K. Feldmeier and B. Hoecker, Computational protein design of ligand binding and catalysis, Curr. Opin. Chem. Biol, vol.17, p.929, 2013.

C. E. Tinberg, S. D. Khare, J. Dou, L. Doyle, J. W. Nelson et al., Computational design of ligand-binding proteins with high affinity and selectivity, Nature, vol.501, p.212, 2013.

, Methods in Molecular Biology: Design and Creation of Ligand Binding Proteins, 2016.

N. Pokala and T. M. Handel, Energy functions for protein design I: Efficient and accurate continuum electrostatics and solvation, Protein Sci, vol.13, p.925, 2004.
DOI : 10.1110/ps.03486104

I. Samish, C. M. Macdermaid, J. M. Perez-aguilar, and J. G. Saven, Theoretical and computational protein design, Annu. Rev. Phys. Chem, vol.62, p.129, 2011.
DOI : 10.1146/annurev-physchem-032210-103509

Z. Li, Y. Yang, J. Zhan, L. Dai, and Y. Zhou, Energy functions in de novo protein design: Current challenges and future prospects, Annu. Rev. Biochem, vol.42, p.315, 2013.

X. Hu, H. Hu, D. N. Beratan, and W. Yang, A gradient-directed Monte Carlo approach for protein design, J. Comput. Chem, vol.31, p.2164, 2010.

A. Leaver-fay, M. Tyka, S. M. Lewis, O. F. Lange, J. Thompson et al., Rosetta3: An objectoriented software suite for the simulation and design of macromolecules, Methods Enzymol, vol.487, p.545, 2011.

R. F. Goldstein, Evolutionary perspectives on protein thermodynamics, Lect. Notes Comput. Sci, vol.3039, p.718, 2004.
DOI : 10.1007/978-3-540-25944-2_93
URL : https://link.springer.com/content/pdf/10.1007%2F978-3-540-25944-2_93.pdf

E. J. Hong, S. M. Lippow, B. Tidor, and T. Lozano-perez, Rotamer optimization for protein design through MAP estimation and problem size reduction, J. Comput. Chem, vol.30, p.1923, 2009.
DOI : 10.1002/jcc.21188
URL : http://europepmc.org/articles/pmc3495010?pdf=render

I. Georgiev, R. H. Lilien, and B. R. Donald, The minimized dead-end elimination criterion and its application to protein redesign in a hybrid scoring and search algorithm for computing partition functions over molecular ensembles, J. Comput. Chem, vol.29, p.1527, 2008.

S. Traore, D. Allouche, I. André, S. De-givry, G. Katsirelos et al., A new framework for computational protein design through cost function network optimization, Bioinformatics, vol.29, p.2129, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01268555

D. Simoncini, D. Allouche, S. De-givry, C. Delmas, S. Barbe et al., Guaranteed discrete energy optimization on large protein design problems, J. Chem. Theory Comput, vol.11, p.5980, 2015.
DOI : 10.1021/acs.jctc.5b00594

C. Viricel, D. Simoncini, D. Allouche, S. De-givry, S. Barbe et al., Approximate counting with deterministic guarantees for affinity computation, inAdvances in Intelligent Systems and Computing, vol.360, p.165, 2015.
DOI : 10.1007/978-3-319-18167-7_15
URL : https://hal.archives-ouvertes.fr/hal-01269259

K. E. Roberts, P. R. Cushing, P. Boisguerin, D. R. Madden, and B. R. Donald, Design of protein-protein interactions with a novel ensemble-based scoring algorithm, Lect. Notes Bioinf, vol.6577, p.361, 2011.

Q. Shen, H. Tian, D. Tang, W. Yao, and X. Gao, Ligand-K * sequence elimination: A novel algorithm for ensemble-based redesign of receptorligand binding, IEEE/ACM Trans. Comput. Biol. Bioinf, vol.11, p.573, 2014.

J. Gorodkin, I. L. Hofacker, and W. L. Ruzzo, Concepts and introduction to RNA bioinformatics, Methods in Molecular Biology, vol.1097, pp.1-31, 2014.
DOI : 10.1007/978-1-62703-709-9_1

S. Findless, M. Etzel, S. Will, M. Moerl, and P. F. Stadler, Design of artificial riboswitches as biosensors, Sensors, vol.17, 1990.

J. Waldispuehl, C. W. O'donnell, S. Devadas, P. Clote, and B. Berger, Modelling ensembles of transmembrane beta-barrel proteins, Proteins, vol.71, p.1097, 2008.

J. Valleau and G. Torrie, A guide to Monte Carlo for statistical mechanics, Modern Theoretical Chemistry, vol.5, pp.137-194, 1977.
DOI : 10.1007/978-1-4684-2553-6_5

V. Helms and R. C. Wade, Computational alchemy to calculate absolute protein-ligand binding free energy, Biophys. J, vol.120, p.2710, 1998.

Y. Deng and B. Roux, Computations of standard binding free energies with molecular dynamics simulations, J. Phys. Chem. B, vol.113, p.2234, 2009.

T. Simonson, ;. Becker, A. Mackerell, B. Roux, and M. Watanabe, Free energy calculations, Computational Biochemistry and Biophysics, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00767378

C. Chipot and A. Pohorille, Free Energy Calculations: Theory and Applications in Chemistry and Biology, 2007.

T. Lelievre, G. Stoltz, and M. Rousset, Free Energy Computations: A Mathematical Perspective, 2010.

B. Roux and T. Simonson, Implicit solvent models, Biophys. Chem, vol.78, p.1, 1999.

X. Kong and C. L. Brooks, Lambda-dynamics: A new approach to free energy calculations, J. Chem. Phys, vol.105, p.2414, 1996.

Z. Y. Guo, J. Durkin, T. Fischmann, R. Ingram, A. Prongay et al., Application of the lambda-dynamics method to evaluate the relative binding free energies of inhibitors to HCV protease, J. Med. Chem, vol.46, p.5360, 2003.

R. L. Hayes, K. A. Armacost, J. Z. Vilseck, and C. L. Brooks, Adaptive landscape flattening accelerates sampling of alchemical space in multisite lambda dynamics, J. Phys. Chem. B, vol.121, p.3626, 2017.

A. Laio and M. Parrinello, Escaping free-energy minima, Proc. Natl. Acad. Sci. U. S. A, vol.99, p.12562, 2002.

A. Laio and F. Gervasio, Metadynamics: A method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science, Rep. Prog. Phys, vol.71, p.126601, 2008.

A. Barducci, G. Bussi, and M. Parrinello, Well-tempered metadynamics: A smoothly converging and tunable free-energy method, Phys. Rev. Lett, vol.100, p.20603, 2008.

J. F. Dama, M. Parrinello, and G. A. Voth, Well-tempered metadynamics converges asymptotically, Phys. Rev. Lett, vol.112, p.240602, 2014.

F. G. Wang and D. P. Landau, Efficient, multiple-range random walk algorithm to calculate the density of states, Phys. Rev. Lett, vol.86, p.2050, 2001.

B. A. Berg and T. Neuhaus, Multicanonical ensemble: A new approach to simulate 1st-order phase transitions, Phys. Rev. Lett, vol.68, p.9, 1992.

N. Nakajima, H. Nakamura, and A. Kidera, Multicanonical ensemble generated by molecular dynamics simulation for enhanced conformational sampling of peptides, J. Phys. Chem. B, vol.101, p.817, 1997.

C. Bartels, M. Schaefer, and M. Karplus, Determination of equilibrium properties of biomolecular systems using multidimensional adaptive umbrella sampling, J. Chem. Phys, vol.111, p.8048, 1999.

F. Lou and P. Clote, Thermodynamics of RNA structures by Wang-Landau sampling, Bioinformatics, vol.26, p.278, 2010.

K. Druart, J. Bigot, E. Audit, and T. Simonson, A hybrid Monte Carlo method for multibackbone protein design, J. Chem. Theory Comput, vol.12, p.6035, 2017.

T. Simonson, T. Gaillard, D. Mignon, M. Schmidt, A. Busch et al., Computational protein design: The Proteus software and selected applications, J. Comput. Chem, vol.34, p.2472, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00868677

K. Druart, Z. Palmai, E. Omarjee, and T. Simonson, Protein:ligand binding free energies: A stringent test for computational protein design, J. Comput. Chem, vol.37, p.404, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01276168

A. Bhattacherjee and S. Wallin, Exploring protein-peptide binding specificity through computational peptide screening, PLoS Comput. Biol, vol.9, p.1003277, 2013.

X. Yang and J. G. Saven, Computational methods for protein design and protein sequence variability: Biased Monte Carlo and replica exchange, Chem. Phys. Lett, vol.401, p.205, 2005.

D. Mignon and T. Simonson, Comparing three stochastic search algorithms for computational protein design: Monte Carlo, replica exchange Monte Carlo, and a multistart, steepest-descent heuristic, J. Comput. Chem, vol.37, p.1781, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01445473

S. Polydorides, E. Michael, D. Mignon, K. Druart, G. Archontis et al., Proteus and the design of ligand binding sites, Methods in Molecular Biology: Design and Creation of Ligand Binding Proteins, vol.1414, pp.77-97, 2016.

. Villa, J. Chem. Phys, vol.149, p.72302, 2018.

S. Polydorides and T. Simonson, Monte Carlo simulations of proteins at constant pH with generalized Born solvent, flexible sidechains, and an effective dielectric boundary, J. Comput. Chem, vol.34, p.2742, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00984643

E. Michael, S. Polydorides, T. Simonson, and G. Archontis, Simple models for nonpolar solvation: Parametrization and testing, J. Comput. Chem, vol.38, p.2509, 2017.

F. Villa, D. Mignon, S. Polydorides, and T. Simonson, Comparing pairwiseadditive and many-body generalized born models for acid/base calculations and protein design, J. Comput. Chem, vol.38, p.2396, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01964492

A. E. Mertens, R. C. Roovers, and J. G. Collard, Regulation of Tiam1-Rac signalling, FEBS Lett, vol.546, p.11, 2003.

T. R. Shepherd, S. M. Klaus, X. Liu, S. Ramaswamy, K. A. Demali et al., The Tiam1 PDZ domain couples to syndecan1 and promotes cell-matrix adhesion, J. Mol. Biol, vol.398, p.730, 2010.

B. I. Dahiyat and S. L. Mayo, De novo protein design: Fully automated sequence selection, Science, vol.278, p.82, 1997.

T. R. Shepherd, R. L. Hard, A. M. Murray, D. Pei, and E. J. Fuentes, Distinct ligand specificity of the Tiam1 and Tiam2 PDZ domains, Biochemistry, vol.50, p.1296, 2011.

N. Panel, Y. J. Sun, E. J. Fuentes, and T. Simonson, A simple PB/LIE free energy function accurately predicts the peptide binding specificity of the Tiam1 PDZ domain, Front. Mol. Biosci, vol.4, p.65, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01961715

N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys, vol.21, p.1087, 1953.

D. Frenkel and B. Smit, Understanding Molecular Simulation, 1996.

G. R. Grimmett and D. R. Stirzaker, Probability and Random Processes, 2001.

X. Liu, T. R. Shepherd, A. M. Murray, Z. Xu, and E. J. Fuentes, The structure of the Tiam1 PDZ domain/phospho-syndecan1 complex reveals a ligand conformation that modulates protein dynamics, Structure, vol.21, 2013.

P. Tuffery, C. Etchebest, S. Hazout, and R. Lavery, A new approach to the rapid determination of protein side chain conformations, J. Biomol. Struct. Dyn, vol.8, p.1267, 1991.
URL : https://hal.archives-ouvertes.fr/hal-00313445

A. T. Brünger, X-Plor Version 3.1: A System for X-Ray Crystallography and NMR, 1992.

W. Cornell, P. Cieplak, C. Bayly, I. Gould, K. Merz et al., A second generation force field for the simulation of proteins, nucleic acids, and organic molecules, J. Am. Chem. Soc, vol.117, p.5179, 1995.

G. D. Hawkins, C. Cramer, and D. Truhlar, Pairwise descreening of solute charges from a dielectric medium, Chem. Phys. Lett, vol.246, p.122, 1995.
DOI : 10.1016/0009-2614(95)01082-k

A. Lopes, A. Aleksandrov, C. Bathelt, G. Archontis, and T. Simonson, Computational sidechain placement and protein mutagenesis with implicit solvent models, Proteins, vol.67, p.853, 2007.
DOI : 10.1002/prot.21379

M. Schmidt, A. Busch, D. Lopes, T. Mignon, and . Simonson, Computational protein design: Software implementation, parameter optimization, and performance of a simple model, J. Comput. Chem, vol.29, p.1092, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00488192

T. Gaillard and T. Simonson, Pairwise decomposition of an MMGBSA energy function for computational protein design, J. Comput. Chem, vol.35, p.1371, 2014.
DOI : 10.1002/jcc.23637
URL : https://hal.archives-ouvertes.fr/hal-01048588

N. Panel, F. Villa, E. J. Fuentes, and T. Simonson, Accurate PDZ:peptide binding specificity with additive and polarizable free energy simulations, Biophys. J, vol.114, p.1091, 2018.
DOI : 10.1016/j.bpj.2018.01.008
URL : https://hal.archives-ouvertes.fr/hal-01974145

J. B. Abrams and M. E. Tuckerman, Efficient and direct generation of multidimensional free energy surfaces via adiabatic dynamics without coordinate transformations, The Journal of Physical Chemistry B, vol.112, p.65, 2008.

B. Aguilar, R. Shadrach, and A. V. Onufriev, Reducing the secondary structure bias in the generalized born model via r6 effective radii, Journal of Chemical Theory and Computation, vol.6, pp.3613-3630, 2010.

R. A. Alberty and R. N. Goldberg, Standard thermodynamic formation properties for the adenosine 5, 1992.
DOI : 10.1021/bi00158a025

, ? -triphosphate series, Biochemistry, vol.31, pp.10610-10615

R. F. Alford, A. Leaver-fay, J. R. Jeliazkov, M. J. O'meara, F. P. Dimaio et al., The rosetta all-atom energy function for macromolecular modeling and design, Journal of Chemical Theory and Computation, vol.13, p.16, 2017.

D. Allouche, I. Andre, S. Barbe, J. Davies, S. De-givry et al., Computational protein design as an optimization problem, Artificial Intelligence, vol.212, p.16, 2014.
DOI : 10.1016/j.artint.2014.03.005
URL : https://hal.archives-ouvertes.fr/hal-01268554

V. M. Anisimov, G. Lamoureux, I. V. Vorobyov, N. Huang, B. Roux et al., Determination of electrostatic parameters for a polarizable force field based on the classical drude oscillator, Journal of Chemical Theory and Computation, vol.1, p.99, 2005.

J. Applequist, J. Carl, and K. Fung, Atom dipole interaction model for molecular polarizability. application to polyatomic molecules and determination of atom polarizabilities, Journal of American Chemical Society, vol.94, p.45, 1972.
DOI : 10.1021/ar50111a002

G. Archontis and T. Simonson, A residue-pairwise generalized born scheme suitable for protein design calculations, The Journal of Physical Chemistry B, vol.109, pp.22667-22673, 2005.
DOI : 10.1021/jp055282+
URL : https://hal.archives-ouvertes.fr/hal-00770118

B. Baker and C. M. , Polarizable force fields for molecular dynamics simulations of biomolecules, Wiley Interdisciplinary Reviews: Computational Molecular Science, vol.5, p.49, 2015.
DOI : 10.1002/wcms.1215

A. Barducci, G. Bussi, and M. Parrinello, Well-tempered metadynamics: A smoothly converging and tunable free-energy method, Physical Review Letters, vol.100, p.39, 2008.
DOI : 10.1103/physrevlett.100.020603
URL : http://arxiv.org/pdf/0803.3861

D. C. Bas, D. M. Rogers, and J. H. Jensen, Very fast prediction and rationalization of pKa values for protein-ligand complexes, Proteins: Structure, Function, and Bioinformatics, vol.73, p.36, 2008.
DOI : 10.1002/prot.22102
URL : https://onlinelibrary.wiley.com/doi/pdf/10.1002/prot.22102

N. Basdevant, H. Weinstein, and M. Ceruso, Thermodynamic basis for promiscuity and selectivity in protein-protein interactions: PDZ domains, a case study, Journal of the American Chemical Society, vol.128, pp.12766-12777, 2006.
DOI : 10.1021/ja060830y
URL : https://hal.archives-ouvertes.fr/hal-00157806

G. Bendas and L. Borsig, Cancer cell adhesion and metastasis: Selectins, integrins, and the inhibitory potential of heparins, International Journal of Cell Biology, vol.2012, pp.1-10, 2012.
DOI : 10.1155/2012/676731
URL : http://downloads.hindawi.com/journals/ijcb/2012/676731.pdf

C. H. Bennett, Efficient estimation of free energy differences from monte carlo data, Journal of Computational Physics, vol.22, p.63, 1976.

T. C. Beutler, A. E. Mark, R. C. Van-schaik, P. R. Gerber, and W. F. Van-gunsteren, Avoiding singularities and numerical instabilities in free energy calculations based on molecular simulations, Chemical Physics Letters, vol.222, p.75, 1994.

N. Blöchliger, M. Xu, and A. Caflisch, Peptide binding to a pdz domain by electrostatic steering via nonnative salt bridges, Biophysical Journal, vol.108, pp.2362-2370, 2015.

H. Böhm and G. Schneider, Protein-ligand interactions: From molecular recognition to drug design. Wiley, Methods and Principles in Medicinal Chemistry, 2003.

S. Boresch, F. Tettinger, M. Leitgeb, and M. Karplus, Absolute binding free energies: a quantitative approach for their calculation, The Journal of Physical Chemistry B, vol.107, p.66, 2003.

C. Böttcher, Theory of electric polarization, p.48, 1973.

S. Boyce, D. Mobley, G. Rocklin, A. Graves, K. Dill et al., Predicting ligand binding affinity with alchemical free energy methods in a polar model binding site, Journal of Molecular Biology, vol.394, pp.747-763, 2009.

P. Cieplak, J. Caldwell, and P. Kollman, Molecular mechanical models for organic and biological systems going beyond the atom centered two body additive approximation: aqueous solution free energies of methanol and n-methyl acetamide, nucleic acid base, and amide hydrogen bonding and chloroform/water partition coefficients of the nucleic acid bases, Journal of Computational Chemistry, vol.22, p.49, 2001.

M. A. Cuendet and M. E. Tuckerman, Free energy reconstruction from metadynamics or adiabatic free energy dynamics simulations, Journal of Chemical Theory and Computation, vol.10, p.65, 2014.

B. I. Dahiyat and S. L. Mayo, De novo protein design: Fully automated sequence selection, Science, vol.278, p.13, 1997.

T. Darden, Computational biochemistry and biophysics, treatment of long-range forces and potential, vol.74, p.107, 2001.

K. Debiec, A. Gronenborn, and L. Chong, Evaluating the strength of salt bridges: A comparison of current biomolecular force fields, Journal of Physical Chemistry B, vol.118, p.44, 2014.

Y. Deng and B. Roux, Calculation of standard binding free energies: Aromatic molecules in the t4 lysozyme l99a mutant, Journal of Chemical Theory and Computation, vol.2, p.44, 2006.

Y. Ding, B. Chen, S. Wang, L. Zhao, J. Chen et al., Overexpression of tiam1 in hepatocellular carcinomas predicts poor prognosis of HCC patients, International Journal of Cancer, vol.124, pp.653-658, 2009.

K. Druart, Z. Palmai, E. Omarjee, and T. Simonson, Protein:ligand binding free energies: A stringent test for computational protein design, Journal of Computational Chemistry, vol.37, p.24, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01276168

R. L. Dunbrack and M. Karplus, Backbone-dependent rotamer library for proteins application to side-chain prediction, Journal of Molecular Biology, vol.230, pp.543-574, 1993.

A. Ernst, D. Gfeller, Z. Kan, S. Seshagiri, P. M. Kim et al., Coevolution of PDZ domain-ligand interactions analyzed by high-throughput phage display and deep sequencing, Molecular BioSystems, vol.6, 1782.

B. Feller, S. E. Zhang, Y. Pastor, R. W. Brooks, and B. R. , Constant pressure molecular dynamics simulation: The langevin piston method, The Journal of Chemical Physics, vol.103, p.107, 1995.

M. H. Feng, M. Philippopoulos, A. D. Mackerell, and C. Lim, Structural characterization of the phosphotyrosine binding region of a high-affinity SH2 domain-phosphopeptide complex by molecular dynamics simulation and chemical shift calculations, Journal of the American Chemical Society, vol.118, p.131, 1996.

F. Figueirido, G. S. Buono, and R. M. Levy, On finite-size corrections to the free energy of ionic hydration, The Journal of Physical Chemistry B, vol.101, p.69, 1997.

A. V. Finkelstein and O. B. Ptitsyn, Theory of protein molecule self-organization. i. thermodynamic parameters of local secondary structures in the unfolded protein chain, Biopolymers, vol.16, pp.469-495, 1977.

A. Fiser, R. Do, and A. Svali, Modeling of loops in protein structures, Protein Science, vol.9, p.73, 2000.

M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb et al., Gaussian09 revision e01, p.95, 2009.

T. Gaillard and T. Simonson, Pairwise decomposition of an MMGBSA energy function for computational protein design, Journal of Computational Chemistry, vol.35, pp.1371-1387, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01048588

I. J. General, A note on the standard state's binding free energy, Journal of Chemical Theory and Computation, vol.6, p.59, 2010.

E. Harder and B. Roux, On the origin of the electrostatic potential difference at a liquid-vacuum interface, The Journal of Chemical Physics, vol.129, p.82, 2008.

J. S. Cheng, X. Lee, J. Kim, S. Park, S. J. Patel et al., CHARMM-GUI 10 years for biomolecular modeling and simulation, Journal of Computational Chemistry, vol.38, pp.1114-1124, 2016.

W. Jorgensen and L. Thomas, Perspective on free energy perturbation calculations for chemical equilibria, Journal of Chemical Theory and Computation, vol.4, pp.869-876, 2008.

W. Jorgensen, D. Maxwell, and J. Tirado-rives, Development and testing of the opls all-atom force field on conformational energetics and properties of organic liquids, Journal of American Chemical Society, vol.118, p.43, 1996.

G. A. Khoury, R. C. Baliban, and C. A. Floudas, Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database, Scientific Reports, vol.1, p.91, 2011.

H. Kokubo, T. Tanaka, and Y. Okamoto, Two-dimensional replica-exchange method for predicting protein-ligand binding structures, Journal of Computational Chemistry, vol.34, p.89, 2013.

P. Kollman, Free energy calculations: Applications to chemical and biochemical phenomena, Chemical Reviews, vol.93, pp.2395-2417, 1993.

G. G. Krivov, M. V. Shapovalov, and R. L. Dunbrack, Improved prediction of protein sidechain conformations with scwrl4, Proteins: Structure, Function, and Bioinformatics, vol.77, p.73, 2009.

M. Kumar, T. Simonson, G. Ohanessian, and C. Clavaguéra, Structure and thermodynamics of mg:phosphate interactions in water: A simulation study, ChemPhysChem, vol.16, p.122, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01111619

M. Kumar, T. Simonson, G. Ohanessian, and C. Clavaguéra, Corrigendum: Structure and thermodynamics of mg:phosphate interactions in water: A simulation study, ChemPhysChem, vol.19, p.122, 2018.

L. Lagardère, L. H. Jolly, F. Lipparini, F. Aviat, B. Stamm et al., Tinker-HP: a massively parallel molecular dynamics package for multiscale simulations of large complex systems with advanced point dipole polarizable force fields, Chemical Science, vol.9, pp.956-972, 2018.

A. Laio and M. Parrinello, Escaping free-energy minima, Proceedings of the National Academy of Sciences, vol.99, p.89, 2002.

G. Lamoureux and B. Roux, Modeling induced polarization with classical drude oscillators: Theory and molecular dynamics simulation algorithm, Journal of Chemical Physics, vol.119, p.45, 2003.

G. Lamoureux, A. Mackerell, and B. Roux, A simple polarizable model of water based on classical drude oscillators, Journal of Chemical Physics, vol.119, p.52, 2003.

G. Lamoureux, E. Harder, I. Vorobyov, B. Roux, and A. Mackerell, A polarizable model of water for molecular dynamics simulations of biomolecules, Chemical Physics Letters, vol.418, p.82, 2006.

T. Lazaridis and M. Karplus, Effective energy function for proteins in solution, Proteins: Structure, Function, and Genetics, vol.35, p.12, 1999.

J. Lemkul and A. Mackerell, Balancing the interactions of mg2+ in aqueous solution and with nucleic acid moieties for a polarizable force field based on the classical drude oscillator model, Journal of Physical Chemistry B, vol.120, p.134, 2016.

J. Lemkul, J. Huang, B. Roux, and A. Mackerell, An empirical polarizable force field based on the classical drude oscillator model: Development history and recent applications, Chemical Reviews, vol.116, p.94, 2016.

J. A. Lemkul and A. D. Mackerell, Balancing the interactions of mg2 2+ in aqueous solution and with nucleic acid moieties for a polarizable force field based on the classical drude oscillator model, The Journal of Physical Chemistry B, vol.120, p.122, 2016.

I. Letunic and P. Bork, 20 years of the SMART protein domain annotation resource, Nucleic Acids Research, vol.46, pp.493-496, 2017.

Y. L. Lin, A. Aleksandrov, T. Simonson, and B. Roux, An overview of electrostatic free energy computations for solutions and proteins, Journal of Chemical Theory and Computation, vol.10, p.83, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01057807

K. Lindorff-larsen, S. Piana, R. Dror, and . S. De, How fast-folding proteins fold, Nature, vol.28, p.43, 2011.

X. Liu, T. R. Shepherd, A. M. Murray, Z. Xu, and E. J. Fuentes, The structure of the tiam1 PDZ domain/ phospho-syndecan1 complex reveals a ligand conformation that modulates protein dynamics, Structure, vol.21, p.91, 2013.

A. D. Mackerell, D. Bashford, M. Bellott, R. L. Dunbrack, J. D. Evanseck et al., All-atom empirical potential for molecular modeling and dynamics studies of proteins ?, The Journal of Physical Chemistry B, vol.102, p.43, 1998.

J. Maier, C. Martinez, K. Kasavajhala, L. Wickstrom, K. Hauser et al., ff14sb: Improving the accuracy of protein side chain and backbone parameters from ff99sb, Journal of Chemical Theory and Computation, vol.11, p.43, 2015.

L. Maragliano and E. Vanden-eijnden, A temperature accelerated method for sampling free energy and determining reaction pathways in rare events simulations, Chemical Physics Letters, vol.426, p.65, 2006.

G. J. Martyna, D. J. Tobias, and M. L. Klein, Constant pressure molecular dynamics algorithms, The Journal of Chemical Physics, vol.101, p.55, 1994.

N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, Equation of state calculations by fast computing machines, The Journal of Chemical Physics, vol.21, pp.1087-1092, 1953.

K. J. Miller, Additivity methods in molecular polarizability, Journal of the American Chemical Society, vol.112, p.103, 1990.

M. E. Minard, L. M. Ellis, and G. E. Gallick, Tiam1 regulates cell adhesion, migration and apoptosis in colon tumor cells, Clinical & Experimental Metastasis, vol.23, pp.301-313, 2006.

J. Neely and R. Connick, Rate of water exchange from hydrated magnesium ion, Journal of the American Chemical Society, vol.92, p.119, 1970.

C. Oostenbrink, A. Villa, A. Mark, and W. 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.25, pp.1656-1676, 2004.

N. Panel, Y. J. Sun, E. J. Fuentes, and T. Simonson, A simple PB/LIE free energy function accurately predicts the peptide binding specificity of the tiam1 PDZ domain, Frontiers in Molecular Biosciences 4. cited pages, vol.36, p.38, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01961715

N. Panel, F. Villa, E. J. Fuentes, and T. Simonson, Accurate PDZ/peptide binding specificity with additive and polarizable free energy simulations, Biophysical Journal, vol.114, p.134, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01974145

S. Patel and C. L. Brooks, CHARMM fluctuating charge force field for proteins: I parameterization and application to bulk organic liquid simulations, Journal of Computational Chemistry, vol.25, p.45, 2003.

S. Patel, A. D. Mackerell, and C. L. Brooks, CHARMM fluctuating charge force field for proteins: II protein/solvent properties from molecular dynamics simulations using a nonadditive electrostatic model, Journal of Computational Chemistry, vol.25, p.45, 2004.
DOI : 10.1002/jcc.20077

T. Pawson and J. D. Scott, Protein phosphorylation in signaling -50 years and counting, Trends in Biochemical Sciences, vol.30, p.91, 2005.
DOI : 10.1016/j.tibs.2005.04.013

J. C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid et al., Scalable molecular dynamics with NAMD, Journal of Computational Chemistry, vol.26, p.49, 2005.
DOI : 10.1002/jcc.20289
URL : http://europepmc.org/articles/pmc2486339?pdf=render

S. Polydorides and T. Simonson, Monte carlo simulations of proteins at constant pH with generalized born solvent, flexible sidechains, and an effective dielectric boundary, Journal of Computational Chemistry, vol.34, p.25, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00984643

J. W. Ponder and F. M. Richards, Tertiary templates for proteins, Journal of Molecular Biology, vol.193, pp.775-791, 1987.
DOI : 10.1016/0022-2836(87)90358-5

R. Qi, Z. Jing, C. Liu, J. P. Piquemal, K. N. Dalby et al., Elucidating the phosphate binding mode of phosphate-binding protein: The critical effect of buffer solution, The Journal of Physical Chemistry B, vol.122, p.93, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02126853

X. Qin, F. Hayashi, and S. Y. , Solution structure of the PDZ domain of t-cell lymphoma invasion and metastasis 1 varian, 2006.

J. Reiland, V. L. Ott, C. S. Lebakken, C. Yeanman, J. Mccarty et al., Pervanadate activation of intracellular kinases leads to tyrosine phosphorylation and shedding of syndecan-1, Biochemical Journal, vol.319, p.91, 1996.

P. Bibliography-ren and J. W. Ponder, Polarizable atomic multipole water model for molecular mechanics simulation, Journal of Physical Chemistry B, vol.104, p.49, 2003.

G. J. Rocklin, D. L. Mobley, K. A. Dill, and P. H. Hünenberger, Calculating the binding free energies of charged species based on explicit-solvent simulations employing lattice-sum methods: An accurate correction scheme for electrostatic finite-size effects, The Journal of Chemical Physics, vol.139, p.69, 2013.

L. Rosso, P. Mináry, Z. Zhu, and M. E. Tuckerman, On the use of the adiabatic molecular dynamics technique in the calculation of free energy profiles, The Journal of Chemical Physics, vol.116, p.65, 2002.

J. P. Ryckaert, G. Ciccotti, and H. J. Berendsen, Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes, Journal of Computational Physics, vol.23, p.107, 1977.

P. Satpati, C. Clavaguera, G. Ohanessian, and T. Simonson, Free energy simulations of a gtpase: Gtp and gdp binding to archaeal initiation factor 2, The Journal of Physical Chemistry B, vol.115, p.123, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00764855

A. Savelyev and A. D. Mackerell, All-atom polarizable force field for DNA based on the classical drude oscillator model, Journal of Computational Chemistry, vol.35, p.121, 2014.
DOI : 10.1002/jcc.23611
URL : http://europepmc.org/articles/pmc4075971?pdf=render

M. Schaefer and M. Karplus, A comprehensive analytical treatment of continuum electrostatics, The Journal of Physical Chemistry, vol.100, p.10, 1996.
DOI : 10.1021/jp9521621

J. Schultz, SMART: a web-based tool for the study of genetically mobile domains, Nucleic Acids Research, vol.28, pp.231-234, 2000.

O. Sensoy and H. Weinstein, A mechanistic role of helix 8 in GPCRs: Computational modeling of the dopamine d2 receptor interaction with the GIPC1-PDZ-domain, Biochimica et Biophysica Acta (BBA) -Biomembranes, vol.1848, pp.976-983, 2015.

T. R. Shepherd, S. M. Klaus, X. Liu, S. Ramaswamy, K. A. Demali et al., The tiam1 PDZ domain couples to syndecan1 and promotes cell-matrix adhesion, Journal of Molecular Biology, vol.398, pp.3-72, 2010.
DOI : 10.1016/j.jmb.2010.03.047
URL : http://europepmc.org/articles/pmc5489123?pdf=render

T. R. Shepherd, R. L. Hard, A. M. Murray, D. Pei, and E. J. Fuentes, Distinct ligand specificity of the tiam1 and tiam2 PDZ domains, Biochemistry, vol.50, p.92, 2011.

Y. Shi, Z. Xia, J. Zhang, R. Best, C. Wu et al., Polarizable atomic multipole-based amoeba force field for proteins, Journal of Chemical Theory and Computation, vol.9, p.44, 2013.
DOI : 10.1021/ct4003702
URL : http://europepmc.org/articles/pmc3806652?pdf=render

L. Silberstein, Molecular refrectivity and atomic interaction, Philosophical Magazine, vol.33, p.45, 1917.

T. Simonson, Electrostatics and dynamics of proteins, Reports on Progress in Physics, vol.66, pp.737-787, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00770715

T. Simonson and B. Roux, Concepts and protocols for electrostatic free energies, Molecular Simulation, vol.42, p.83, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01445477

T. Simonson, G. Archontis, and M. Karplus, Free energy simulations come of age: Proteinligand recognition, Accounts of Chemical Research, vol.35, pp.430-437, 2002.

T. Simonson, J. Carlsson, and D. A. Case, Proton binding to proteins: pKaCalculations with explicit and implicit solvent models, Journal of the American Chemical Society, vol.126, p.25, 2004.

T. Simonson, T. Gaillard, D. Mignon, M. S. Busch, A. Lopes et al., Computational protein design: The proteus software and selected applications, Journal of Computational Chemistry, vol.34, p.16, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00868677

T. Simonson, S. Ye-lehmann, Z. Palmai, N. Amara, S. Wydau-dematteis et al., Redesigning the stereospecificity of tyrosyl-tRNA synthetase, Proteins: Structure, Function, and Bioinformatics, vol.84, p.86, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01315193

Z. Songyang, Recognition of unique carboxyl-terminal motifs by distinct PDZ domains, Science, vol.275, pp.73-77, 1997.

I. Spiegel, D. Salomon, B. Erne, N. Schaeren-wiemers, and E. Peles, Caspr3 and caspr4, two novel members of the caspr family are expressed in the nervous system and interact with pdz domains, Mol. Cell. Neurosci, vol.20, pp.283-297, 2002.

S. Steiner and A. Caflisch, Peptide binding to the pdz3 domain by conformational selection, Proteins: Structure, Function, and Bioinformatics, vol.80, pp.2562-2572, 2012.

M. A. Stiffler, J. R. Chen, V. P. Grantcharova, Y. Lei, D. Fuchs et al., PDZ domain binding selectivity is optimized across the mouse proteome, Science, vol.317, pp.364-369, 2007.

W. C. Still, A. Tempczyk, R. C. Hawley, and T. Hendrickson, Semianalytical treatment of solvation for molecular mechanics and dynamics, Journal of the American Chemical Society, vol.112, p.10, 1990.

T. P. Straatsma and J. A. Mccammon, Computational alchemy, Annual Review of Physical Chemistry, vol.43, pp.407-435, 1992.

A. G. Street and S. L. Mayo, Pairwise calculation of protein solvent-accessible surface areas, Folding and Design, vol.3, pp.253-258, 1998.

B. Sulka, H. Lortat-jacob, R. Terreux, F. Letourneur, and P. Rousselle, Tyrosine dephosphorylation of the syndecan-1 PDZ binding domain regulates syntenin-1 recruitment, Journal of Biological Chemistry, vol.284, p.91, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01063046

B. Thole, Molecular polarizabilities calculated with a modified dipole interaction, Chemical Physics, vol.59, p.80, 1981.

F. Tian, Y. Lv, P. Zhou, and L. Yang, Characterization of pdz domain-peptide interactions using an integrated protocol of qm/mm, pb/sa, and cfea analyses, Journal of ComputerAided Molecular Design, vol.25, pp.947-958, 2011.

R. Tonikian, Y. Zhang, S. L. Sazinsky, B. Currell, J. H. Yeh et al., A specificity map for the PDZ domain family, PLoS Biology, vol.6, 2008.

S. Traoré, D. Allouche, I. André, S. De-givry, G. Katsirelos et al., A new framework for computational protein design through cost function network optimization, Bioinformatics, vol.29, p.16, 2013.

M. E. Tuckerman, Free energy calculations: Theory and applications in chemistry and biology. springer series in chemical physics, 86 edited by christophe chipot and andrew pohorille, Journal of the American Chemical Society, vol.129, pp.10963-10964, 2007.

P. Tuffery, C. Etchebest, S. Hazout, and R. Lavery, A new approach to the rapid determination of protein side chain conformations, Journal of Biomolecular Structure and Dynamics, vol.8, pp.1267-1289, 1991.
URL : https://hal.archives-ouvertes.fr/hal-00313445

J. M. Turney, A. C. Simmonett, R. M. Parrish, E. G. Hohenstein, F. A. Evangelista et al., Psi4: an open-source ab initio electronic structure program, Wiley Interdisciplinary Reviews: Computational Molecular Science, vol.2, p.95, 2011.
DOI : 10.1002/wcms.93

K. Vanommeslaeghe, E. Hatcher, C. Acharya, S. Kundu, S. Zhong et al., CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields, Journal of Computational Chemistry NA-NA, p.96, 2009.

R. M. Verbeeck, P. A. Bruyne, F. C. Driessens, and F. Verbeek, Solubility of magnesium hydrogen phosphate trihydrate and ion-pair formation in the system magnesium hydroxide-phosphoric acid-water at 25 ? c, Inorganic Chemistry, vol.23, p.123, 1984.

F. Villa, D. Mignon, S. Polydorides, and T. Simonson, Comparing pairwise-additive and many-body generalized born models for acid/base calculations and protein design, Journal of Computational Chemistry, vol.38, p.21, 2017.
DOI : 10.1002/jcc.24898
URL : https://hal.archives-ouvertes.fr/hal-01964492

F. Villa, A. Mackerell, B. Roux, and T. Simonson, Classical drude polarizable force field model for methyl phosphate and its interactions with mg2+, The Journal of Physical Chemistry A, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01975114

F. Villa, N. Panel, X. Chen, and T. Simonson, Adaptive landscape flattening in amino acid sequence space for the computational design of protein:peptide binding, The Journal of Chemical Physics, vol.149, p.40, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01975456

L. Wernisch, S. Hery, and S. J. Wodak, Automatic protein design with all atom force-fields by exact and heuristic optimization 1 1edited by j. thorton, Journal of Molecular Biology, vol.301, p.15, 2000.

U. Wiedemann, P. Boisguerin, R. Leben, D. Leitner, G. Krause et al., Quantification of PDZ domain specificity, prediction of ligand affinity and rational design of super-binding peptides, Journal of Molecular Biology, vol.343, pp.703-718, 2004.

W. Yu, P. Lopes, B. Roux, and A. Mackerell, Six-site polarizable model of water based on the classical drude oscillator, Journal of Chemical Physics, vol.138, p.89, 2013.

B. Zacharias, M. Straatsma, T. P. Mccammon, and J. A. , Separation-shifted scaling, a new scaling method for lennard-jones interactions in thermodynamic integration, The Journal of Chemical Physics, vol.100, p.75, 1994.

L. Zhao, Y. Liu, X. Sun, M. He, and Y. Ding, Overexpression of t lymphoma invasion and metastasis 1 predict renal cell carcinoma metastasis and overall patient survival, Journal of Cancer Research and Clinical Oncology, vol.137, pp.393-398, 2010.

L. Zheng, M. Chen, and W. Yang, Random walk in orthogonal space to achieve efficient free-energy simulation of complex systems, Proceedings of the National Academy of Sciences, vol.105, p.89, 2008.

R. W. Zwanzig, High-temperature equation of state by a perturbation method. i. nonpolar gases, The Journal of Chemical Physics, vol.22, pp.1420-1426, 1954.

, Résumé Les interactions protéine-protéine (IPPs) médient la signalisation cellulaire. Leur ingénie-rie peut fournir des informations et conduire au développement de molécules thérapeutiques

, Leur caractérisation avec des modèles physiques est complexe à cause des nombreux événements qui ont lieu après le binding: changements de conformation, réorganisation des molécules de solvant

, Le travail de thèse est focalisé sur les domaines PDZ, importants médiateurs de IPPs

, Elles lient aussi les peptides correspondants, qui peuvent servir de systèmes modèle ou d'inhibiteurs. Nous avons développé deux approches computationnelles basé sur des modèles physiques et les avons appliquées au domaine PDZ de la protéine Tiam1, un facteur d'échange pour la protéine Rac, impliqué dans la protrusion neuronale. Sa cible est la protéine Syndecan1. Des affinités expérimentales sont connues pour le peptide C-terminal, noté Sdc1, et plusieurs mutants, Elles lient les 4-10 résidus C-terminaux de protéines cibles

, Contrairement aux différentes méthodes computationnelles existantes, les nouveaux modèles permettent l'étude d'un grand nombre de variants de protéines (grands ensembles des séquences et des structures) et de réduire significativement les temps de calcul. Nous avons appliqué notre méthodes pour de dessin computationnel haut débit de Sdc1 dans le complexe Tiam1-Sdc1. Une simulation Monte Carlo est faite où les chaines latérales de la protéine et du peptide peuvent changer de conformères et certaines positions peuvent muter. Le solvant est implicite. Le paysage énergétique est applati par la méthode adaptative de Wang-Landau, de sorte qu'un vaste ensemble de variants est échantillonné. Effectuant des simulations distinctes du complexe et du peptide seul nous avons obtenu les énergies libres relatives d'association de 75,000 variants en heure CPU sur une machine de bureau, Les valeurs sont compatibles avec les quelques données expérimentales disponibles. Notre modèle haut débit est implémenté dans la nouvelle version de Proteus