2025Activity‌ reportProject-TeamABS

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the one hand, genome​​​‌ sequencing projects give supply​ protein sequences, and ~200​‌ millions of sequences have​​ been archived in UniProtKB​​​‌/TrEMBL – which​ collects the protein sequences​‌ yielded by genome sequencing​​ projects.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 $\sim {10}^{\mathrm{8‌}}$ 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​​ $n$ atoms has $\mathrm{d‌}=3n$ 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 $d=‌3n$ 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 $\gg {\mathrm{10‌‌}}^{12}$ conformations/frames, a (brute)​​ tour de force rarely​​​‌ achieved 38.

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 37,​​ 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.‌​‌

The spike of SARS-CoV-2​​ is responsible for the​​​‌ infection. Molecules can be‌ engineered to compete with‌​‌ the receptor of the​​ spike, and block infection.​​​‌

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 26​​​‌. 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. 1​​​‌). 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. 1(A), 29​​). 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. 1​‌(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 29,​​ 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 29.​ 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.

Prosaically,​​​‌ the energy landscape of​ a molecular system is​‌ the set of valleys​​ and passes connecting them,​​​‌ corresponding to stable states​ and transitions between them.​‌

Protein​​ dynamics: core CS -​​​‌ maths challenges.

To present​ challenges in structural modeling,​‌ let us recall the​​ following ingredients (Fig. 2​​​‌). First, a molecular​ model with $n$ atoms​‌ is parameterized over a​​ conformational space $𝒳$ of​​​‌ dimension $d=\mathrm{3}n$ in Cartesian coordinates,​‌ or $d=\mathrm{3}n-6$ 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​‌ $V(·)$ from ${ℝ}^d$ to​​​‌ $ℝ$ providing a potential​ energy for each conformation​‌ 39, 36.​​ 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 $\sim 1,000$),​​ fitted to reproduce physico-chemical​​​‌ properties of (bio-)molecules 40‌.

These PE are‌​‌ usually considered good enough​​ to study non covalent​​​‌ interactions – our focus,‌ even though they do‌​‌ not cover the modification​​ of chemical bonds. In​​​‌ any case, we take‌ such a function for‌​‌ granted 1.

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​‌ 38, 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​‌ 32. 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 27;‌​‌ fortunately, they enjoy a​​ multi-scale structure 30.​​​‌ 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​​​‌ $\sim$ 15 and $\sim ‌$ 4 orders of magnitude‌​‌ in time and amplitude​​ respectively 25. 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 $K_{\mathrm{o‌}n}$ and $K_{\mathrm{o}ff}$), or​​​‌ fluorescence based methods (for​ kinetics of folding).

3.1​ 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 39,​​​‌ 36 (Fig. 2)​ 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 4​‌, 16, 3​​. 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 28​​35. 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 34​‌–an adaptive Monte Carlo​​ Markov Chain (MCMC) algorithm,​​​‌ as well as a​ generalization of multi-phase Monte​‌ Carlo methods for convex/polytope​​ volume calculations 33,​​​‌ 31, for non​ convex strata of PELs.​‌

3.2 Algorithmic foundations: geometry,​​ optimization, machine learning

Keywords:​​​‌ Geometry, optimization, machine learning,​ randomized algorithms, sampling, optimization.​‌

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 7​​, statistical tests /​​​‌ two-sample tests 8,​ 13, the comparison​‌ of clustering 9,​​ the complexity study of​​​‌ graph inference problems for​ low-resolution reconstruction of assemblies​‌ 12, the analysis​​ of partition (or clustering)​​​‌ stability in large networks,​ the complexity of the​‌ representation of simplicial complexes​​ 2. 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.‌​‌

3.3 Software: the Structural​​ Bioinformatics Library

Keywords: Scientific​​​‌ software, generic programming, molecular‌ modeling.

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.

3.4‌​‌ 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.​‌ Examples of 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 17).​​​‌ 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​‌ 14, 15.​​ 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.

5.1 Footprint​ of research activities

A​‌ tenet of ABS is​​ to carefully analyze the​​​‌ performances of the algorithms​ designed–either formally or experimentally,​‌ so as to avoid​​ massive calculations. Therefore, the​​​‌ footprint of our research​ activities has remained limited.​‌

5.2 Impact of research​​ results

The scientific agenda​​​‌ of ABS is geared​ towards a fine understanding​‌ of complex phenomena at​​ the atomic/molecular level. While​​​‌ the current focus is​ rather fundamental, as explained​‌ in Research program,​​ an overarching goal for​​​‌ the current period (i.e.​ 12 years) is to​‌ make significant contributions to​​ important problems in biology​​​‌ and medicine.

Science.​​​‌

Regarding the algorithmic foundations​ of our activities, we​‌ have made significant progress​​ on various clustering procedures​​​‌ which are needed to​ model molecular conformations in​‌ high dimensional spaces. In​​ particular, our improvement of​​​‌ the k-means seeding procedure​ 18 is currently being​‌ used to define complex​​ mixtures of periodic functions​​​‌ to model joint probabilities​ of torsion angles in​‌ proteins.

On the applied​​ side, we developed more​​ thorough and rigorous analysis​​​‌ of structure predictions made‌ by the Nobel prize‌​‌ winning program AlphaFold, see​​ 23 and 22.​​​‌ We hope some of‌ these analysis will become‌​‌ standard and made available​​ on portal such as​​​‌ the AlphaFold Protein Structure‌ Database.

Software.

We‌​‌ have continued our efforts​​ on the development of​​​‌ the Structural Bioinformatics Library,‌ adding no less than‌​‌ eight new packages –​​ see updates about the​​​‌ Bioinformatics Library.

Most importantly,‌ the Plugin_manager package was‌​‌ developed in the context​​ of the PIQ project​​​‌ (Programme InriaQuadrant) Eminence headed‌ by F. Cazals, which‌​‌ stared in June 2025.​​ For each application package,​​​‌ this specific package eases‌ the development of plugins‌​‌ for various target platforms,​​ including VMD, PyMol​​​‌, and web servers.‌ A vast campaign advertising‌​‌ plugins for six applications​​ will be orchestrated early​​​‌ 2026, both at the‌ national and international levels.‌​‌

Teaching.

Attracting bright students​​ is a notoriously difficult​​​‌ challenge. In 2025, F.‌ Cazals started the class‌​‌ Algorithms and learning for​​ protein science, within​​​‌ the Master Mathématiques, Vision,‌ Apprentissage, ENS Paris-Saclay. This‌​‌ first season involved 25​​ students.

7.1 Latest software developments‌​‌

8.1 Modeling the​​ dynamics of proteins

Keywords:​​​‌ Protein flexibility, protein conformations,​ collective coordinates, conformational sampling,​‌ loop closure, kinematics, dimensionality​​ reduction.

8.2 Algorithmic foundations

Keywords:​‌ Computational geometry, computational topology,​​ optimization, graph theory, data​​​‌ analysis, statistical physics.

8.3 Applications in structural​​ bioinformatics and beyond

Keywords:​​​‌ Docking, scoring, interfaces, protein‌ complexes, phylogeny, evolution.

9.1 Bilateral​​ contracts with industry

Since​​​‌ Septembre 2025, Frédéric Cazals‌ co-supervises the CIFRE PhD‌​‌ project of Antoine Hauser,​​ in collaboration with the​​​‌ Vect-Horus company. The goal‌ of this project is‌​‌ to design new molecules​​ crossing the blood-brain barrier,​​​‌ a notoriously difficult challenge‌ to address the brain–i.e.‌​‌ to send medicines into​​ the brain or molecules​​​‌ used for imaging purposes.‌

10.1​​​‌ National initiatives

10.2 Regional​ initiatives

11.1 Promoting scientific​‌ activities

11.2 Teaching - Supervision​​ - Juries - Educational​​​‌ and pedagogical outreach

• 2024–...:​ Master Mathématiques, Vision, Apprentissage,​‌ ENS Paris-Saclay, Algorithms and​​ learning for protein science​​​‌; F. Cazals (24h).​ See Official web site​‌ and Lecture notes.​​
• 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: GTML.

12.1 Major publications

• 1‌ articleJ.-C.Jean-Claude Bermond‌​‌, D.Dorian Mazauric​​, V.Vishal Misra​​​‌ and P.Philippe Nain‌. Distributed Link Scheduling‌​‌ in Wireless Networks.​​Discrete Mathematics, Algorithms and​​​‌ Applications1252020‌, 1-38HALDOI‌​‌
• 2 articleJ.-D.Jean-Daniel​​ Boissonnat and D.Dorian​​​‌ Mazauric. On the‌ complexity of the representation‌​‌ of simplicial complexes by​​ trees.Theoretical Computer​​​‌ Science617February 2016‌, 17HALDOI‌​‌back to text
• 3​​ articleJ.J. Carr​​​‌, D.D. Mazauric‌, F.F. Cazals‌​‌ and D. J.D.​​ J. Wales. Energy​​​‌ landscapes and persistent minima‌.The Journal of‌​‌ Chemical Physics1445​​2016, 4URL:​​​‌ https://www.repository.cam.ac.uk/handle/1810/253412DOIback to‌ text
• 4 articleF.‌​‌F. Cazals, T.​​T. Dreyfus, D.​​​‌D. Mazauric, A.‌A. Roth and C.‌​‌C.H. Robert. Conformational​​ Ensembles and Sampled Energy​​​‌ Landscapes: Analysis and Comparison‌.J. of Computational‌​‌ Chemistry36162015​​, 1213--1231URL: https://hal.archives-ouvertes.fr/hal-01076317​​​‌DOIback to text‌
• 5 articleF.Frédéric‌​‌ Cazals and T.Tom​​ Dreyfus. The Structural​​​‌ Bioinformatics Library: modeling in‌ biomolecular science and beyond‌​‌.Bioinformatics338​​April 2017HALDOI​​​‌back to text
• 6‌ reportF.Frédéric Cazals‌​‌ and T.Tom Dreyfus​​. The Structural Bioinformatics​​​‌ Library: modeling in biomolecular‌ science and beyond.‌​‌RR-8957InriaOctober 2016​​HAL
• 7 inproceedingsF.​​​‌Frédéric Cazals and A.‌Alix Lhéritier. Beyond‌​‌ Two-sample-tests: Localizing Data Discrepancies​​​‌ in High-dimensional Spaces.​IEEE/ACM International Conference on​‌ Data Science and Advanced​​ AnalyticsIEEE/ACM International Conference​​​‌ on Data Science and​ Advanced AnalyticsIEEE/ACM International​‌ Conference on Data Science​​ and Advanced AnalyticsParis,​​​‌ FranceMarch 2015,​ 29HALback to​‌ text
• 8 inproceedingsF.​​Frédéric Cazals and A.​​​‌Alix Lhéritier. Low-Complexity​ Nonparametric Bayesian Online Prediction​‌ with Universal Guarantees.​​NeurIPS 2019 - Thirty-third​​​‌ Conference on Neural Information​ Processing SystemsVancouver, Canada​‌December 2019HALback​​ to text
• 9 article​​​‌F.Frédéric Cazals,​ D.Dorian Mazauric,​‌ R.Romain Tetley and​​ R.Rémi Watrigant.​​​‌ Comparing Two Clusterings Using​ Matchings between Clusters of​‌ Clusters.ACM Journal​​ of Experimental Algorithmics24​​​‌1December 2019,​ 1-41HALDOIback​‌ to text
• 10 inproceedings​​A.Augustin Chevallier,​​​‌ F.Frédéric Cazals and​ P.Paul Fearnhead.​‌ Efficient computation of the​​ volume of a polytope​​​‌ in high-dimensions using Piecewise​ Deterministic Markov Processes.​‌AISTATS 2022 - 25th​​ International Conference on Artificial​​​‌ Intelligence and StatisticsVirtual,​ FranceMarch 2022HAL​‌
• 11 articleA.Augustin​​ Chevallier and F.Frédéric​​​‌ Cazals. Wang-Landau Algorithm:​ an adapted random walk​‌ to boost convergence.​​Journal of Computational Physics​​​‌4102020, 109366​HALDOI
• 12 article​‌N.Nathann Cohen,​​ F.Frédéric Havet,​​​‌ D.Dorian Mazauric,​ I.Ignasi Sau Valls​‌ and R.Rémi Watrigant​​. Complexity dichotomies for​​​‌ the Minimum F -Overlay​ problem.Journal of​‌ Discrete Algorithms52-53September​​ 2018, 133-142HAL​​​‌DOIback to text​
• 13 articleA.Alix​‌ Lhéritier and F.Frédéric​​ Cazals. A Sequential​​​‌ Non-Parametric Multivariate Two-Sample Test​.IEEE Transactions on​‌ Information Theory645​​May 2018, 3361-3370​​​‌HALback to text​
• 14 reportS.Simon​‌ Marillet, P.Pierre​​ Boudinot and F.Frédéric​​​‌ Cazals. High Resolution​ Crystal Structures Leverage Protein​‌ Binding Affinity Predictions.​​RR-8733InriaMarch 2015​​​‌HALback to text​
• 15 articleS.Simon​‌ Marillet, M.-P.Marie-Paule​​ Lefranc, P.Pierre​​​‌ Boudinot and F.Frédéric​ Cazals. Novel Structural​‌ Parameters of Ig–Ag Complexes​​ Yield a Quantitative Description​​​‌ of Interaction Specificity and​ Binding Affinity.Frontiers​‌ in Immunology8February​​ 2017, 34HAL​​​‌DOIback to text​
• 16 articleA.A.​‌ Roth, T.T.​​ Dreyfus, C.C.H.​​​‌ Robert and F.F.​ Cazals. Hybridizing rapidly​‌ growing random trees and​​ basin hopping yields an​​​‌ improved exploration of energy​ landscapes.J. Comp.​‌ Chem.3782016​​, 739--752URL: https://hal.inria.fr/hal-01191028​​​‌DOIback to text​
• 17 articleM.Méliné​‌ Simsir, I.Isabelle​​ Broutin, I.Isabelle​​​‌ Mus‐Veteau and F.Frédéric​ Cazals. Studying dynamics​‌ without explicit dynamics: A​​ structure‐based study of the​​​‌ export mechanism by AcrB​.Proteins - Structure,​‌ Function and BioinformaticsSeptember​​ 2020HALDOIback​​​‌ to text

12.2 Publications​ of the year

12.3 Cited‌​‌ publications

• 25 articleS.​​S.A. Adcock and A.​​​‌A.J. McCammon. Molecular‌ dynamics: survey of methods‌​‌ for simulating the activity​​ of proteins.Chemical​​​‌ reviews10652006‌, 1589--1615back to‌​‌ text
• 26 articleF.​​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 and M.‌​‌M.P. Rout. The​​ molecular architecture of the​​​‌ nuclear pore complex.‌Nature45071702007‌​‌, 695--701back to​​ text
• 27 articleK.​​​‌K.D. Ball and R.‌R.S. Berry. Dynamics‌​‌ on statistical samples of​​ potential energy surfaces.​​​‌The Journal of chemical‌ physics11151999‌​‌, 2060--2070back to​​ text
• 28 bookH.​​​‌H.B. Callen. Thermodynamics‌ and an Introduction to‌​‌ Thermostatistics.Wiley1985​​back to text
• 29​​​‌ articleL.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​‌ and D.D. Baker​​. De novo design​​​‌ of picomolar SARS-CoV-2 miniprotein‌ inhibitors.Science370‌​‌65152020, 426--431​​back to textback​​​‌ to textback to‌ textback to text‌​‌
• 30 articleJ.J.​​​‌ Carr, D.D.​ Mazauric, F.F.​‌ Cazals and D. J.​​D. J. Wales.​​​‌ Energy landscapes and persistent​ minima.The Journal​‌ of Chemical Physics144​​52016, 4​​​‌URL: https://www.repository.cam.ac.uk/handle/1810/253412DOIback​ to text
• 31 article​‌B.B. Cousins and​​ S.S. Vempala.​​​‌ A practical volume algorithm​.Mathematical Programming Computation​‌822016,​​ 133--160back to text​​​‌
• 32 bookD.D.​ Frenkel and B.B.​‌ Smit. Understanding molecular​​ simulation.Academic Press​​​‌2002back to text​
• 33 articleR.R.​‌ Kannan, L.L.​​ Lovász and M.M.​​​‌ Simonovits. Random walks​ and an $O^{*‌}(n^5$)​​ volume algorithm for convex​​​‌ bodies.Random Structures​ & Algorithms111​‌1997, 1--50back​​ to text
• 34 book​​​‌D.D. Landau and​ K.K. Binder.​‌ A guide to Monte​​ Carlo simulations in statistical​​​‌ physics.Cambridge university​ press2014back to​‌ text
• 35 bookT.​​T. Lelièvre, G.​​​‌G. Stoltz and M.​M. Rousset. Free​‌ energy computations: A mathematical​​ perspective.World Scientific​​​‌2010back to text​
• 36 articleC.C.​‌ Schön and M.M.​​ Jansen. Prediction, determination​​​‌ and validation of phase​ diagrams via the global​‌ study of energy landscapes​​.Int. J. of​​​‌ Materials Research1002​2009, 135back​‌ to textback to​​ text
• 37 articleA.​​​‌A. Senior, R.​R. Evans, J.​‌J. Jumper, J.​​J. Kirkpatrick, L.​​​‌L. Sifre, T.​T. Green, C.​‌C. Qin, A.​​A. Żídek, A.​​​‌A. Nelson, A.​A. Bridgland, H.​‌H. Penedones, S.​​S. Petersen, K.​​​‌K. Simonyan, S.​S. Crossan, K.​‌K. Pushmeet, D.​​D. Jones, D.​​​‌D. Silver, K.​K. Kavukcuoglu and D.​‌D. Hassabis. Improved​​ protein structure prediction using​​​‌ potentials from deep learning​.Nature2020,​‌ 1--5back to text​​
• 38 articleD. 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 and W.W.​ Wriggers. Atomic-level characterization​‌ of the structural dynamics​​ of proteins..Science​​​‌33060022010,​ 341--346URL: http://dx.doi.org/10.1126/science.1187409back​‌ to textback to​​ text
• 39 bookD.​​​‌ J.D. J. Wales​. Energy Landscapes.​‌Cambridge University Press2003​​back to textback​​​‌ to text
• 40 article​L.-P.Lee-Ping Wang,​‌ T. J.Todd J​​ Martinez and V. S.​​​‌Vijay S Pande.​ Building force fields: an​‌ automatic, systematic, and reproducible​​ approach.The journal​​​‌ of physical chemistry letters​5112014,​‌ 1885--1891back to text​​