Dorian
+ Mazauric
+ Chercheur
+ Sophia
+ Inria, Researcher
+ oui
+
+
+ Edoardo
+ Sarti
+ Chercheur
+ Sophia
+ Inria, Researcher, from Oct 2021
+
+
+ Vladimir
+ Krajnak
+ PostDoc
+ Sophia
+ Inria, from Oct 2021
+
+
+ Timothee
+ O Donnell
+ PhD
+ Sophia
+ Inria
+
+
+ Louis
+ Goldenberg
+ Stagiaire
+ Sophia
+ Inria, from Mar 2021 until Aug 2021
+
+
+ Aarushi
+ Gupta
+ Stagiaire
+ Sophia
+ Inria, from May 2021 until Aug 2021
+
+
+ Valentin
+ Madelaine
+ Stagiaire
+ Sophia
+ Inria, until Mar 2021
+
+
+ Maximilien
+ Martin
+ Stagiaire
+ Sophia
+ École Normale Supérieure de Lyon, from Oct 2021
+
+
+ Florence
+ Barbara
+ Assistant
+ Sophia
+ Inria
+
+
+ Charles
+ Robert
+ CollaborateurExterieur
+ Sophia
+ CNRS
+ oui
+
+
+ Konstantin
+ Roeder
+ CollaborateurExterieur
+ Sophia
+ Robinson College - Cambridge
+
+
+
+ Overall objectives
+
+ Biomolecules and their function(s).
+
Computational Structural Biology (CSB) is the scientific domain concerned with the development of algorithms
+ and software to understand and predict the structure and function of biological macromolecules. This research
+ field is inherently multi-disciplinary. On the experimental side, biology and medicine provide the objects
+ studied, while biophysics and bioinformatics supply experimental data, which are of two main kinds. On the one
+ hand, genome sequencing projects give supply protein sequences, and ~200 millions of sequences have been
+ archived in UniProtKB/TrEMBL – which collects the protein sequences
+ yielded by genome sequencing projects. On the other hand, structure determination experiments (notably X-ray
+ crystallography, nuclear magnetic resonance, and cryo-electron microscopy) give access to geometric models of
+ molecules – atomic coordinates. Alas, only ~150,000 structures have been solved and deposited in the Protein
+ Data Bank (PDB), a number to be compared against the sequences found in UniProtKB/TrEMBL. With one structure for ~1000 sequences, we hardly know anything
+ about biological functions at the atomic/structural level. Complementing experiments, physical
+ chemistry/chemical physics supply the required models (energies, thermodynamics, etc). More specifically, let us
+ recall that proteins with atoms has Cartesian coordinates, and fixing
+ these (up to rigid motions) defines a conformation. As conveyed by the iconic lock-and-key
+ metaphor for interacting molecules, Biology is based on the interactions stable conformations make with each
+ other. Turning these intuitive notions into quantitative ones requires delving into statistical physics, as
+ macroscopic properties are average properties computed over ensembles of conformations. Developing effective
+ algorithms to perform accurate simulations is especially challenging for two main reasons. The first one is the
+ high dimension of conformational spaces – see above, typically several tens of
+ thousands, and the non linearity of the energy functionals used. The second one is the multiscale nature of the
+ phenomena studied: with biologically relevant time scales beyond the millisecond, and atomic vibrations periods
+ of the order of femto-seconds, simulating such phenomena typically requires conformations/frames, a (brute)
+ tour de force rarely achieved .
+
+
+
+ Computational Structural Biology: three main challenges.
+
The first challenge, sequence-to-structure prediction, aims to infer the possible
+ structure(s) of a protein from its amino acid sequence. While recent progress has been made recently using in
+ particular deep learning techniques , the models obtained so far
+ are static and coarse-grained.
+
The second one is protein function prediction. Given a protein with known structure i.e. 3D coordinates, the goal is to predict the partners of this protein, in terms of stability
+ and specificity. This understanding is fundamental to biology and medicine, as illustrated by the example of the
+ SARS-CoV-2 virus responsible of the Covid19 pandemic. To infect a host, the virus first fuses its envelope with
+ the membrane of a target cell, and then injects its genetic material into that cell. Fusion is achieved by a
+ so-called class I fusion protein, also found in other viruses (influenza, SARS-CoV-1, HIV, etc). The fusion
+ process is a highly dynamic process involving large amplitude conformational changes of the molecules. It is
+ poorly understood, which hinders our ability to design therapeutics to block it.
+
+
Finally, the third one, large assembly reconstruction, aims at solving (coarse-grain)
+ structures of molecular machines involving tens or even hundreds of subunits. This research vein was promoted
+ about 15 years back by the work on the nuclear pore complex . It is often
+ referred to as reconstruction by data integration, as it necessitates to combine coarse-grain
+ models (notably from cryo-electron microscopy (cryo-EM) and native mass spectrometry) with atomic models of
+ subunits obtained from X ray crystallography. Fitting the latter into the former requires exploring the
+ conformation space of subunits, whence the importance of protein dynamics.
+
As an illustration of these three challenges, consider the problem of designing proteins blocking the entry of
+ SARS-CoV-2 into our cells (Fig. ). The first challenge is illustrated
+ by the problem of predicting the structure of a blocker protein from its sequence of amino-acids – a tractable
+ problem here since the mini proteins used only comprise of the order of 50 amino-acids (Fig. (A), ). The second
+ challenge is illustrated by the calculation of the binding modes and the binding affinity of the designed
+ proteins for the RBD of SARS-CoV-2 (Fig. (B)). Finally, the last challenge is
+ illustrated by the problem of solving structures of the virus with a cell, to understand how many spikes are
+ involved in the fusion mechanism leading to infection. In , the promising
+ designs suggested by modeling have been assessed by an array of wet lab experiments (affinity measurements,
+ circular dichroism for thermal stability assessment, structure resolution by cryo-EM). The hyperstable minibinders identified provide starting points for SARS-CoV-2 therapeutics . We note in passing that this is truly remarkable work, yet,
+ the designed proteins stem from a template (the bottom helix from ACE2), and are rather
+ small.
To present challenges in structural modeling, let us recall the following ingredients. First, a molecular model
+ with atoms
+ is parameterized over a conformational space of dimension in Cartesian coordinates, or
+ in internal
+ coordinate–upon removing rigid motions, also called degree of freedom (d.o.f.). Second,
+ recall that the potential energy landscape (PEL) is the mapping from to providing a
+ potential energy for each conformation , . Example potential energies (PE) are CHARMM, AMBER, MARTINI, etc. Such PE belong to the realm of
+ molecular mechanics, and implement atomic or coarse-grain models. They may embark a solvent model, either
+ explicit or implicit. Their definition requires a significant number of parameters (up to ), fitted to reproduce
+ physico-chemical properties of (bio-)molecules .
+
These PE are usually considered good enough to study non covalent interactions – our focus, even tough they do
+ not cover the modification of chemical bonds. In any case, we take such a function for granted .
+
The PEL codes all structural, thermodynamic, and kinetic properties,
+ which can be obtained by averaging properties of conformations over so-called thermodynamic
+ ensembles. The structure of a macromolecular system requires the characterization of
+ active conformations and important intermediates in functional pathways involving significant basins. In
+ assigning occupation probabilities to these conformations by integrating Boltzmann's distribution, one treats
+ thermodynamics. Finally, transitions between the states, modeled, say, by a master
+ equation (a continuous-time Markov process), correspond to kinetics. Classical simulation
+ methods based on molecular dynamics (MD) and Monte Carlo sampling (MC) are developed in the lineage of the
+ seminal work by the 2013 recipients of the Nobel prize in chemistry (Karplus, Levitt, Warshel), which was
+ awarded “for the development of multiscale models for complex chemical systems”. However,
+ except for highly specialized cases where massive calculations have been used , neither MD nor MC give access to the aforementioned time
+ scales. In fact, the main limitation of such methods is that they treat structural, thermodynamic and kinetic
+ aspects at once . The absence of specific insights on these three
+ complementary pieces of the puzzle makes it impossible to optimize simulation methods, and results in general in
+ the inability to obtain converged simulations on biologically relevant time-scales.
+
The hardness of structural modeling owes to three intertwined reasons.
+
First, PELs of biomolecules usually exhibit a number of critical points exponential in the dimension ; fortunately, they enjoy a multi-scale structure . Intuitively, the significant local minima/basins are those
+ which are deep or isolated/wide, two notions which are mathematically
+ qualified by the concepts of persistence and prominence. Mathematically, problems are plagued with the curse of
+ dimensionality and measure concentration phenomena. Second, biomolecular processes are inherently multi-scale,
+ with motions spanning 15 and 4 orders of magnitude in time and amplitude respectively . Developing methods able to exploit this multi-scale
+ structure has remained elusive. Third, macroscopic properties of biomolecules i.e.
+ observables, are average properties computed over ensembles of conformations, which calls for a multi-scale
+ statistical treatment both of thermodynamics and kinetics.
+
+
+ Validating models.
+
A natural and critical question naturally concerns the validation of models proposed in structural
+ bioinformatics. For all three types of questions of interest (structures, thermodynamics, kinetics), there exist
+ experiments to which the models must be confronted – when the experiments can be conducted.
+
For structures, the models proposed can readily be compared against experimental results stemming from X ray
+ crystallography, NMR, or cryo electron microscopy. For thermodynamics, which we illustrate here with binding
+ affinities, predictions can be compared against measurements provided by calorimetry or surface plasmon
+ resonance. Lastly, kinetic predictions can also be assessed by various experiments such as binding affinity
+ measurements (for the prediction of and ), or fluorescence
+ based methods (for kinetics of folding).
+
+
+
+ Research program
+
Our research program ambition to develop a comprehensive set of novel concepts and algorithms to study protein
+ dynamics, based on the modular framework of PEL.
+
+ Modeling the dynamics of proteins
+ Keywords: Molecular conformations, conformational exploration, energy landscapes,
+ thermodynamics, kinetics.
+
As noticed while discussing Protein dynamics: core CS - maths challenges, the integrated
+ nature of simulation methods such as MD or MC is such that these methods do not in general give access to
+ biologically relevant time scales. The framework of energy landscapes , (Fig. ) is much more
+ modular, yet, large biomolecular systems remain out of reach.
+
To make a definitive step towards solving the prediction of protein dynamics, we will serialize the discovery
+ and the exploitation of a PEL , , . Ideas and
+ concepts from computational geometry/geometric motion planning, machine learning, probabilistic algorithms, and
+ numerical probability will be used to develop two classes of probabilistic algorithms. The first deals with
+ algorithms to discover/sketch PELs i.e. enumerate all significant (persistent or prominent)
+ local minima and their connections across saddles, a difficult task since the number of all local
+ minima/critical points is generally exponential in the dimension. To this end, we will develop a hierarchical
+ data structure coding PELs as well as multi-scale proposals to explore molecular conformations. (Nb: in Monte
+ Carlo methods, a proposal generates a new conformation from an existing one.) The second focuses on methods to
+ exploit/sample PELs i.e. compute so-called densities of states, from which all thermodynamic
+ quantities are given by standard relations . This is a hard problem akin to high-dimensional
+ numerical integration. To solve this problem, we will develop a learning based strategy for the Wang-Landau
+ algorithm –an adaptive Monte Carlo Markov Chain (MCMC) algorithm, as
+ well as a generalization of multi-phase Monte Carlo methods for convex/polytope volume calculations , , for
+ non convex strata of PELs.
As discussed in the previous Section, the study of PEL and protein dynamics raises difficult algorithmic /
+ mathematical questions. As an illustration, one may consider our recent work on the comparison of high
+ dimensional distribution , statistical tests /
+ two-sample tests , ,
+ the comparison of clustering , the complexity study of
+ graph inference problems for low-resolution reconstruction of assemblies , the analysis of partition (or clustering) stability
+ in large networks, the complexity of the representation of simplicial complexes . Making progress on such questions is
+ fundamental to advance the state-of-the art on protein dynamics.
+
We will continue to work on such questions, motivated by CSB / theoretical biophysics, both in the continuous
+ (geometric) and discrete settings. The developments will be based on a combination of ideas and concepts from
+ computational geometry, machine learning (notably on non linear dimensionality reduction, the reconstruction of
+ cell complexes, and sampling methods), graph algorithms, probabilistic algorithms, optimization, numerical
+ probability, and also biophysics.
While our main ambition is to advance the algorithmic foundations of molecular simulation, a major challenge
+ will be to ensure that the theoretical and algorithmic developments will change the fate of applications, as
+ illustrated by our case studies. To foster such a symbiotic relationship between theory, algorithms and
+ simulation, we will pursue high quality software development and integration within the SBL,
+ and will also take the appropriate measures for the software to be widely adopted.
+
+ Software in structural bioinformatics.
+
Software development for structural bioinformatics is especially challenging, combining advanced geometric,
+ numerical and combinatorial algorithms, with complex biophysical models for PEL and related
+ thermodynamic/kinetic properties. Specific features of the proteins studied must also be accommodated. About
+ 50 years after the development of force fields and simulation methods (see the 2013 Nobel prize in chemistry),
+ the software implementing such methods has a profound impact on molecular science at large. One can indeed
+ cite packages such as CHARMM, AMBER, gromacs, gmin, MODELLER, Rosetta, VMD, PyMol, .... On the other hand, these packages are goal oriented, each tackling a (small set
+ of) specific goal(s). In fact, no real modular software design and integration has taken place. As a result,
+ despite the high quality software packages available, inter-operability between algorithmic building blocks
+ has remained very limited.
+
+
+ The SBL.
+
Predicting the dynamics of large molecular systems requires the integration of advanced algorithmic building
+ blocks / complex software components. To achieve a sufficient level of integration, we undertook the
+ development of the Structural Bioinformatics Library (SBL, ) , a generic C++/python
+ cross-platform library providing software to solve complex problems in structural bioinformatics. For
+ end-users, the SBL provides ready to use, state-of-the-art applications to model
+ macro-molecules and their complexes at various resolutions, and also to store results in perennial and easy to
+ use data formats (). For developers, the SBL provides a
+ broad C++/python toolbox with modular design (). This hybrid status targeting both
+ end-users and developers stems from an advanced software design involving four software components, namely
+ applications, core algorithms, biophysical models, and modules (). This modular design makes it possible to optimize
+ robustness and the performance of individual components, which can then be assembled within a goal oriented
+ application.
+
+
+
+ Applications: modeling interfaces, contacts, and interactions
+ Keywords: Protein interactions, protein complexes,
+ structure/thermodynamics/kinetics prediction.
+
Our methods will be validated on various systems for which flexibility operates at various scales. Example such
+ systems are antibody-antigen complexes, (viral) polymerases, (membrane) transporters.
+
Even very complex biomolecular systems are deterministic in prescribed conditions (temperature, pH, etc),
+ demonstrating that despite their high dimensionality, all d.o.f. are not at play at the same
+ time. This insight suggests three classes of systems of particular interest. The first class consists of systems
+ defined from (essentially) rigid blocks whose relative positions change thanks to conformational changes of
+ linkers; a Newton cradle provides an interesting way to envision such as system. We have recently worked on one
+ such system, a membrane proteins involve in antibiotic resistance (AcrB, see . The second class consists of cases where relative
+ positions of subdomains do not significantly change, yet, their intrinsic dynamics are significantly altered. A
+ classical illustration is provided by antibodies, whose binding affinity owes to dynamics localized in six
+ specific loops , .
+ The third class, consisting of composite cases, will greatly benefit from insights on the first two classes. As
+ an example, we may consider the spikes of the SARS-CoV-2 virus, whose function (performing infection) involves
+ both large amplitude conformational changes and subtle dynamics of the so-called receptor binding domain. We
+ have started to investigate this system, in collaboration with B. Delmas (INRAe) .
+
In ABS, we will investigate systems in these three tiers, in collaboration with expert collaborators, to
+ hopefully open new perspectives in biology and medicine. Along the way, we will also collaborate on selected
+ questions at the interface between CSB and systems biology, as it is now clear that the structural level and the
+ systems level (pathways of interacting molecules) can benefit from one another.
+
+
+
+ Application domains
+
The main application domain is Computational Structural Biology, as underlined in the Research
+ Program.
+
+
+ Highlights of the year
+
In October 2021, Edoardo Sarti has joined ABS as Chargé de Recherche de Classe Normale. His
+ expertise comprises a diverse set of interests spanning from algorithmic questions about geometrical, functional
+ and evolutionary aspects of biomolecules (latest study: ), to
+ the collection and analysis of large collections of molecular structural data. From the very start, E. Sarti has
+ started taking part in several research and technical projects of ABS.
+
+
+ New software and platforms
+
See report on the Structural Bioinformatics Library.
+
+ New software
+
+ SBL
+
+
+
+
+
+
+
+
+
+
+
+
+
+ New results
+
+ Modeling interfaces, contacts, and interactions
+ Keywords: docking, scoring, interfaces, protein complexes, Voronoi diagrams,
+ arrangements of balls.
+
+ Boosting the analysis of protein interfaces with Multiple Interface String Alignments: illustration
+ on the spikes of coronaviruses
+
+
+ F.
+ Cazals
+
+
+
+ In collaboration with S. Bereux and B. Delmas (INRAe, Jouy-en-Josas).
+
+
In this work , we introduce Multiple Interface
+ String Alignment (MISA), a visualization tool to display coherently various sequence and structure
+ based statistics at protein-protein interfaces (SSE elements, buried surface area, , B factor values, etc). The
+ amino-acids supporting these annotations are obtained from Voronoi interface models. The benefit of MISA is to
+ collate annotated sequences of (homologous) chains found in different biological contexts i.e. bound with
+ different partners or unbound. The aggregated views MISA/SSE, MISA/BSA, MISA/ etc make it trivial to identify
+ commonalities and differences between chains, to infer key interface residues, and to understand where
+ conformational changes occur upon binding. As such, they should prove of key relevance for knowledge based
+ annotations of protein databases such as the Protein Data Bank.
+
Illustrations are provided on the receptor binding domain (RBD) of coronaviruses, in complex with their
+ cognate partner or (neutralizing) antibodies. MISA computed with a minimal number of structures complement and
+ enrich findings previously reported.
+
The corresponding package is available from the Structural Bioinformatics Library (
+
and ).
+
+
+ SARS-CoV-2 Through the Lens of Computational Biology: How bioinformatics is playing a key role in the
+ study of the virus and its origins
+
+
+ F.
+ Cazals
+
+
+
+ In collaboration with Samuel Alizon (MIVEGEC - Maladies infectieuses et vecteurs : écologie,
+ génétique, évolution et contrôle), Stéphane Guindon (MAB - Méthodes et Algorithmes pour la Bioinformatique,
+ LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier), Claire Lemaitre
+ (GenScale - Scalable, Optimized and Parallel Algorithms for Genomics, Inria Rennes – Bretagne Atlantique),
+ Tristan Mary-Huard (INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et
+ l’Environnement), Anna Niarakis (Lifeware - Computational systems biology and optimization, Inria Saclay - Ile
+ de France; GenHotel - Laboratoire de recherche européen pour la polyarthrite rhumatoïde), Mikaël Salson
+ (CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189), Celine Scornavacca
+ (UMR ISEM - Institut des Sciences de l'Evolution de Montpellier), Hélène Touzet (CRIStAL - Centre de Recherche
+ en Informatique, Signal et Automatique de Lille - UMR 9189).
+
+
On December 2019, the Chinese Center for Disease Control reported several cases of severe pneumonia that
+ resists usual treatments in the city of Wuhan. This was the beginning of the COVID-19 pandemic which caused
+ more than 80 millions infection cases and 1.7 millions deaths during the year 2020 alone1 . This major
+ outbreak has given rise to global public health responses as well as an international research effort of
+ unprecedented scope and speed. This scientific mobilization has led to remarkable results, which have enabled
+ a great deal of knowledge to be accumulated in just a few months on this novel pathogen: identification of the
+ virus, of its main proteins, analysis of its origin and its functionning. This basic biological knowledge is
+ mandatory to medical advances: design tests, find a vaccine or a cure.
+
In this document , one year after the beginning of the worldwide
+ spread of the disease, we wish to shed particular light on the contribution of bioinformatics in all this
+ work. Bioinformatics is a discipline at crossroads of computer sciences, mathematics and biology that has
+ taken on an inestimable importance in modern biology and medicine. It provides computational models,
+ algorithms and software to the scientific community, that are both operational and effective. The discovery
+ and study of the SARS-Cov-2 coronavirus is an emblematic example. The utilization of bioinformatics methods
+ has been at the heart of essential milestones : from the sequencing of the virus genome and its annotation to
+ the history of its origin, the modelisation of interacting biological entities both at the molecular scale and
+ at the network scale, and the study of the host genetic susceptibility. All these studies, as a whole, have
+ made it possible to elucidate the nature and the functionning of the novel pathogen and have greatly
+ contributed to the fight against COVID-19.
+
+
+ Gene prioritization based on random walks with restarts and absorbing states, to define gene sets
+ regulating drug pharmacodynamics from single-cell analyses
+
+
+ F.
+ Cazals
+
+
+ D.
+ Mazauric
+
+
+ A.
+ Sales de Queiroz
+
+
+ G.
+ Sales Santa Cruz
+
+
+
+ In collaboration with Alain Jean-Marie (Inria Neo) and Jérémie Roux (Inserm and CNRS and UCA).
+
+
Prioritizing genes for their role in drug sensitivity, is an important step in understanding drugs mechanisms
+ of action and discovering new molecular targets for co-treatment. In this work , we formalize this problem by considering
+ two sets of genes and respectively composing the predictive gene signature of sensitivity to a
+ drug and the genes involved in its mechanism of action, as well as a protein interaction network (PPIN)
+ containing the products of and as nodes. We introduce Genetrank, a method to prioritize
+ the genes in for their likelihood to regulate the genes in .
+
Genetrank uses asymmetric random walks with restarts, absorbing states, and a suitable
+ renormalization scheme. Using novel so-called saturation indices, we show that the conjunction of absorbing
+ states and renormalization yields an exploration of the PPIN which is much more progressive than that afforded
+ by random walks with restarts only. Using MINT as underlying network, we apply Genetrank to
+ a predictive gene signature of cancer cells sensitivity to tumor-necrosis-factor-related apoptosis-inducing
+ ligand (TRAIL), performed in single-cells. Our ranking provides biological insights on drug sensitivity and a
+ gene set considerably enriched in genes regulating TRAIL pharmacodynamics when compared to the most
+ significant differentially expressed genes obtained from a statistical analysis framework alone. We also
+ introduce gene expression radars, a visualization tool to assess all pairwise interactions
+ at a glance.
+
Genetrank is made available in the Structural Bioinformatics Library (). It should prove useful for mining gene sets in
+ conjunction with a signaling pathway, whenever other approaches yield relatively large sets of genes.
+
+
+
+ Modeling the dynamics of proteins
+ Keywords: protein, flexibility, collective coordinate, conformational sampling
+ dimensionality reduction.
+
+ Tripeptide loop closure: a detailed study of reconstructions based on Ramachandran
+ distributions
+
+
+ F.
+ Cazals
+
+
+ T.
+ O'Donnell
+
+
+
+ In collaboration with C. Robert (IBPC / CNRS, Paris, France).
+
+
Tripeptide loop closure (TLC) is a standard procedure to reconstruct protein backbone conformations, by
+ solving a polynomial system in a single variable yielding up to 16 real solutions.
+
In this work , we first show that multiprecision is required in
+ a TLC solver to guarantee the existence and the accuracy of solutions. We then compare solutions yielded by
+ the TLC solver against tripeptides from the Protein Data Bank. We show that these solutions are geometrically
+ diverse (up to RMSD with respect to the data), and sound in terms
+ of potential energy. Finally, we compare Ramachandran distributions of data and reconstructions for the three
+ amino acids. The distribution of reconstructions in the second angular space
+ stands out, with a rather uniform distribution leaving a central void.
+
We anticipate that these insights, coupled to our robust implementation in the Structural Bioinformatics
+ Library (), will help understanding the
+ properties of TLC reconstructions, with potential applications to the generation of conformations of flexible
+ loops in particular.
+
+
+
+ Algorithmic foundations
+ Keywords: Computational geometry, computational topology, optimization, data
+ analysis.
+
+ Frechet mean and p-mean on the unit circle: characterization, decidability, and algorithm
+
+
+ F.
+ Cazals
+
+
+ T.
+ O'Donnell
+
+
+
+ In collaboration with B. Delmas (INRAe, Jouy-en-Josas).
+
+
The center of mass of a point set lying on a manifold generalizes the celebrated Euclidean centroid, and is
+ ubiquitous in statistical analysis in non Euclidean spaces.
+
In this work , we give a complete characterization of the weighted
+ -mean of
+ a finite set of angular values on , based on a decomposition of such that the functional of interest has at most one
+ local minimum per cell. This characterization is used to show that the problem is decidable for rational
+ angular values –a consequence of Lindemann's theorem on the transcendence of , and to develop an effective
+ algorithm parameterized by exact predicates. A robust implementation of this algorithm based on
+ multi-precision interval arithmetic is also presented, and is shown to be effective for large values of
+ and
+ . We use
+ it as building block to implement the k-means and k-means++ clustering algorithms on the flat torus, with
+ applications to clustering protein molecular conformations. These algorithms are available in the Structural
+ Bioinformatics Library ().
+
Our derivations are of interest in two respects. First, efficient -mean calculations are relevant to
+ develop principal components analysis on the flat torus encoding angular spaces–a particularly important case
+ to describe molecular conformations. Second, our two-stage strategy stresses the interest of combinatorial
+ methods for p-means, also emphasizing the role of numerical issues.
+
+
+ Improved polytope volume calculations based on Hamiltonian Monte Carlo with boundary reflections and
+ sweet arithmetics
+
+
+ F.
+ Cazals
+
+
+ A.
+ Chevallier
+
+
+
+ In collaboration with S. Pion IMS (Univ. Bordeaux / Bordeaux INP / CNRS UMR 5218).
+
+
Computing the volume of a high dimensional polytope is a fundamental problem in geometry, also connected to
+ the calculation of densities of states in statistical physics, and a central building block of such algorithms
+ is the method used to sample a target probability distribution.
+
This paper studies Hamiltonian Monte Carlo (HMC) with
+ reflections on the boundary of a domain, providing an enhanced alternative to Hit-and-run (HAR) to sample a
+ target distribution restricted to the polytope. We make three contributions. First, we provide a convergence
+ bound, paving the way to more precise mixing time analysis. Second, we present a robust implementation based
+ on multi-precision arithmetic, a mandatory ingredient to guarantee exact predicates and robust constructions.
+ We however allow controlled failures to happen, introducing the Sweeten Exact Geometric
+ Computing (SEGC) paradigm. Third, we use our HMC random walk to perform H-polytope volume calculations,
+ using it as an alternative to HAR within the volume algorithm by Cousins and Vempala. The systematic tests
+ conducted up to dimension on the cube, the isotropic and the
+ standard simplex show that HMC significantly outperforms HAR both in terms of accuracy and running time.
+ Additional tests show that calculations may be handled up to dimension .
+ These tests also establish that multiprecision is mandatory to avoid exits from the polytope.
+
+
+ Overlaying a hypergraph with a graph with bounded maximum degree, with application for low-resoluton
+ reconstructions of molecular assemblies
+
+
+ D.
+ Mazauric
+
+
+
+ In collaboration with F. Havet, T. V. H. Nguyen laboratoire I3S (CNRS, Université Côte d'Azur).
+
+
We analyze a generalization of the minimum connectivity inference problem (MCI) that models the computation
+ of low-resolution structures of macro-molecular assemblies, based on data obtained by native mass
+ spectrometry. The generalization studied in this work, allows us to consider more refined constraints for the
+ characterization of low resolution models of large assemblies, such as degree constraints (e.g. a protein has
+ a limited number of other proteins in contact).
+
More precisely, let and be respectively a graph and a hypergraph defined on a same set of vertices,
+ and let be
+ a graph. We say that -overlays a hyperedge
+ of
+ if the
+ subgraph of induced by contains as a spanning subgraph, and
+ that -overlays if it -overlays every hyperedge of
+ . For a
+ fixed graph and a fixed integer , the problem --Overlay
+ consists in deciding whether there exists a graph with maximum degree at most that -overlays a given hypergraph
+ . In , we prove that for any graph which is neither complete
+ nor anticomplete, there exists an integer such that --Overlay is
+ -complete for all .
+
+
+ Conflict coloring problems: complexity and application to high resolution biological assembly
+ modeling
+
+
+ F.
+ Cazals
+
+
+ D.
+ Mazauric
+
+
+
+ In collaboration with F. Havet, T. V. H. Nguyen laboratoire I3S (CNRS, Université Côte d'Azur).
+
+
Given a graph ,
+ a color set for each vertex , a bipartite graph between
+ color sets and for every edge , Conflict
+ Coloring consists in deciding whether exists a conflict coloring, that is a coloring in which
+ is not an edge of the bipartite graph. Conflict Coloring is motivated by computational
+ structural biology problems, high resolution determination of molecular assemblies. The graph represents the
+ subunits and the interaction between them, the colors are the given conformations, and the edges of the
+ bipartite graphs are the incompatible conformations of two subunits.
+
In this work, we first establish the complexity dichotomies (polynomial vs -complete)
+ for Conflict Coloring and its variants. We provide some experiments in which we build
+ instances of Conflict Coloring associated to Voronoi diagram in the
+ plane, and we then analyse the existences of a solution related to parameters used in our experimental
+ setup.
+
+
+
+
+ Partnerships and cooperations
+
+
+ F.
+ Cazals
+
+
+ D.
+ Mazauric
+
+
+
+
+ International research visitors
+
+ Visits of international scientists
+
+ Inria International Chair
+
+
David Wales, Cambridge University, is endowed chair within 3IA Côte d'Azur / ABS.
Frédéric Cazals was involved in the organization of:
+
+
Symposium Multidisciplinary approaches in cancer research, Organized at Inria Sophia
+ Antipolis Méditerranée. Web: .
+
Winter School Machine Learning Methods to Analyze and Predict Protein Structure, Dynamics
+ and Function, CIRM, Luminy, November 7-12, 2021. Web: .
+
+ Critical evaluation of methods for scoring interfaces of protein complexes, Online Elixir
+ 3D-Bioinfo meeting, organized by Emmanuel Levy (Elixir IL), Frederic Cazals (Elixir FR), Shoshana Wodak
+ (Elixir BE).
+
+
+
+ Scientific events: selection
+
+ Member of the conference program committees
+
Frédéric Cazals participated to the following program committees:
+
+
Symposium on Solid and Physical Modeling
+
Intelligent Systems for Molecular Biology (ISMB) / European Conference on Computational Biology
+ (ECCB)
+
+
+
+
+ Invited talks
+
Frédéric Cazals gave the following invited talks:
+
+
+ Mining protein flexibility: a new class of move sets; GDR BIM/GT MASIM, November 2021;
+ UCA, 5th Academy 4 Research Webinar - Mental Retardation and Protein Dynamics, October 2021.
+
+
+
+ Leadership within the scientific community
+
Frédéric Cazals
+
+
2010-...: Member of the steering committee of the GDR Bioinformatique Moléculaire, for the Structure and
+ macro-molecular interactions theme.
+
2017-...: Co-chair, with Yann Ponty, of the working group / groupe de travail (GT MASIM - Méthodes
+ Algorithmiques pour les Structures et Interactions Macromoléculaires), within the GDR de BIoinfor- matique
+ Moléculaire (GDR BIM, ).
+
+
+
+ Research administration
+
Frédéric Cazals
+
+
2018-...: Member of the bureau du comité des équipes projets.
+
2020-...: Member of the bureau of the EUR Life, Université Côte d’Azur.
2014–...: Master Data Sciences Program (M2), Department of Applied Mathematics, Ecole Centrale-Supélec;
+ Foundations of Geometric Methods in Data Analysis; F. Cazals and M. Carrière, Inria
+ Sophia / (ABS, DataShape). Web: .
+
2021–...: Master Data Sciences & Artificial Intelligence (M1), Université Côte d’Azur; Introduction to machine learning (course practicals); E. Sarti.
+
2021–...: Master Data Sciences & Artificial Intelligence (M2), Université Côte d’Azur; Geometric and topological methods in machine learning; F. Cazals, J-D. Boissonnat and M. Carrière,
+ Inria Sophia / (ABS, DataShape, DataShape); Web: .
+
2021–...: Master Cancérologie et Recherche Translationnelle (M2), Université Côte d’Azur; Binding affinity maturation and protein interaction network analysis: two examples of bioinformatics
+ applications in medicine; F. Cazals.
+
2020–...: Master Sciences du Vivant (M2), parcours Biologie, Informatique, Mathématiques, Université Côte
+ d’Azur; Introduction to statistical physics of biomolecules; F. Cazals.
+ PhD in progress, 3rd year: Timothée O'Donnel, Modeling the influenza
+ polymerase. Université Côte d'Azur. Thesis co-supervised by Frédéric Cazals and Bernard Delmas, INRA
+ Jouy-en-Josas.
+
+ Defended PhD: Thi Viet Ha Nguyen, Graph Algorithms techniques for (low
+ and high) resolution models of large protein assemblies. Université Côte d'Azur. Thesis co-supervised
+ by Frédéric Havet, Laboratoire I3S (CNRS, Université Côte d'Azur).
+
+
Interns:
+
+
Aarushi Gupta, intern from IIT Delhi, summer 2021. Modeling protein backbone flexibility
+ using solutions of the tripeptide loop closure.
+
+
Louis Goldenberg, intern from Ecole Polytechnique, summer 2021. Parametric models for
+ compact clusters.
+
+
Sebastián Gallardo Diaz, Universidad Técnica Federico Santa Marı́a, Valparaı́so, Chile. Advisors: Pierre
+ Kornprobst (Inria project-team Biovision), Dorian Mazauric. Algorithms for a new packing
+ problem : Towards Reading Accessible Newspapers.
+
Vivian Losciale, Université Côte d'Azur. Advisors: Jérémy Camponovo, Frédéric Havet, Buntheng Ly, Dorian
+ Mazauric, Maxime Sermesant.Jeux-vidéos de médiation : Intelligence artificielle pour
+ l’imagerie médicale.
+
Quentin Larose, Université Côte d'Azur. Advisors: Agnès Bessière, Carole Clastres, Jérémy Camponovo, Luc
+ Hogie, Dorian Mazauric, Eric Pascual, Sandrine Selosse, Brigitte Trousse. Portail des
+ ressources Terra Numerica.
+
+
+
+ Juries
+
Frédéric Cazals participated to the following committees:
+
+
Luke Dicks, Cambridge University, April 2021. Rapporteur for the PhD thesis K-means
+ landscapes: exploring clustering solution spaces using energy landscape theory. Advisor: David
+ Wales.
+
Manon Ruffini, Univ. of Toulouse, March 2021. Rapporteur on the PhD thesis Models and
+ Algorithms for Computational Protein Design. Advisor: Thomas Schiex.
+
Dorian Mazauric, Habilitation thesis, Université Côte d'Azur, November 2021. Committee member (president)
+ for the habilitation Algorithmique des graphes pour les réseaux et la biologie structurale
+ computationnelle.
+
+
Dorian Mazauric participated to the following committees:
+
+
Thi Viet Ha Nguyen, Université Côte d'Azur, December 2021. Committee member for the PhD thesis Graph Algorithms techniques for (low and high) resolution models of large protein
+ assemblies. Advisors: Frédéric Havet, Dorian Mazauric.
2019–...: Head of Commission (Médiation et Animation des
+ MAthématiques, des Sciences et Techniques Informatiques et des Communications), Inria Sophia Antipolis -
+ Méditerranée.
+
2019–...: Coordinator of , an ambitious scientific
+ popularisation project. Its main goal is to create a "Dedicated Digital space" in the south of France, (in
+ the spirit of the "Cité des Sciences" or "Palais de la découverte" in Paris). To do so, Terra Numerica is
+ developing and structuring popularisation activities, supports which are spread in different antennas
+ throughout the territory (e.g. Espace Terra Numerica - Valbonne Sophia Antipolis, MIA, in schools,
+ exhibition extensions...). This large-scale project involves (brings together) all the actors of research,
+ education, industry, associations and collectivities... It is actually composed of more than one hundred
+ people.
+
2018–...: Member of the Conseil d'Administration de l'association les Petits Débrouillards.
+
2017–...: Member of projet de médiation Galéjade : Graphes et ALgorithmes : Ensemble de Jeux À Destination
+ des Ecoliers... (mais pas que).
+
+
+
+ Articles and contents
+
Frédéric Cazals:
+
+
Podcast Investiga’Sciences Vive la protéine: interview-discussion of
+ Thomas Schiex and myself by Valérie Ravinet, October 2021. .
+
+
Dorian Mazauric:
+
+
Participation to the development of .
+
Participation to the development of popularization videos games .
+
+
+
+ Interventions
+
Dorian Mazauric - Fête de la Science 2021:
+
+
Village des Sciences de Villeneuve-Loubet Avec Pobot. Samedi 02 octobre 2021 et dimanche 03 octobre 2021.
+ With Thomas Dissaux, Adrien Gausseran, Nicolas Nisse, Eric Pascual, Lucas Picassari-Arrieta, Brigitte
+ Trousse.
+
Village des sciences de la vallée de la Vésubie avec Les Apprentis Pas Sages Samedi 02 octobre 2021. Puzzle du nid d’abeilles – Graphes et algorithmes grandeur nature. With Samantha
+ Lanney-Ricci, Magali Martin-Mazauric.
+
Interventions au Campus International de Valbonne Lundi 04 octobre 2021. La magie du binaire
+ – Pas besoin de réfléchir, les ordinateurs calculent tellement vite ? Problèmes actuels en
+ algorithmique. With Estelle Zavoli.
+
Atelier scientifique à l’Espace d’Art Concret (EAC), Mouans-Sartoux Organisé par l’EAC (Amandine Briand,
+ Sabrina Lah, Martin Merle, Claire Spada, Brigitte Segatori, Roubaud). Du lundi 04 octobre 2021 au vendredi
+ 08 octobre 2021. Des reines sur une oeuvre d’art (Tenth Copper Corner une oeuvre minimaliste
+ de Carl André formée de 55 carreaux) : mathématiques et algorithmique. With Frédéric Havet, Nicolas
+ Nisse, Martine Olivi. En collaboration avec Geoffroy Aubry et Valérie Doya (atelier de Physique).
+
Intervention au collège La chênaie de Mouans-Sartoux Mercredi 06 octobre 2021. La magie des
+ graphes et du binaire – Pas besoin de réfléchir, les ordinateurs calculent tellement vite ? Problèmes
+ actuels en algorithmique. With Mylène Raibaudi, Brigitte Trousse.
+
Village des Sciences de Mouans-Sartoux Samedi 09 octobre 2021. Sabrina Barnabé, Martine Olivi, Brigitte
+ Trousse, Thierry Viéville.
+
Festival des sciences de Nice d’Université Côte d’Azur Samedi 09 octobre 2021 et dimanche 10 octobre 2021.
+ With Alexandre Bonlarron, Foivos Fioravantes, Victor Jung, Hicham Lesfari, Steve Malalel, Magali
+ Martin-Mazauric, Romain Michelucci, Nicolas Nisse, Marie Pelleau, Nina Singlan, Rudan Xiao.
+
Interventions au collège de Roquebillière Jeudi 07 octobre 2021. La magie des graphes et du
+ binaire – Jeux combinatoires – Pas besoin de réfléchir, les ordinateurs calculent tellement vite ?
+ Problèmes actuels en algorithmique – Ateliers Jeux Graphes et Algorithmes. With Samantha
+ Lanney-Ricci.
+
Conférence à la médiathèque de Biot Vendredi 08 octobre 2021.
+
Village des Sciences et de l’Innovation de la CASA à Antibes Juan-les-Pins Avec PoBot, SLV, @b4games.
+ Samedi 16 octobre 2021 et dimanche 17 octobre 2021. With Agnès Bessière, Armel Berceliot, Étienne Chaplain,
+ Thomas Dissaux, Thierry Lespinasse, Stéphane Mansour, Magali Martin-Mazauric, Nicolas Nisse, Eric Pascual,
+ Lucas Picassari-Arrieta, Frédéric Rallo, Sandrine Selosse, Brigitte Trousse.
+
+
Dorian Mazauric - Interventions at Maison de l'Intelligence Artificielle:
+
+
Ateliers Terra Numerica avec les étudiants du Master SmartEdTech. Mercredi 14 avril 2021. Journée
+ intensive de formation hybride et animation et co-création d’ateliers. With Saint-Clair Lefevre, Frédéric
+ Havet, Margarida Romero, Thierry Viéville.
+
+
Dorian Mazauric - Cordées de la réussite (coordonné par Université Côte d'Azur):
+
+
Deux classes du collège Henri Nans, Aups. Les sciences du numérique à portée de mains ! Découvrir,
+ Explorer, Expérimenter ! Pirates et trésor : des maths et des algorithmes à la programmation
+ Scratch et mBot. With Frédéric Havet, Eric Pascual, Brigitte Trousse.
+
+
Dorian Mazauric - Programme Chiche:
+
+
Intervention au lycée Apollinaire, Nice Jeudi 14 octobre 2021.
+
Intervention au lycée Estienne d’Orves, Nice Jeudi 21 octobre 2021.
+
Intervention au CIV, Valbonne Sophia Antipolis Jeudi 02 décembre 2021.
+
+
Dorian Mazauric - Formations:
+
+
Formation d’enseignants co-organisée par la DANE et Terra Numerica avec les ateliers Terra Numerica à la
+ Maison de l’intelligence Artificielle. Mardi 9 mars 2021, mardi 23 mars 2021, mardi 6 avril 2021, mardi 20
+ avril 2021, mardi 25 mai 2021. Machine d’apprentissage par renforcement pour gagner aux jeux,
+ Initiation à la reconnaissance d’images avec des drones, ateliers d’informatique débranchée. With
+ Jérémy Camponovo, Frédéric Havet, Eric Pascual, Brigitte Trousse.
+
Formation de personnels de médiathèques de la CASA. Vendredi 25 juin 2021, jeudi 23 septembre 2021.
+ Formation sur les fondements de l’informatique : Transmission de pensée – La magie du
+ binaire.
+
Présentation et formation au Fab’Ecole 06 de la DRANE, collège Bertone d’Antibes. Vendredi 26 novembre
+ 2021. Présentation et formation sur des ateliers Terra Numerica. With Brigitte Trousse.
+
+
Dorian Mazauric - In schools:
+
+
Collège Bechet d’Antibes Juan-les-Pins Lundi 8 mars 2021. Dans le cadre du projet pédagogique Ethique des
+ données et de l’information (1/3). Introductions aux algorithmes. With Sylvain Etienne, Frédéric Giroire,
+ Géraldine Rouard, Brigitte Trousse.
+
Centre International de Valbonne Sophia Antipolis Lundi 15 mars 2021. Dans le cadre de séances autour de
+ l’Intelligence Artificielle avec une classe de terminale du CIV organisées par Les
+ Petits Débrouillards. Intelligence Artificielle et reconnaissance d’images. With Marie Barbieux, Marine
+ Beaudet, Soledad Tolosa.
+
Collège Bechet d’Antibes Juan-les-Pins Vendredi 26 mars 2021 et 9 avril 2021. Dans le cadre du projet
+ pédagogique Ethique des données et de l’information (2/3). Modélisation d’un réseau social et
+ de contenus, et algorithmes de recommandation. With Sylvain Etienne, Frédéric Giroire, Géraldine
+ Rouard, Brigitte Trousse.
+
Collège Bechet d’Antibes Juan-les-Pins Lundi 7 juin 2021. Dans le cadre du projet pédagogique Ethique des
+ données et de l’information (3/3). Conférence Protection des données et métier de Déléguée à
+ la Protection des Données d’Inria (Anne Combe). With Anne Combe, Sylvain Etienne, Frédéric Giroire,
+ Géraldine Rouard, Brigitte Trousse.
+
Roquefort-les-Pins Dans le cadre des activités du centre aéré de la commune. Lundi 26 juillet 2021 et
+ mardi 27 juillet 2021. Trois demi-journées : ateliers d’informatique débranchée (pour les 3 à
+ 6 ans), ateliers pour découvrir les algorithmes de recommandation dans les réseaux sociaux (pour les
+ adolescents) et tours de magie pour découvrir comment l’ordinateur compte (pour les 6 à 10 ans). With
+ Frédéric Havet.
+
Lycée Internationale de Valbonne Jeudi 02 décembre 2021. Ateliers algorithmiques grandeur
+ nature. With Bérengère Abric, Perrine Le Dûs.
+
+
Dorian Mazauric - Internships:
+
+
Treize stagiaires de troisième au centre Inria d’Université Côte d’Azur Du lundi 13 décembre au vendredi
+ 17 décembre 2021.
+
+
+
+
+
+
+
+
+
+ Distributed Link Scheduling in Wireless Networks
+
+
+ V.
+ Vishal
+ Misra
+
+
+ P.
+ Philippe
+ Nain
+
+
+
+
+ Discrete Mathematics, Algorithms and Applications
+
+ 2020
+ 12
+ 5
+ 1-38
+
+
+
+
+
+
+
+ On the complexity of the representation of simplicial complexes by trees
+
+
+ J.-D.
+ Jean-Daniel
+ Boissonnat
+
+
+
+
+ Theoretical Computer Science
+
+ February 2016
+ 617
+ 17
+
+
+
+
+
+
+ Energy landscapes and persistent minima
+
+
+ J.
+ J.
+ Carr
+
+
+ D.
+ D.
+ Mazauric
+
+
+ F.
+ F.
+ Cazals
+
+
+ D. J.
+ D. J.
+ Wales
+
+
+
+
+ The Journal of Chemical Physics
+
+ 2016
+ 144
+ 5
+ 4
+
+
+
+
+
+
+ Conformational Ensembles and Sampled Energy Landscapes: Analysis and Comparison
+
+
+ F.
+ F.
+ Cazals
+
+
+ T.
+ T.
+ Dreyfus
+
+
+ D.
+ D.
+ Mazauric
+
+
+ A.
+ A.
+ Roth
+
+
+ C.
+ C.H.
+ Robert
+
+
+
+
+ J. of Computational Chemistry
+
+ 2015
+ 36
+ 16
+ 1213--1231
+
+
+
+
+
+
+ The Structural Bioinformatics Library: modeling in biomolecular science and beyond
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ T.
+ Tom
+ Dreyfus
+
+
+
+
+
+ October 2016
+ RR-8957
+
+
+
+
+
+
+ Beyond Two-sample-tests: Localizing Data Discrepancies in High-dimensional Spaces
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+
+
+ IEEE/ACM International Conference on Data Science and Advanced Analytics
+ IEEE/ACM International Conference on Data Science and Advanced Analytics
+ Paris, France
+
+ March 2015
+ 29
+
+
+
+
+
+
+ Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+
+
+ NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems
+ Vancouver, Canada
+
+ December 2019
+
+
+
+
+
+
+
+ Comparing Two Clusterings Using Matchings between Clusters of Clusters
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ D.
+ Dorian
+ Mazauric
+
+
+ R.
+ Romain
+ Tetley
+
+
+ R.
+ Rémi
+ Watrigant
+
+
+
+
+ ACM Journal of Experimental Algorithmics
+
+ December 2019
+ 24
+ 1
+ 1-41
+
+
+
+
+
+
+
+ Complexity dichotomies for the Minimum F -Overlay problem
+
+
+
+ Journal of Discrete Algorithms
+
+ September 2018
+ 52-53
+ 133-142
+
+
+
+
+
+
+ A Sequential Non-Parametric Multivariate Two-Sample Test
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+
+
+ IEEE Transactions on Information Theory
+
+ May 2018
+ 64
+ 5
+ 3361-3370
+
+
+
+
+
+
+ High Resolution Crystal Structures Leverage Protein Binding Affinity Predictions
+
+
+ S.
+ Simon
+ Marillet
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+
+
+
+ March 2015
+ RR-8733
+
+
+
+
+
+
+
+ Novel Structural Parameters of Ig–Ag Complexes Yield a Quantitative Description of Interaction
+ Specificity and Binding Affinity
+
+
+ S.
+ Simon
+ Marillet
+
+
+ M.-P.
+ Marie-Paule
+ Lefranc
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+
+
+ Frontiers in Immunology
+
+ February 2017
+ 8
+ 34
+
+
+
+
+
+
+ Hybridizing rapidly growing random trees and basin hopping yields an improved exploration of
+ energy landscapes
+
+
+ A.
+ A.
+ Roth
+
+
+ T.
+ T.
+ Dreyfus
+
+
+ C.
+ C.H.
+ Robert
+
+
+ F.
+ F.
+ Cazals
+
+
+
+
+ J. Comp. Chem.
+
+ 2016
+ 37
+ 8
+ 739--752
+
+
+
+
+
+
+
+ Studying dynamics without explicit dynamics: A structure‐based study of the export mechanism by
+ AcrB
+
+
+ I.
+ Isabelle
+ Mus‐Veteau
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+
+
+ Proteins - Structure, Function and Bioinformatics
+
+ September 2020
+
+
+
+
+
+
+ Boosting the analysis of protein interfaces with Multiple Interface String Alignments:
+ illustration on the spikes of coronaviruses
+
+
+ S.
+ Stéphane
+ Bereux
+
+
+ B.
+ B
+ Delmas
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+
+
+ Proteins - Structure, Function and Bioinformatics
+
+ November 2021
+
+
+
+
+
+
+ Improved polytope volume calculations based on Hamiltonian Monte Carlo with boundary
+ reflections and sweet arithmetics
+
+
+ A.
+ Augustin
+ Chevallier
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+
+
+ Journal of Computational Geometry
+
+ 2022
+
+
+
+
+
+
+ Tripeptide loop closure: a detailed study of reconstructions based on Ramachandran
+ distributions
+
+
+ T.
+ T
+ O'donnell
+
+
+ C. H.
+ C H
+ Robert
+
+
+ F.
+ F
+ Cazals
+
+
+
+
+ Proteins - Structure, Function and Bioinformatics
+
+ 2022
+
+
+
+
+
+
+ Fréchet mean and <formula type="inline"><math xmlns="http://www.w3.org/1998/Math/MathML"
+ ><mi>p</mi></math></formula>-mean on the unit circle: decidability, algorithm, and applications to
+ clustering on the flat torus
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+ B.
+ B
+ Delmas
+
+
+ T.
+ Timothee
+ O'donnell
+
+
+
+
+ SEA 2021 - 19th Symposium on Experimental Algorithms
+ Sophia Antipolis, France
+
+ June 2021
+
+
+
+
+
+
+ Graph Algorithm Techniques for Networks and Computational Structural Biology
+
+
+
+
+ November 2021
+
+
+
+
+
+
+ Graph problems motivated by (low and high) resolution models of large protein
+ assemblies
+
+
+ V.-H.
+ Viet-Ha
+ Nguyen
+
+
+
+
+
+ December 2021
+
+
+
+
+
+
+ SARS-CoV-2 Through the Lens of Computational Biology:How bioinformatics is playing a key role
+ in the study of the virus and its origins
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+
+
+
+ March 2021
+ 1-35
+
+
+
+
+
+
+ On the complexity of overlaying a hypergraph with a graph with bounded maximum degree
+
+
+ F.
+ Frédéric
+ Havet
+
+
+ D.
+ Dorian
+ Mazauric
+
+
+ V.-H.
+ Viet-Ha
+ Nguyen
+
+
+
+
+
+ 2021
+
+
+
+
+
+
+
+ Crystal structure of chloroplast fructose-1,6-bisphosphate aldolase from the green alga
+ Chlamydomonas reinhardtii
+
+
+ T.
+ Théo
+ Le Moigne
+
+
+ E.
+ Edoardo
+ Sarti
+
+
+ A.
+ Antonin
+ Nourisson
+
+
+ A.
+ Alessandra
+ Carbone
+
+
+ J.
+ Julien
+ Henri
+
+
+
+
+
+ January 2022
+
+
+
+
+
+
+ Gene prioritization based on random walks with restarts and absorbing states, to define gene
+ sets regulating drug pharmacodynamics from single-cell analyses
+
+
+ A.
+ Augusto
+ Sales-De-Queiroz
+
+
+ G. G.
+ Guilherme Guilherme
+ Sales Santa Cruz
+
+
+ A.
+ Alain
+ Jean-Marie
+
+
+ D.
+ Dorian
+ Mazauric
+
+
+ F.
+ Frédéric
+ Cazals
+
+
+
+
+
+ November 2021
+
+
+
+
+
+ Molecular dynamics: survey of methods for simulating the activity of proteins
+
+
+ S.
+ S.A.
+ Adcock
+
+
+ A.
+ A.J.
+ McCammon
+
+
+
+
+ Chemical reviews
+
+ 2006
+ 106
+ 5
+ 1589--1615
+
+
+
+
+
+ The molecular architecture of the nuclear pore complex
+
+
+ F.
+ F.
+ Alber
+
+
+ S.
+ S.
+ Dokudovskaya
+
+
+ L.
+ L.M.
+ Veenhoff
+
+
+ W.
+ W.
+ Zhang
+
+
+ J.
+ J.
+ Kipper
+
+
+ D.
+ D.
+ Devos
+
+
+ A.
+ A.
+ Suprapto
+
+
+ O.
+ O.
+ Karni-Schmidt
+
+
+ R.
+ R.
+ Williams
+
+
+ B.
+ B.T.
+ Chait
+
+
+ A.
+ A.
+ Sali
+
+
+ M.
+ M.P.
+ Rout
+
+
+
+
+ Nature
+
+ 2007
+ 450
+ 7170
+ 695--701
+
+
+
+
+
+ Dynamics on statistical samples of potential energy surfaces
+
+
+ K.
+ K.D.
+ Ball
+
+
+ R.
+ R.S.
+ Berry
+
+
+
+
+ The Journal of chemical physics
+
+ 1999
+ 111
+ 5
+ 2060--2070
+
+
+
+
+
+ Thermodynamics and an Introduction to Thermostatistics
+
+
+ H.
+ H.B.
+ Callen
+
+
+
+
+
+ 1985
+ Wiley
+
+
+
+
+
+ De novo design of picomolar SARS-CoV-2 miniprotein inhibitors
+
+
+ L.
+ L.
+ Cao
+
+
+ I.
+ I.
+ Goreshnik
+
+
+ B.
+ B.
+ Coventry
+
+
+ J.
+ J.B.
+ Case
+
+
+ L.
+ L.
+ Miller
+
+
+ L.
+ L.
+ Kozodoy
+
+
+ R.
+ R.
+ Chen
+
+
+ L.
+ L.
+ Carter
+
+
+ A.
+ A.
+ Walls
+
+
+ Y.-J.
+ Y-J.
+ Park
+
+
+ E.-M.
+ E-M
+ Strauch
+
+
+ L.
+ L.
+ Stewart
+
+
+ M.
+ M.S.
+ Diamond
+
+
+ D.
+ D.
+ Veesler
+
+
+ D.
+ D.
+ Baker
+
+
+
+
+ Science
+
+ 2020
+ 370
+ 6515
+ 426--431
+
+
+
+
+
+
+ Energy landscapes and persistent minima
+
+
+ J.
+ J.
+ Carr
+
+
+ D.
+ D.
+ Mazauric
+
+
+ F.
+ F.
+ Cazals
+
+
+ D. J.
+ D. J.
+ Wales
+
+
+
+
+ The Journal of Chemical Physics
+
+ 2016
+ 144
+ 5
+ 4
+
+
+
+
+
+ A practical volume algorithm
+
+
+ B.
+ B.
+ Cousins
+
+
+ S.
+ S.
+ Vempala
+
+
+
+
+ Mathematical Programming Computation
+
+ 2016
+ 8
+ 2
+ 133--160
+
+
+
+
+
+ Understanding molecular simulation
+
+
+ D.
+ D.
+ Frenkel
+
+
+ B.
+ B.
+ Smit
+
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+
+
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+ Academic Press
+
+
+
+
+
+ Random walks and an <formula type="inline"><math xmlns="http://www.w3.org/1998/Math/MathML"
+ ><mrow><msup><mi>O</mi>
+ <mo>*</mo>
+ </msup><mrow><mo>(</mo></mrow><msup><mi>n</mi>
+ <mn>5</mn>
+ </msup></mrow></math></formula>) volume algorithm for convex bodies
+
+
+ R.
+ R.
+ Kannan
+
+
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+
+
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+ M.
+ Simonovits
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+ A guide to Monte Carlo simulations in statistical physics
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+
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+ Landau
+
+
+ K.
+ K.
+ Binder
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+
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+ Cambridge university press
+
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+ Free energy computations: A mathematical perspective
+
+
+ T.
+ T.
+ Lelièvre
+
+
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+ Stoltz
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+ M.
+ M.
+ Rousset
+
+
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+
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+ World Scientific
+
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+ Prediction, determination and validation of phase diagrams via the global study of energy
+ landscapes
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+ C.
+ Schön
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+
+ M.
+ M.
+ Jansen
+
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+ Int. J. of Materials Research
+
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+ 2
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+ Improved protein structure prediction using potentials from deep learning
+
+
+ A.
+ A.
+ Senior
+
+
+ R.
+ R.
+ Evans
+
+
+ J.
+ J.
+ Jumper
+
+
+ J.
+ J.
+ Kirkpatrick
+
+
+ L.
+ L.
+ Sifre
+
+
+ T.
+ T.
+ Green
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+ C.
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+ Qin
+
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+ A.
+ A.
+ Żídek
+
+
+ A.
+ A.
+ Nelson
+
+
+ A.
+ A.
+ Bridgland
+
+
+ H.
+ H.
+ Penedones
+
+
+ S.
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+ Petersen
+
+
+ K.
+ K.
+ Simonyan
+
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+ S.
+ S.
+ Crossan
+
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+ K.
+ K.
+ Pushmeet
+
+
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+ Jones
+
+
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+ Silver
+
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+ K.
+ K.
+ Kavukcuoglu
+
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+ Hassabis
+
+
+
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+
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+ 1--5
+
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+
+
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+ Atomic-level characterization of the structural dynamics of proteins.
+
+
+ D. E.
+ D. E.
+ Shaw
+
+
+ P.
+ P.
+ Maragakis
+
+
+ K.
+ K.
+ Lindorff-Larsen
+
+
+ S.
+ S.
+ Piana
+
+
+ R. O.
+ R. O.
+ Dror
+
+
+ M. P.
+ M. P.
+ Eastwood
+
+
+ J. A.
+ J. A.
+ Bank
+
+
+ J. M.
+ J. M.
+ Jumper
+
+
+ J. K.
+ J. K.
+ Salmon
+
+
+ Y.
+ Y.
+ Shan
+
+
+ W.
+ W.
+ Wriggers
+
+
+
+
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+
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+ 330
+ 6002
+ 341--346
+
+
+
+
+
+ Energy Landscapes
+
+
+ D. J.
+ D. J.
+ Wales
+
+
+
+
+
+ 2003
+ Cambridge University Press
+
+
+
+
+
+ Building force fields: an automatic, systematic, and reproducible approach
+
+
+ L.-P.
+ Lee-Ping
+ Wang
+
+
+ T. J.
+ Todd J
+ Martinez
+
+
+ V. S.
+ Vijay S
+ Pande
+
+
+
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+ The journal of physical chemistry letters
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+
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+++ b/RADAR2TEI/XSLT/RADAR2TEI.xsl
@@ -0,0 +1,369 @@
+
+
+
+
+
+
+
+ href="../schema-TEI-RADAR/out/TEI_RADAR.rng" type="application/xml"
+ schematypens="http://relaxng.org/ns/structure/1.0"
+ href="../schema-TEI-RADAR/out/TEI_RADAR.rng" type="application/xml"
+ schematypens="http://purl.oclc.org/dsdl/schematron"
+
+
+
+
+
+
+
+
+ Title
+
+
+