T. Kortemme and D. Baker, Computational design of protein???protein interactions, Current Opinion in Chemical Biology, vol.8, issue.1, pp.91-97, 2004.
DOI : 10.1016/j.cbpa.2003.12.008

I. Georgiev, H. Ryan, . Lilien, R. Bruce, and . Donald, Improved Pruning algorithms and Divide-and-Conquer strategies for Dead-End Elimination, with application to protein design, Bioinformatics, vol.22, issue.14, pp.22-174, 2006.
DOI : 10.1093/bioinformatics/btl220

A. Zanghellini, L. Jiang, M. Andrew, G. Wollacott, J. Cheng et al., New algorithms and an in silico benchmark for computational enzyme design, Protein Science, vol.14, issue.12, pp.2785-2794, 2006.
DOI : 10.1007/978-1-4615-4022-9

S. Traoré, D. Allouche, I. André, G. Simon-de-givry, T. Katsirelos et al., A new framework for computational protein design through cost function network optimization, Bioinformatics, vol.13, issue.17, pp.292129-2136, 2013.
DOI : 10.1007/s10601-007-9033-9

D. Allouche, I. André, S. Barbe, J. Davies, G. Simon-de-givry et al., Computational protein design as an optimization problem, Thomas Schiex, and Seydou Traoré, pp.59-79, 2014.
DOI : 10.1016/j.artint.2014.03.005
URL : https://hal.archives-ouvertes.fr/hal-01268554

D. Simoncini, D. Allouche, C. Simon-de-givry, S. Delmas, T. Barbe et al., Guaranteed Discrete Energy Optimization on Large Protein Design Problems, Journal of Chemical Theory and Computation, vol.11, issue.12, pp.5980-5989, 2015.
DOI : 10.1021/acs.jctc.5b00594

S. Traoré, E. Kyle, D. Roberts, . Allouche, R. Bruce et al., Fast search algorithms for computational protein design, Journal of Computational Chemistry, vol.35, issue.12, pp.1048-1058, 2016.
DOI : 10.1002/(SICI)1097-0134(19990501)35:2<133::AID-PROT1>3.0.CO;2-N

C. Viricel, D. Simoncini, D. Allouche, S. Simon-de-givry, T. Barbe et al., Approximate Counting with Deterministic Guarantees for Affinity Computation, Modelling, Computation and Optimization in Information Systems and Management Sciences, pp.165-176, 2015.
DOI : 10.1007/978-3-319-18167-7_15
URL : https://hal.archives-ouvertes.fr/hal-01269259

C. Viricel, D. Simoncini, S. Barbe, and T. Schiex, Guaranteed Weighted Counting for Affinity Computation: Beyond Determinism and Structure, International Conference on Principles and Practice of Constraint Programming, pp.733-750, 2016.
DOI : 10.1109/TIT.2005.850091
URL : https://hal.archives-ouvertes.fr/hal-01606791

D. Allouche, S. De-givry, G. Katsirelos, T. Schiex, and M. Zytnicki, Anytime Hybrid Best-First Search with Tree Decomposition for Weighted CSP, International Conference on Principles and Practice of Constraint Programming, pp.12-29, 2015.
DOI : 10.1007/978-3-319-23219-5_2
URL : https://hal.archives-ouvertes.fr/hal-01198361

K. Murphy, An introduction to graphical models, Rap. tech, pp.1-19, 2001.

D. Koller and N. Friedman, Probabilistic graphical models : principles and techniques, 2009.

R. Dechter, Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.4, issue.2, pp.1-191, 2013.
DOI : 10.1145/1831407.1831430

M. Christopher and . Bishop, Pattern recognition, Machine Learning, pp.1-58, 2006.

J. Brendan and . Frey, Extending factor graphs so as to unify directed and undirected graphical models, Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence, pp.257-264, 2002.

R. Kindermann and J. Snell, Markov random fields and their applications, 1980.
DOI : 10.1090/conm/001

Z. Stan and . Li, Markov random field modeling in image analysis, 2009.

P. Kevin and . Murphy, Machine learning : a probabilistic perspective, 2012.

S. Bistarelli, H. Fargier, U. Montanari, F. Rossi, T. Schiex et al., Semiring-based CSPs and valued CSPs : Frameworks, properties and comparison, Constraints, vol.4, issue.3, pp.199-240, 1999.
DOI : 10.1023/A:1026441215081

M. C. Cooper, Reduction operations in fuzzy or valued constraint satisfaction. Fuzzy Sets and Systems, pp.311-342, 2003.

J. Simon-de-givry, P. Larrosa, T. Meseguer, and . Schiex, Solving Max-SAT as Weighted CSP, Proc. of the Ninth International Conference on Principles and Practice of Constraint Programming, 2003.
DOI : 10.1007/978-3-540-45193-8_25

M. Cooper, S. De-givry, M. Sanchez, T. Schiex, M. Zytnicki et al., Soft arc consistency revisited, Artificial Intelligence, vol.174, issue.7-8, pp.449-478, 2010.
DOI : 10.1016/j.artint.2010.02.001

A. Stephen and . Cook, The complexity of theorem-proving procedures, Proceedings of the third annual ACM symposium on Theory of computing, pp.151-158, 1971.

A. Ingo, H. J. Beinlich, M. Suermondt, . Chavez, F. Gregory et al., The alarm monitoring system : A case study with two probabilistic inference techniques for belief networks, AIME 89, pp.247-256, 1989.

J. Simon and . Prince, Computer vision : models, learning, and inference, 2012.

C. Wang, N. Komodakis, and N. Paragios, Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey, Computer Vision and Image Understanding, vol.117, issue.11, pp.1610-1627, 2013.
DOI : 10.1016/j.cviu.2013.07.004
URL : https://hal.archives-ouvertes.fr/hal-00858390

M. Ghallab, D. Nau, and P. Traverso, Automated Planning : theory and practice, 2004.

G. Leslie and . Valiant, The complexity of computing the permanent. Theoretical computer science, pp.189-201, 1979.

C. Lecoutre, S. Saïs, V. Tabary, and . Vidal, Reasoning from last conflict(s) in constraint programming, Artificial Intelligence, vol.173, issue.18, p.15921614, 2009.
DOI : 10.1016/j.artint.2009.09.002
URL : https://hal.archives-ouvertes.fr/hal-00868108

F. Boussemart, F. Hemery, C. Lecoutre, and L. Sais, Boosting systematic search by weighting constraints, ECAI, p.146, 2004.

J. Pearl, Heuristics : intelligent search strategies for computer problem solving, 1984.

E. Peter, . Hart, J. Nils, B. Nilsson, and . Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE transactions on Systems Science and Cybernetics, vol.4, issue.2, pp.100-107, 1968.

J. Larrosa and T. Schiex, In the quest of the best form of local consistency for weighted CSP, Proc. of the 18 th IJCAI, pp.239-244, 2003.

M. C. Cooper and T. Schiex, Arc consistency for soft constraints, Artificial Intelligence, vol.154, issue.1-2, pp.199-227, 2004.
DOI : 10.1016/j.artint.2003.09.002

J. Larrosa, S. De-givry, F. Heras, and M. Zytnicki, Existential arc consistency : getting closer to full arc consistency in weighted CSPs, Proc. of the 19 th IJCAI, pp.84-89, 2005.

C. Martin, . Cooper, M. Simon-de-givry, T. Sanchez, M. Schiex et al., Virtual arc consistency for weighted csp, AAAI, pp.253-258, 2008.

C. Lecoutre, N. Paris, O. Roussel, and S. Tabary, Propagating Soft Table Constraints, Principles and Practice of Constraint Programming, pp.390-405, 2012.
DOI : 10.1007/978-3-642-33558-7_30
URL : https://hal.archives-ouvertes.fr/hal-00865618

H. Nguyen, T. Schiex, and C. Bessiere, Dynamic virtual arc consistency, Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC '13, pp.98-103, 2013.
DOI : 10.1145/2480362.2480384
URL : https://hal.archives-ouvertes.fr/lirmm-00830307

J. Larrosa, On arc and node consistency in weighted CSP, Proc. AAAI'02, pp.48-53, 2002.

N. Robertson, D. Paul, and . Seymour, Graph minors. III. Planar tree-width, Journal of Combinatorial Theory, Series B, vol.36, issue.1, pp.49-64, 1984.
DOI : 10.1016/0095-8956(84)90013-3
URL : https://doi.org/10.1006/jctb.1996.0059

U. Bertelé and F. Brioshi, Nonserial Dynamic Programming, 1972.

R. Marinescu, R. Dechter, and A. T. Ihler, And/or search for marginal map, UAI, pp.563-572, 2014.

T. Simon-de-givry, G. Schiex, and . Verfaillie, Exploiting tree decomposition and soft local consistency in weighted csp, AAAI, pp.1-6, 2006.

N. Peyrard, S. De-givry, A. Franc, S. Robin, R. Sabbadin et al., Exact and approximate inference in graphical models : variable elimination and beyond. arXiv preprint, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01197655

C. Terrioux and P. Jegou, Bounded Backtracking for the Valued Constraint Satisfaction Problems, Proc. of the Ninth International Conference on Principles and Practice of Constraint Programming, 2003.
DOI : 10.1007/978-3-540-45193-8_48

S. De-givry, T. Schiex, and G. Verfaillie, Exploiting Tree Decomposition and Soft Local Consistency in Weighted CSP, Proc. of the National Conference on Artificial Intelligence, AAAI-2006, pp.22-27, 2006.

S. Toda, On the computational power of PP and (+)P, 30th Annual Symposium on Foundations of Computer Science, pp.514-519, 1989.
DOI : 10.1109/SFCS.1989.63527

R. Frank, . Kschischang, J. Brendan, H. Frey, and . Loeliger, Factor graphs and the sum-product algorithm, IEEE Transactions on information theory, vol.47, issue.2, pp.498-519, 2001.

J. Martin, . Wainwright, S. Tommi, A. S. Jaakkola, and . Willsky, Treereweighted belief propagation algorithms and approximate ml estimation by pseudo-moment matching, In Workshop on Artificial Intelligence and Statistics Society for Artificial Intelligence and Statistics Np, vol.21, p.97, 2003.

T. Hazan, S. Maji, and T. Jaakkola, On sampling from the Gibbs distribution with random maximum a-posteriori perturbations, Advances in Neural Information Processing Systems, pp.1268-1276, 2013.

S. Ermon, C. P. Gomes, A. Sabharwal, and B. Selman, Taming the curse of dimensionality : Discrete integration by hashing and optimization . arXiv preprint arXiv :1302, 2013.

S. Chakraborty, J. Daniel, . Fremont, S. Kuldeep, . Meel et al., Distribution-aware sampling and weighted model counting for sat, 2014.

M. Thurley, sharpSAT ??? Counting Models with Advanced Component Caching and Implicit BCP, International Conference on Theory and Applications of Satisfiability Testing, pp.424-429, 2006.
DOI : 10.1007/11814948_38

T. Sang, P. Beame, and H. Kautz, Heuristics for Fast Exact Model Counting, International Conference on Theory and Applications of Satisfiability Testing, pp.226-240, 2005.
DOI : 10.1007/11499107_17

R. Dechter, Bucket elimination: A unifying framework for reasoning, Artificial Intelligence, vol.113, issue.1-2, pp.41-85, 1999.
DOI : 10.1016/S0004-3702(99)00059-4

M. Chavira and A. Darwiche, On probabilistic inference by weighted model counting, Artificial Intelligence, vol.172, issue.6-7, pp.772-799, 2008.
DOI : 10.1016/j.artint.2007.11.002

U. Oztok and A. Darwiche, A top-down compiler for sentential decision diagrams, Proceedings of the 24th International Conference on Artificial Intelligence, 2015.

T. Sang, P. Beame, and H. Kautz, Solving bayesian networks by weighted model counting, Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), pp.475-482, 2005.

G. Shafer, An axiomatic study of computation in hypertrees. Working paper 232, School of Business, 1991.

R. Kaivola, R. Ghughal, N. Narasimhan, A. Telfer, J. Whittemore et al., Replacing Testing with Formal Verification in Intel $^{\scriptsize\circledR}$ CoreTM i7 Processor Execution Engine Validation, CAV, pp.414-429, 2009.
DOI : 10.1109/FMCAD.2008.ECP.12

A. Darwiche, M. W. Moskewicz, C. F. Madigan, Y. Zhao, L. Zhang et al., Chaff : Engineering an efficient sat solver, 38 th Design Automation Conference (DAC'01), pp.409-420, 2001.

S. Yogesh, Z. Mahajan, S. Fu, and . Malik, Zchaff2004 : An efficient sat solver, International Conference on Theory and Applications of Satisfiability Testing, pp.360-375, 2004.

P. Beame, R. Impagliazzo, T. Pitassi, and N. Segerlind, Memoization and DPLL: formula caching proof systems, 18th IEEE Annual Conference on Computational Complexity, 2003. Proceedings., pp.248-259, 2003.
DOI : 10.1109/CCC.2003.1214425

M. Stephen, . Majercik, L. Michael, and . Littman, Using caching to solve larger probabilistic planning problems, AAAI/IAAI, pp.954-959, 1998.

F. Bacchus, S. Dalmao, and T. Pitassi, Algorithms and complexity results for #SAT and Bayesian inference, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings., pp.340-351, 2003.
DOI : 10.1109/SFCS.2003.1238208

P. João, . Silva, A. Karem, and . Sakallah, Grasp ? ? ?a new search algorithm for satisfiability, Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design, pp.220-227, 1997.

H. Zhang and . Sato, An efficient prepositional prover. Automated Deduction ? ? ?CADE-14, pp.272-275, 1997.

L. Zhang, F. Conor, . Madigan, H. Matthew, S. Moskewicz et al., Efficient conflict driven learning in a boolean satisfiability solver, Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design, pp.279-285, 2001.

J. Roberto, J. D. Jr, and . Pehoushek, Counting models using connected components, AAAI/IAAI, pp.157-162, 2000.

F. Bacchus, S. Dalmao, and T. Pitassi, Value elimination : Bayesian inference via backtracking search, Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence, pp.20-28, 2002.

A. Darwiche and P. Marquis, A knowledge compilation map, Journal of Artificial Intelligence Research, vol.17, issue.1, pp.229-264, 2002.

A. Darwiche and P. Marquis, A perspective on knowledge compilation, IJCAI, pp.175-182, 2001.

S. Kirkpatrick, Optimization by simulated annealing: Quantitative studies, Journal of Statistical Physics, vol.21, issue.5-6, pp.975-986, 1984.
DOI : 10.1007/BF01009452

M. Radford and . Neal, Probabilistic inference using Markov chain Monte Carlo methods, 1993.

M. Radford and . Neal, Annealed importance sampling, Statistics and Computing, vol.11, issue.2, pp.125-139, 2001.

J. Ma, J. Peng, S. Wang, and J. Xu, Estimating the partition function of graphical models using langevin importance sampling, AISTATS, pp.433-441, 2013.

J. Pearl, Reverend Bayes on inference engines : A distributed hierarchical approach, 1982.

S. Jonathan, . Yedidia, T. William, Y. Freeman, and . Weiss, Generalized belief propagation, NIPS, pp.689-695, 2000.

P. Kevin, Y. Murphy, . Weiss, I. Michael, and . Jordan, Loopy belief propagation for approximate inference : An empirical study, Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pp.467-475

C. Berrou, A. Glavieux, and P. Thitimajshima, Near Shannon limit error-correcting coding and decoding: Turbo-codes. 1, Proceedings of ICC '93, IEEE International Conference on Communications, pp.1064-1070, 1993.
DOI : 10.1109/ICC.1993.397441

S. Jonathan, . Yedidia, T. William, Y. Freeman, and . Weiss, Constructing freeenergy approximations and generalized belief propagation algorithms, IEEE Transactions on Information Theory, vol.51, issue.7, pp.2282-2312, 2005.

J. Domke, Learning Graphical Model Parameters with Approximate Marginal Inference, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.10, pp.2454-2467, 2013.
DOI : 10.1109/TPAMI.2013.31

J. Martin, . Wainwright, S. Tommi, A. S. Jaakkola, and . Willsky, A new class of upper bounds on the log partition function. Information Theory, IEEE Transactions on, vol.51, issue.7, pp.2313-2335, 2005.

J. Martin, . Wainwright, I. Michael, and . Jordan, Graphical models, exponential families, and variational inference, Foundations and Trends R in Machine Learning, vol.1, issue.12, pp.1-305, 2008.

M. Joris and . Mooij, libDAI : A free and open source C++ library for discrete approximate inference in graphical models, Journal of Machine Learning Research, vol.11, pp.2169-2173, 2010.

Q. Liu and A. T. Ihler, Bounding the partition function using holder's inequality, Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp.849-856, 2011.

R. Dechter, Mini-buckets, Proc. of the 16 th IJCAI, pp.1297-1303, 1997.
DOI : 10.1145/636865.636866

A. Fersht, Structure and mechanism in protein science : a guide to enzyme catalysis and protein folding, 1999.
DOI : 10.1142/10574

L. Chen and X. Han, Anti???PD-1/PD-L1 therapy of human cancer: past, present, and future, Journal of Clinical Investigation, vol.125, issue.9, pp.3384-3391, 2015.
DOI : 10.1172/JCI80011

F. Chiti, M. Christopher, and . Dobson, Protein Misfolding, Functional Amyloid, and Human Disease, Annual Review of Biochemistry, vol.75, issue.1, pp.333-366, 2006.
DOI : 10.1146/annurev.biochem.75.101304.123901

C. Errol, . Friedberg, C. Graham, W. Walker, . Siede et al., DNA repair and mutagenesis, 2005.

W. Clegg, Crystal structure determination Oxford Chemistry Primers, ) :ALL?ALL, 1998.

D. John and . Roberts, Nuclear magnetic resonance : applications to organic chemistry, 1959.

M. Helen, J. Berman, Z. Westbrook, G. Feng, . Gilliland et al., The protein data bank, Nucleic acids research, vol.28, issue.1, pp.235-242, 2000.

M. Christopher and . Dobson, Protein folding and misfolding, Nature, vol.426, issue.6968, p.884, 2003.

A. Ken, J. L. Dill, and . Maccallum, The protein-folding problem, 50 years on, science, vol.338, issue.6110, pp.1042-1046, 2012.

J. Moult, K. Fidelis, A. Kryshtafovych, T. Schwede, and A. Tramontano, Critical assessment of methods of protein structure prediction: Progress and new directions in round XI, Proteins: Structure, Function, and Bioinformatics, vol.41, issue.2, pp.4-14, 2016.
DOI : 10.1093/nar/gkt294

K. Henzler-wildman and D. Kern, Dynamic personalities of proteins, Nature, vol.124, issue.7172, p.450964, 2007.
DOI : 10.1038/nature06522

A. Katherine, M. Henzler-wildman, V. Lei, J. Thai, M. Kerns et al., A hierarchy of timescales in protein dynamics is linked to enzyme catalysis, Nature, issue.7171, p.450913, 2007.

G. Gordon, . Hammes, J. Stephen, S. Benkovic, and . Hammes-schiffer, Flexibility, diversity, and cooperativity : pillars of enzyme catalysis, Biochemistry, vol.50, issue.48, pp.10422-10430, 2011.

C. Levinthal, Are there pathways for protein folding?, Journal de Chimie Physique, vol.65, pp.44-45, 1968.
DOI : 10.1051/jcp/1968650044

J. Haile, Molecular dynamics simulation, 1992.

M. Mangoni, D. Roccatano, and A. D. Nola, Docking of flexible ligands to flexible receptors in solution by molecular dynamics simulation, Proteins: Structure, Function, and Genetics, vol.28, issue.2, pp.153-162, 1999.
DOI : 10.1021/bi00437a034

R. John, . Desjarlais, M. Tracy, and . Handel, Side-chain and backbone flexibility in protein core design, Journal of molecular biology, vol.290, issue.1, pp.305-318, 1999.

L. John, K. Klepeis, R. O. Lindorff-larsen, . Dror, E. David et al., Long-timescale molecular dynamics simulations of protein structure and function, Current opinion in structural biology, vol.19, issue.2, pp.120-127, 2009.

R. Andrew and . Leach, Ligand docking to proteins with discrete side-chain flexibility, Journal of molecular biology, vol.235, issue.1, pp.345-356, 1994.

W. Ian, . Davis, . Bryan-arendall, C. David, J. S. Richardson et al., The backrub motion : how protein backbone shrugs when a sidechain dances, Structure, vol.14, issue.2, pp.265-274, 2006.

A. Mark, . Hallen, A. Daniel, . Keedy, R. Bruce et al., Dead-end elimination with perturbations (deeper) : A provable protein design algorithm with continuous sidechain and backbone flexibility, Proteins : Structure, Function, and Bioinformatics, vol.81, issue.1, pp.18-39, 2013.

L. Roland and . Dunbrack, Rotamer libraries in the 21 st century, Current opinion in structural biology, vol.12, issue.4, pp.431-440, 2002.

K. Ho, . Fung, J. William, . Welsh, A. Christodoulos et al., Computational de novo peptide and protein design : rigid templates versus flexible templates, Industrial & Engineering Chemistry Research, vol.47, issue.4, pp.993-1001, 2008.

C. Jeffrey, . Moore, H. Frances, and . Arnold, Directed evolution of a paranitrobenzyl esterase for aqueous-organic solvents, Nature biotechnology, vol.14, issue.4, pp.458-467, 1996.

A. Christopher, . Voigt, L. Stephen, . Mayo, H. Frances et al., Computational method to reduce the search space for directed protein evolution, Proceedings of the National Academy of Sciences, vol.98, issue.7, pp.3778-3783, 2001.

C. Pabo, Molecular technology: Designing proteins and peptides, Nature, vol.281, issue.5897, pp.200-200, 1983.
DOI : 10.1038/301200a0

K. Drexler, Molecular engineering: An approach to the development of general capabilities for molecular manipulation, Proceedings of the National Academy of Sciences, pp.5275-5278, 1981.
DOI : 10.1073/pnas.78.9.5275

T. Harder, W. Boomsma, M. Paluszewski, J. Frellsen, E. Kristoffer et al., Beyond rotamers: a generative, probabilistic model of side chains in proteins, BMC Bioinformatics, vol.11, issue.1, p.306, 2010.
DOI : 10.1186/1471-2105-11-306

V. Maxim, . Shapovalov, L. Roland, and . Dunbrack, A smoothed backbonedependent rotamer library for proteins derived from adaptive kernel density estimates and regressions, Structure, vol.19, issue.6, pp.844-858, 2011.

S. Lovell, J. Word, J. Richardson, and D. Richardson, The penultimate rotamer library, Proteins: Structure, Function, and Genetics, vol.38, issue.3, pp.389-408, 2000.
DOI : 10.1080/07391102.1991.10507882

B. Christian and . Anfinsen, Principles that govern the folding of protein chains, Science, vol.181, issue.4096, pp.223-230, 1973.

D. Benjamin-gordon, A. Shannon, . Marshall, L. Stephen, and . Mayot, Energy functions for protein design, Current Opinion in Structural Biology, vol.9, issue.4, pp.509-513, 1999.
DOI : 10.1016/S0959-440X(99)80072-4

T. Lazaridis and M. Karplus, Discrimination of the native from misfolded protein models with an energy function including implicit solvation, Journal of Molecular Biology, vol.288, issue.3, pp.477-487, 1999.
DOI : 10.1006/jmbi.1999.2685

R. John, . Desjarlais, M. Tracy, and . Handel, De novo design of the hydrophobic cores of proteins, Protein Science, vol.4, issue.10, pp.2006-2018, 1995.

B. Kuhlman and D. Baker, Native protein sequences are close to optimal for their structures, Proceedings of the National Academy of Sciences, vol.26, issue.1, pp.10383-10391, 2000.
DOI : 10.1093/nar/26.1.313

E. Pedro, O. Lopes, A. D. Guvench, and . Mackerell, Current status of protein force fields for molecular dynamics simulations, Molecular Modeling of Proteins, pp.47-71, 2015.

D. Wendy, P. Cornell, . Cieplak, I. Christopher, . Bayly et al., A second generation force field for the simulation of proteins, nucleic acids, and organic molecules, Journal of the American Chemical Society, issue.19, pp.1175179-5197, 1995.

A. David, . Pearlman, A. David, . Case, W. James et al., Amber, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules, Computer Physics Communications, vol.91, issue.1, pp.1-41, 1995.

A. D. , M. Jr, D. Bashford, R. L. Bellott, D. Jr et al., All-atom empirical potential for molecular modeling and dynamics studies of proteins, The journal of physical chemistry B, vol.102, issue.18, pp.3586-3616, 1998.

R. Bernard, . Brooks, L. Charles, . Brooks, D. Alexander et al., Charmm : the biomolecular simulation program, Journal of computational chemistry, issue.10, pp.301545-1614, 2009.

C. Oostenbrink, A. Villa, E. Alan, W. F. Mark, and . Van-gunsteren, A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force-field parameter sets 53A5 and 53A6, Journal of Computational Chemistry, vol.91, issue.13, pp.251656-1676, 2004.
DOI : 10.1007/978-94-015-7658-1_21

Z. Dosztanyi, V. Csizmok, P. Tompa, and I. Simon, The Pairwise Energy Content Estimated from Amino Acid Composition Discriminates between Folded and Intrinsically Unstructured Proteins, Journal of Molecular Biology, vol.347, issue.4, pp.827-839, 2005.
DOI : 10.1016/j.jmb.2005.01.071

K. Raha, J. Arjan, . Van-der-vaart, E. Kevin, . Riley et al., Pairwise Decomposition of Residue Interaction Energies Using Semiempirical Quantum Mechanical Methods in Studies of Protein???Ligand Interaction, Journal of the American Chemical Society, vol.127, issue.18, pp.1276583-6594, 2005.
DOI : 10.1021/ja042666p

A. Leaver-fay, M. Tyka, M. Steven, . Lewis, F. Oliver et al., Rosetta3, Methods in enzymology, vol.487, p.545, 2011.
DOI : 10.1016/B978-0-12-381270-4.00019-6

P. Gainza, E. Kyle, I. Roberts, . Georgiev, H. Ryan et al., Protein design with ensembles, flexibility, and provable algorithms, Methods Enzymol, 2012.

A. Carol, . Rohl, E. Charlie, . Strauss, M. Kira et al., Protein structure prediction using rosetta, Methods in enzymology, vol.383, pp.66-93, 2004.

L. Jiang, A. Eric, . Althoff, R. Fernando, L. Clemente et al., De Novo Computational Design of Retro-Aldol Enzymes, Science, vol.37, issue.90001, pp.3191387-1391, 2008.
DOI : 10.1093/nar/gkh028

D. Röthlisberger, O. Khersonsky, M. Andrew, L. Wollacott, J. Jiang et al., Kemp elimination catalysts by computational enzyme design, Nature, vol.160, issue.7192, p.453190, 2008.
DOI : 10.1103/PhysRevB.37.785

J. Daniel, . Mandell, A. Evangelos, T. Coutsias, and . Kortemme, Subangstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling, Nature methods, vol.6, issue.8, pp.551-552, 2009.

D. Gront, W. Daniel, . Kulp, M. Robert, . Vernon et al., Generalized Fragment Picking in Rosetta: Design, Protocols and Applications, PLoS ONE, vol.22, issue.8, p.23294, 2011.
DOI : 10.1371/journal.pone.0023294.s001
URL : https://doi.org/10.1371/journal.pone.0023294

I. Georgiev, H. Ryan, . Lilien, R. Bruce, and . Donald, The minimized deadend elimination criterion and its application to protein redesign in a hybrid scoring and search algorithm for computing partition functions over molecular ensembles, Journal of computational chemistry, issue.10, pp.291527-291569, 2008.

E. Kyle, . Roberts, R. Patrick, P. Cushing, . Boisguerin et al., Computational design of a pdz domain peptide inhibitor that rescues cftr activity, PLoS Comput Biol, vol.8, issue.4, pp.1002477-1002477, 2012.

S. Ivelin, R. S. Georgiev, . Rudicell, O. Kevin, W. Saunders et al., Antibodies vrc01 and 10e8 neutralize hiv-1 with high breadth and potency even with ig-framework regions substantially reverted to germline, The Journal of Immunology, vol.192, issue.3, pp.1100-1106, 2014.

S. Rebecca, Y. D. Rudicell, S. Kwon, A. Ko, . Pegu et al., Enhanced potency of a broadly neutralizing hiv-1 antibody in vitro improves protection against lentiviral infection in vivo, Journal of virology, issue.21, pp.8812669-12682, 2014.

T. Kim, C. Simons, E. Kooperberg, D. Huang, and . Baker, Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions, Journal of molecular biology, vol.268, issue.1, pp.209-225, 1997.

T. Kim, I. Simons, C. Ruczinski, . Kooperberg, A. Brian et al., Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins, Proteins : Structure, Function, and Bioinformatics, vol.34, issue.1, pp.82-95, 1999.

T. Kortemme and D. Baker, A simple physical model for binding energy hot spots in protein-protein complexes, Proceedings of the National Academy of Sciences, pp.14116-14121, 2002.
DOI : 10.1016/S0022-2836(02)00442-4

J. Jeffrey, S. Gray, C. Moughon, O. Wang, B. Schueler-furman et al., Protein?protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations, Journal of molecular biology, vol.331, issue.1, pp.281-299, 2003.

T. Kortemme, E. David, D. Kim, and . Baker, Computational Alanine Scanning of Protein-Protein Interfaces, Science Signaling, vol.8, issue.8, pp.2-2, 2004.
DOI : 10.1110/ps.8.8.1643

P. Bradley, M. Kira, D. Misura, and . Baker, Toward High-Resolution de Novo Structure Prediction for Small Proteins, Science, vol.309, issue.5742, pp.1868-1871, 2005.
DOI : 10.1126/science.1113801

T. Kortemme, A. Lukasz, A. N. Joachimiak, . Bullock, D. Aaron et al., Computational redesign of protein-protein interaction specificity, Nature Structural & Molecular Biology, vol.11, issue.4, p.371, 2004.
DOI : 10.1038/nsmb749

K. Praneeth-kilambi, K. Reddy, J. Jeffrey, and . Gray, Protein-Protein Docking with Dynamic Residue Protonation States, PLoS Computational Biology, vol.26, issue.12, p.1004018, 2014.
DOI : 10.1371/journal.pcbi.1004018.s015

F. Rebecca, J. Alford, . Koehler-leman, D. Brian, . Weitzner et al., An integrated framework advancing membrane protein modeling and design, PLoS computational biology, vol.11, issue.9, p.1004398, 2015.

F. Rebecca, A. Alford, . Leaver-fay, R. Jeliazko, . Jeliazkov et al., The rosetta all-atom energy function for macromolecular modeling and design, Journal of Chemical Theory and Computation, 2017.

J. Edward and J. , On the determination of molecular fields. i. from the variation of the viscosity of a gas with temperature, Proceedings of the Royal Society of London A : Mathematical, Physical and Engineering Sciences, vol.106, issue.738, pp.441-462, 1924.

J. Edward and J. , On the determination of molecular fields. ii. from the equation of state of a gas, Proceedings of the Royal Society of London A : Mathematical, Physical and Engineering Sciences, vol.106, issue.738, pp.463-477, 1924.

T. Lazaridis and M. Karplus, Effective energy function for proteins in solution, Proteins: Structure, Function, and Genetics, vol.37, issue.2, pp.133-152, 1999.
DOI : 10.1002/(SICI)1521-3773(19980420)37:7<868::AID-ANIE868>3.0.CO;2-H

H. Park, P. Bradley, Y. Per-greisen-jr, . Liu, . Vikram-khipple-mulligan et al., Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules, Journal of Chemical Theory and Computation, vol.12, issue.12, pp.6201-6212, 2016.
DOI : 10.1021/acs.jctc.6b00819

C. Yanover and P. Bradley, Extensive protein and DNA backbone sampling improves structure-based specificity prediction for C2H2 zinc fingers, Nucleic Acids Research, vol.38, issue.11, pp.4564-4576, 2011.
DOI : 10.1093/nar/gkq268

T. Kortemme, V. Alexandre, D. Morozov, and . Baker, An Orientation-dependent Hydrogen Bonding Potential Improves Prediction of Specificity and Structure for Proteins and Protein???Protein Complexes, Journal of Molecular Biology, vol.326, issue.4, pp.1239-1259, 2003.
DOI : 10.1016/S0022-2836(03)00021-4

J. Matthew, A. Meara, . Leaver-fay, D. Michael, A. Tyka et al., Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with rosetta, Journal of chemical theory and computation, vol.11, issue.2, pp.609-622, 2015.

A. Leaver-fay, J. Matthew, M. Meara, R. Tyka, Y. Jacak et al., Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement, Methods in enzymology, vol.523, p.109, 2013.
DOI : 10.1016/B978-0-12-394292-0.00006-0

S. Donald, . Berkholz, M. Camden, . Driggers, V. Maxim et al., Nonplanar peptide bonds in proteins are common and conserved but not biased toward active sites, Proceedings of the National Academy of Sciences, pp.449-453, 2012.

H. Hellinga and F. Richards, Optimal sequence selection in proteins of known structure by simulated evolution., Proceedings of the National Academy of Sciences, pp.5803-5807, 1994.
DOI : 10.1073/pnas.91.13.5803

A. Ken and . Dill, Dominant forces in protein folding, Biochemistry, issue.31, pp.297133-7155, 1990.

I. Bassil, . Dahiyat, L. Stephen, and . Mayo, De novo protein design : fully automated sequence selection, Science, vol.278, issue.5335, pp.82-87, 1997.

T. Scott, H. Walsh, . Cheng, W. James, H. Bryson et al., Solution structure and dynamics of a de novo designed three-helix bundle protein, Proceedings of the National Academy of Sciences, pp.965486-5491, 1999.

W. James, . Bryson, R. John, . Desjarlais, M. Tracy et al., From coiled coils to small globular proteins : Design of a native-like three-helix bundle, Protein Science, vol.7, issue.6, pp.1404-1414, 1998.

B. Kuhlman, G. Dantas, C. Gregory, G. Ireton, . Varani et al., Design of a Novel Globular Protein Fold with Atomic-Level Accuracy, Science, vol.11, issue.5649, pp.3021364-1368, 2003.
DOI : 10.1006/jmrb.1995.1109

R. Arnout, H. Voet, C. Noguchi, D. Addy, D. Simoncini et al., Computational design of a self-assembling symmetrical ?-propeller protein, Proceedings of the National Academy of Sciences, pp.11115102-15107, 2014.

E. Christine, . Tinberg, D. Sagar, J. Khare, L. Dou et al., Computational design of ligandbinding proteins with high affinity and selectivity, Nature, issue.7466, pp.501212-216, 2013.

N. Jaafar, B. Haidar, Y. Pierce, W. Yu, M. Tong et al., Structure-based design of a t-cell receptor leads to nearly 100-fold improvement in binding affinity for pepmhc, Proteins : Structure, Function, and Bioinformatics, vol.74, issue.4, pp.948-960, 2009.

G. Brian, . Pierce, M. Lance, M. Hellman, . Hossain et al., Computational design of the affinity and specificity of a therapeutic t cell receptor, PLoS Comput Biol, vol.10, issue.2, p.1003478, 2014.

N. Daniel, . Bolon, L. Stephen, and . Mayo, Enzyme-like proteins by computational design, Proceedings of the National Academy of Sciences, pp.14274-14279, 2001.

S. Nauli, B. Kuhlman, and D. Baker, Computer-based redesign of a protein folding pathway, Nature Structural Biology, vol.8, issue.7, p.602, 2001.
DOI : 10.1038/89638

L. Loren, . Looger, A. Mary, . Dwyer, J. James et al., Computational design of receptor and sensor proteins with novel functions, Nature, issue.6936, p.423185, 2003.

G. Dantas, B. Kuhlman, D. Callender, M. Wong, and D. Baker, A Large Scale Test of Computational Protein Design: Folding and Stability of Nine Completely Redesigned Globular Proteins, Journal of Molecular Biology, vol.332, issue.2, pp.449-460, 2003.
DOI : 10.1016/S0022-2836(03)00888-X

M. Shaun, B. T. Lippow, A. Stuart, J. Sievers, . Karanicolas et al., Progress in computational protein design Structure-based design of non-natural amino acid inhibitors of amyloid fibrillation, Current opinion in biotechnology Nature, vol.18180, issue.47354, pp.305-311, 2007.

A. Verges, E. Cambon, S. Barbe, S. Salamone, Y. Le-guen et al., Computer-Aided Engineering of a Transglycosylase for the Glucosylation of an Unnatural Disaccharide of Relevance for Bacterial Antigen Synthesis, ACS Catalysis, vol.5, issue.2, pp.1186-1198, 2015.
DOI : 10.1021/cs501288r

P. Sormanni, A. Francesco, M. Aprile, and . Vendruscolo, Rational design of antibodies targeting specific epitopes within intrinsically disordered proteins, Proceedings of the National Academy of Sciences, pp.9902-9907, 2015.
DOI : 10.1093/nar/gkt1043

A. Jorge, G. Fallas, W. Ueda, V. Sheffler, D. E. Nguyen et al., Duilio Cascio, et al. Computational design of self-assembling cyclic protein homo-oligomers, Protein Expression and Purification, vol.28, p.29, 2016.

E. Strauch, M. Steffen, D. Bernard, A. J. La, . Bohn et al., Computational design of trimeric influenzaneutralizing proteins targeting the hemagglutinin receptor binding site, Nature Biotechnology, issue.7, pp.35667-671, 2017.

S. Kiyonaka, R. Kubota, Y. Michibata, M. Sakakura, H. Takahashi et al., Allosteric activation of membrane-bound glutamate receptors using coordination chemistry within living cells, Nature Chemistry, vol.8, issue.10, 2016.
DOI : 10.1016/j.jneumeth.2008.03.013

P. Pattnaik, Surface plasmon resonance Applied biochemistry and biotechnology, pp.79-92, 2005.

A. Velázquez-campoy, H. Ohtaka, A. Nezami, S. Muzammil, and E. Freire, Isothermal titration calorimetry. Current protocols in cell biology, pp.17-25, 2004.

Y. Tokiwa, P. Buenaventurada, . Calabia, U. Charles, S. Ugwu et al., Biodegradability of Plastics, International Journal of Molecular Sciences, vol.19, issue.12, pp.3722-3742, 2009.
DOI : 10.1007/s10532-008-9188-0

L. Panagiotis, . Kastritis, M. Alexandre, and . Bonvin, On the binding affinity of macromolecular interactions : daring to ask why proteins interact, Journal of The Royal Society Interface, vol.10, issue.79, p.20120835, 2013.

G. Mocz and J. A. Ross, Fluorescence Techniques in Analysis of Protein???Ligand Interactions, Protein-Ligand Interactions : Methods and Applications, pp.169-210, 2013.
DOI : 10.1007/978-1-62703-398-5_7

S. Kurt, . Thorn, A. Andrew, and . Bogan, Asedb : a database of alanine mutations and their effects on the free energy of binding in protein interactions, Bioinformatics, vol.17, issue.3, pp.284-285, 2001.

M. Shaji, K. , and M. Michael-gromiha, Pint : protein?protein interactions thermodynamic database, Nucleic acids research, vol.34, issue.suppl_1, pp.195-198, 2006.

H. Iain, J. Moal, and . Fernández-recio, Skempi : a structural kinetic and energetic database of mutant protein interactions and its use in empirical models, Bioinformatics, vol.28, issue.20, pp.2600-2607, 2012.

Z. Liu, Y. Li, L. Han, J. Li, J. Liu et al., PDB-wide collection of binding data: current status of the PDBbind database, Bioinformatics, vol.53, issue.3, pp.31405-412, 2014.
DOI : 10.1021/ci400120b

T. Vreven, H. Iain, A. Moal, . Vangone, G. Brian et al., Updates to the Integrated Protein???Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2, Journal of Molecular Biology, vol.427, issue.19, pp.4273031-3041, 2015.
DOI : 10.1016/j.jmb.2015.07.016

X. Wu, T. Zhou, J. Zhu, B. Zhang, I. Georgiev et al., Focused Evolution of HIV-1 Neutralizing Antibodies Revealed by Structures and Deep Sequencing, Science, vol.72, issue.4, pp.3331593-1602, 2011.
DOI : 10.1002/prot.22005

A. Timothy, A. Whitehead, Y. Chevalier, C. Song, . Dreyfus et al., Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing, Nature biotechnology, issue.6, pp.30543-548, 2012.

P. William, . Robins, M. Shah, J. J. Faruque, and . Mekalanos, Coupling mutagenesis and parallel deep sequencing to probe essential residues in a genome or gene, Proceedings of the National Academy of Sciences, vol.110, issue.9, pp.848-857, 2013.

H. Terrell, W. Nathaniel, . Silver, M. Bracken, . King et al., Cooperativity theory in biochemistry : steady-state and equilibrium systems Efficient computation of small-molecule configurational binding entropy and free energy changes by ensemble enumeration, Springer Science & Business Media Journal of chemical theory and computation, vol.201, issue.11, pp.95098-5115, 2013.

G. Rastelli, A. D. Rio, G. Degliesposti, and M. Sgobba, Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA, Journal of Computational Chemistry, vol.88, issue.4, pp.797-810, 2010.
DOI : 10.1007/s00214-002-0384-4

T. Hou, J. Wang, Y. Li, and W. Wang, Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations, Journal of Chemical Information and Modeling, vol.51, issue.1, pp.69-82, 2010.
DOI : 10.1021/ci100275a

I. Massova, A. Peter, and . Kollman, Combined molecular mechanical and continuum solvent approach (mm-pbsa/gbsa) to predict ligand binding. Perspectives in drug discovery and design, pp.113-135, 2000.

A. Peter, I. Kollman, C. Massova, B. Reyes, S. Kuhn et al., Calculating structures and free energies of complex molecules : combining molecular mechanics and continuum models, Accounts of chemical research, issue.12, pp.33889-897, 2000.

C. Jarzynski, Nonequilibrium Equality for Free Energy Differences, Physical Review Letters, vol.12, issue.14, p.2690, 1997.
DOI : 10.1002/jcc.540120218

J. Åqvist, B. Victor, . Luzhkov, O. Bjørn, and . Brandsdal, Ligand Binding Affinities from MD Simulations, Accounts of Chemical Research, vol.35, issue.6, pp.358-365, 2002.
DOI : 10.1021/ar010014p

H. Gohlke, C. Kiel, A. David, and . Case, Insights into Protein???Protein Binding by Binding Free Energy Calculation and Free Energy Decomposition for the Ras???Raf and Ras???RalGDS Complexes, Journal of Molecular Biology, vol.330, issue.4, pp.891-913, 2003.
DOI : 10.1016/S0022-2836(03)00610-7

L. David, F. Beveridge, and . Dicapua, Free energy via molecular simulation : applications to chemical and biomolecular systems. Annual review of biophysics and biophysical chemistry, pp.431-492, 1989.

B. Surjit, C. Dixit, and . Chipot, Can absolute free energies of association be estimated from molecular mechanical simulations ? the biotinstreptavidin system revisited, The Journal of Physical Chemistry A, vol.105, issue.42, pp.9795-9799, 2001.

C. Chipot, Frontiers in free-energy calculations of biological systems, Wiley Interdisciplinary Reviews: Computational Molecular Science, vol.9, issue.1, pp.71-89, 2014.
DOI : 10.1021/ct300867e

R. Guerois, J. E. Nielsen, and L. Serrano, Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutations, Journal of Molecular Biology, vol.320, issue.2, pp.369-387, 2002.
DOI : 10.1016/S0022-2836(02)00442-4

J. Schymkowitz, J. Borg, F. Stricher, R. Nys, F. Rousseau et al., The FoldX web server: an online force field, Nucleic Acids Research, vol.33, issue.Web Server, pp.382-388, 2005.
DOI : 10.1093/nar/gki387
URL : https://academic.oup.com/nar/article-pdf/33/suppl_2/W382/7622711/gki387.pdf

Y. Dehouck, J. M. Kwasigroch, M. Rooman, and D. Gilis, BeAtMuSiC: prediction of changes in protein???protein binding affinity on mutations, Nucleic Acids Research, vol.161, issue.W1, pp.333-339, 2013.
DOI : 10.1016/j.jbiotec.2012.06.020

R. Jeffrey, Y. Brender, and . Zhang, Predicting the effect of mutations on protein-protein binding interactions through structure-based interface profiles, PLoS Comput Biol, vol.11, issue.10, p.1004494, 2015.

M. Li, M. Petukh, E. Alexov, and A. R. Panchenko, Predicting the Impact of Missense Mutations on Protein???Protein Binding Affinity, Journal of Chemical Theory and Computation, vol.10, issue.4, pp.1770-1780, 2014.
DOI : 10.1021/ct401022c

M. Li, L. Franco, A. Simonetti, A. R. Goncearenco, and . Panchenko, MutaBind estimates and interprets the effects of sequence variants on protein???protein interactions, Nucleic Acids Research, vol.31, issue.W1, p.374, 2016.
DOI : 10.1093/nar/gkr997

S. Marillet, P. Boudinot, and F. Cazals, High-resolution crystal structures leverage protein binding affinity predictions, Proteins: Structure, Function, and Bioinformatics, vol.32, issue.1, pp.9-20, 2016.
DOI : 10.1021/ma990051k
URL : https://hal.archives-ouvertes.fr/hal-01159641

H. Kamisetty, A. Ramanathan, C. Bailey-kellogg, and C. J. Langmead, Accounting for conformational entropy in predicting binding free energies of protein-protein interactions, Proteins: Structure, Function, and Bioinformatics, vol.13, issue.2, pp.444-462, 2011.
DOI : 10.1002/prot.22894

C. Kiel and L. Serrano, Structure-energy-based predictions and network modelling of rasopathy and cancer missense mutations. Molecular systems biology, p.727, 2014.

T. Selzer, S. Albeck, and G. Schreiber, Rational design of faster associating and tighter binding protein complexes, Nature Structural & Molecular Biology, vol.7, issue.7, pp.537-541, 2000.

R. Abagyan and M. Totrov, Biased Probability Monte Carlo Conformational Searches and Electrostatic Calculations for Peptides and Proteins, Journal of Molecular Biology, vol.235, issue.3, pp.983-1002, 1994.
DOI : 10.1006/jmbi.1994.1052

Y. Dehouck, D. Gilis, and M. Rooman, Design of modified proteins using knowledge-based approaches, AIP Conference Proceedings, pp.139-147, 2012.
DOI : 10.1063/1.4730653

Y. Dehouck, A. Grosfils, B. Folch, D. Gilis, P. Bogaerts et al., Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0, Bioinformatics, vol.11, issue.19, pp.252537-2543, 2009.
DOI : 10.1110/ps.0217002

J. Janin, K. Henrick, J. Moult, L. T. Eyck, J. Michael et al., CAPRI: A Critical Assessment of PRedicted Interactions, Proteins: Structure, Function, and Genetics, vol.11, issue.1, pp.2-9, 2003.
DOI : 10.1002/prot.340110407

F. Gao, M. Bonsignori, . Hua-xin, A. Liao, S. Kumar et al., Cooperation of B Cell Lineages in Induction of HIV-1-Broadly Neutralizing Antibodies, Cell, vol.158, issue.3, pp.158481-491, 2014.
DOI : 10.1016/j.cell.2014.06.022

V. Domankevich, Y. Opatowsky, A. Malik, B. Abraham, Z. Korol et al., Adaptive patterns in the p53 protein sequence of the hypoxia- and cancer-tolerant blind mole rat Spalax, BMC Evolutionary Biology, vol.27, issue.10, p.177, 2016.
DOI : 10.1093/molbev/msq115

M. Gribskov, D. Andrew, D. Mclachlan, and . Eisenberg, Profile analysis: detection of distantly related proteins., Proceedings of the National Academy of Sciences, pp.4355-4358, 1987.
DOI : 10.1073/pnas.84.13.4355

P. Xiong, C. Zhang, W. Zheng, and Y. Zhang, BindProfX: Assessing Mutation-Induced Binding Affinity Change by Protein Interface Profiles with Pseudo-Counts, Journal of Molecular Biology, vol.429, issue.3, pp.426-434, 2017.
DOI : 10.1016/j.jmb.2016.11.022

F. Cazals, F. Proust, P. Ranjit, J. Bahadur, and . Janin, Revisiting the Voronoi description of protein-protein interfaces, Protein Science, vol.15, issue.9, pp.2082-2092, 2006.
DOI : 10.1110/ps.062245906
URL : https://hal.archives-ouvertes.fr/hal-00796062

S. Loriot and F. Cazals, Modeling macro-molecular interfaces with Intervor, Bioinformatics, vol.7, issue.2, pp.964-965, 2010.
DOI : 10.1186/1471-2105-7-104
URL : https://hal.archives-ouvertes.fr/hal-00849822

B. Bouvier, R. Grünberg, M. Nilges, and F. Cazals, Shelling the Voronoi interface of protein-protein complexes reveals patterns of residue conservation, dynamics, and composition, Proteins: Structure, Function, and Bioinformatics, vol.2, issue.3, pp.677-692, 2009.
DOI : 10.1093/bioinformatics/18.suppl_2.S249
URL : https://hal.archives-ouvertes.fr/hal-00796032

F. Cazals, H. Kanhere, and S. Loriot, Computing the volume of a union of balls, ACM Transactions on Mathematical Software, vol.38, issue.1, p.3, 2011.
DOI : 10.1145/2049662.2049665
URL : https://hal.archives-ouvertes.fr/hal-00849809

D. Eisenberg, M. Wesson, and M. Yamashita, Interpretation of protein folding and binding with atomic solvation parameters, Chem. Scr. A, vol.29, pp.217-221, 1989.

L. Panagiotis, . Kastritis, P. João, . Rodrigues, E. Gert et al., Proteins feel more than they see : fine-tuning of binding affinity by properties of the non-interacting surface, Journal of molecular biology, issue.14, pp.4262632-2652, 2014.

L. Panagiotis, . Kastritis, H. Iain, H. Moal, Z. Hwang et al., A structure-based benchmark for protein?protein binding affinity, Protein Science, vol.20, issue.3, pp.482-491, 2011.

M. Glenn, . Torrie, P. John, and . Valleau, Nonphysical sampling distributions in monte carlo free-energy estimation : Umbrella sampling, Journal of Computational Physics, vol.23, issue.2, pp.187-199, 1977.

C. Bartels and M. Karplus, Probability Distributions for Complex Systems:?? Adaptive Umbrella Sampling of the Potential Energy, The Journal of Physical Chemistry B, vol.102, issue.5, pp.865-880, 1998.
DOI : 10.1021/jp972280j

J. Kästner, Umbrella sampling, Wiley Interdisciplinary Reviews: Computational Molecular Science, vol.8, issue.6, pp.932-942, 2011.
DOI : 10.1002/cphc.200600527

P. Kollman, Free energy calculations: Applications to chemical and biochemical phenomena, Chemical Reviews, vol.93, issue.7, pp.2395-2417, 1993.
DOI : 10.1021/cr00023a004

D. Shivakumar, J. Williams, Y. Wu, W. Damm, J. Shelley et al., Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field, Journal of Chemical Theory and Computation, vol.6, issue.5, pp.1509-1519, 2010.
DOI : 10.1021/ct900587b

A. Benedix, C. M. Becker, B. L. De-groot, A. Caflisch, and R. A. Böckmann, Predicting free energy changes using structural ensembles, Nature Methods, vol.120, issue.1, pp.3-4, 2009.
DOI : 10.1038/nmeth0109-3

A. Leach and A. Lemon, Exploring the conformational space of protein side chains using dead-end elimination and the A* algorithm, Proteins: Structure, Function, and Genetics, vol.79, issue.2, pp.227-266, 1998.
DOI : 10.1016/0022-2836(73)90011-9

R. Goldstein, Efficient rotamer elimination applied to protein side-chains and related spin glasses, Biophysical Journal, vol.66, issue.5, pp.1335-1375, 1994.
DOI : 10.1016/S0006-3495(94)80923-3

I. Georgiev, R. Bruce, and . Donald, Dead-End Elimination with Backbone Flexibility, Bioinformatics, vol.15, issue.13, pp.185-194, 2007.
DOI : 10.1110/ps.062353106

A. Adegoke, . Ojewole, D. Jonathan, . Jou, G. Vance et al., Bbk?*(branch and bound over k?*) : A provable and efficient ensemblebased algorithm to optimize stability and binding affinity over large sequence spaces, International Conference on Research in Computational Molecular Biology, pp.157-172, 2017.

S. Jane and . Richardson, Osprey : protein design with ensembles, flexibility, and provable algorithms, Methods in enzymology, vol.523, p.87, 2013.

J. Larrosa, Boosting search with variable elimination In Principles and Practice of Constraint Programming -CP, LNCS, vol.1894, pp.291-305, 2000.

R. Clay and P. , Shortest connection networks and some generalizations, Bell Labs Technical Journal, vol.36, issue.6, pp.1389-1401, 1957.

L. Ronald, P. Graham, and . Hell, On the history of the minimum spanning tree problem, Annals of the History of Computing, vol.7, issue.1, pp.43-57, 1985.

. Robert-endre-tarjan, Data structures and network algorithms, SIAM, 1983.

M. Sanchez, D. Allouche, S. De-givry, and T. Schiex, Russian doll search with tree decomposition, Proc. IJCAI'09, pp.603-608, 2009.

H. William, . Landschulz, F. Peter, . Johnson, L. Steven et al., The leucine zipper : a hypothetical structure common to a new class of dna binding proteins, Science, vol.240, issue.4860, pp.1759-1765, 1988.

C. David, D. Chan, . Fass, M. James, . Berger et al., Core structure of gp41 from the hiv envelope glycoprotein, Cell, vol.89, issue.2, pp.263-273, 1997.

W. Christopher, M. Wood, . Bruning, Á. Amaurys, . Ibarra et al., Ccbuilder : an interactive web-based tool for building, designing and assessing coiled-coil protein assemblies, Bioinformatics, issue.21, pp.303029-3035, 2014.

S. Jemimah and . Yugandhar, PROXiMATE: a database of mutant protein???protein complex thermodynamics and kinetics, Bioinformatics, vol.23, issue.17, p.312, 2017.
DOI : 10.1002/humu.20021

S. Chaudhury, S. Lyskov, J. Jeffrey, and . Gray, PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta, Bioinformatics, vol.74, issue.8, pp.689-691, 2010.
DOI : 10.1002/prot.22540

H. Park, P. Bradley, P. Greisen, Y. Liu, D. E. Vikram-khipple-mulligan et al., Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules, Journal of Chemical Theory and Computation, vol.12, issue.12, pp.6201-6212, 2016.
DOI : 10.1021/acs.jctc.6b00819

W. Lu, I. Apostol, M. Qasim, N. Warne, R. Wynn et al., Binding of amino acid side-chains to S 1 cavities of serine proteinases 1 1Edited by R. Huber, Journal of Molecular Biology, vol.266, issue.2, pp.441-461, 1997.
DOI : 10.1006/jmbi.1996.0781

H. Elizabeth, A. Kellogg, D. Leaver-fay, and . Baker, Role of conformational sampling in computing mutation-induced changes in protein structure and stability, Proteins : Structure, Function, and Bioinformatics, vol.79, issue.3, pp.830-838, 2011.

D. Far, D. , and S. Flores, A multiscale approach to predicting affinity changes in protein?protein interfaces, Proteins : Structure, Function, and Bioinformatics, vol.82, issue.10, pp.2681-2690, 2014.

D. Far, D. , and S. Flores, Modeling and fitting protein-protein complexes to predict change of binding energy Scientific reports, 2016.