.. Clash-free-directed-motion, 40 3.2.1 Dynamic Clash-avoiding Constraints, p.42

G. Convergence-in, 71 5.2.1 Elimination of dominated strategies, p.74

.. Local-convergence-towards-strict-equilibrium, 74 Logit map properties, p.76

.. Bandit, 94 6.2.1 -dynamic and -equilibrium, 96 Asymptotic pseudo trajectory . . . . . . . . . . . . 96 Global convergence with the continuous dynamics 97

J. S. Mattick and I. V. Makunin, Non-coding RNA, Human Molecular Genetics, vol.15, issue.suppl_1, pp.17-29, 2006.
DOI : 10.1093/hmg/ddl046

W. R. Taylor, The classification of amino acid conservation, Journal of Theoretical Biology, vol.119, issue.2, pp.205-218, 1986.
DOI : 10.1016/S0022-5193(86)80075-3

J. D. Watson and F. H. Crick, Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic Acid, Nature, vol.9, issue.4356, pp.737-738, 1953.
DOI : 10.1016/0006-3002(53)90232-7

D. Eisenberg, The discovery of the ?-helix and ?-sheet, the principal structural features of proteins, pp.11-207, 2003.

L. Pauling, R. B. Corey, and H. R. Branson, The structure of proteins: Two hydrogen-bonded helical configurations of the polypeptide chain, Proceedings of the National Academy of Sciences, vol.203, issue.4, pp.205-211, 1951.
DOI : 10.1021/cr60102a002

L. Schrödinger, The PyMOL Molecular Graphics System, Version 1.8 Schrödinger, LLC, 2016.

D. Pal and P. Chakrabarti, Cis peptide bonds in proteins: residues involved, their conformations, interactions and locations 1 1Edited by J. M. Thornton, Journal of Molecular Biology, vol.294, issue.1, pp.271-288, 1999.
DOI : 10.1006/jmbi.1999.3217

G. N. Ramachandran, C. Ramakrishnan, and V. Sasisekharan, Stereochemistry of polypeptide chain configurations, Journal of Molecular Biology, vol.7, issue.1, pp.95-99, 1963.
DOI : 10.1016/S0022-2836(63)80023-6

G. T. Ramachandran and V. Sasisekharan, Conformation of Polypeptides and Proteins, Advances in Protein Chemistry, vol.23, pp.283-437, 1968.
DOI : 10.1016/S0065-3233(08)60402-7

R. P. Joosten, T. A. Te-beek, E. Krieger, M. L. Hekkelman, R. W. Hooft et al., A series of PDB related databases for everyday needs, Nucleic Acids Research, vol.39, issue.Database, pp.411-419, 2011.
DOI : 10.1093/nar/gkq1105

L. J. Mcguffin, K. Bryson, and D. T. Jones, The PSIPRED protein structure prediction server, Bioinformatics, vol.16, issue.4, pp.404-405, 2000.
DOI : 10.1093/bioinformatics/16.4.404

S. Asai, K. Hayamizu, and . Handa, Prediction of protein secondary structure by the hidden Markov model, Bioinformatics, vol.9, issue.2, pp.141-146, 1993.
DOI : 10.1093/bioinformatics/9.2.141

F. A. Vendeix, A. M. Munoz, and P. F. Agris, Free energy calculation of modified base-pair formation in explicit solvent: A predictive model, RNA, vol.15, issue.12, pp.2278-2287, 2009.
DOI : 10.1261/rna.1734309

F. H. Crick, Codon???anticodon pairing: The wobble hypothesis, Journal of Molecular Biology, vol.19, issue.2, pp.548-555, 1966.
DOI : 10.1016/S0022-2836(66)80022-0

URL : http://profiles.nlm.nih.gov/SC/B/C/B/S/_/scbcbs.pdf

K. Darty, A. Denise, and Y. Ponty, VARNA: Interactive drawing and editing of the RNA secondary structure, Bioinformatics, vol.25, issue.15, pp.1974-1979, 2009.
DOI : 10.1093/bioinformatics/btp250

URL : https://hal.archives-ouvertes.fr/hal-00432548

H. Yang, F. Jossinet, N. Leontis, L. Chen, J. Westbrook et al., Tools for the automatic identification and classification of RNA base pairs, Nucleic Acids Research, vol.31, issue.13, pp.3450-3460, 2003.
DOI : 10.1093/nar/gkg529

N. B. Leontis and E. Westhof, Geometric nomenclature and classification of RNA base pairs, RNA, vol.7, issue.4, pp.499-512, 2001.
DOI : 10.1017/S1355838201002515

R. Nussinov, G. Pieczenik, J. Griggs, and D. Kleitman, Algorithms for Loop Matchings, SIAM Journal on Applied Mathematics, vol.35, issue.1, pp.68-82, 1978.
DOI : 10.1137/0135006

R. B. Lyngsø and C. N. Pedersen, RNA Pseudoknot Prediction in Energy-Based Models, Journal of Computational Biology, vol.7, issue.3-4, pp.409-427, 2000.
DOI : 10.1089/106652700750050862

M. Zuker and P. Stiegler, Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information, Nucleic Acids Research, vol.9, issue.1, pp.133-148, 1981.
DOI : 10.1093/nar/9.1.133

URL : https://academic.oup.com/nar/article-pdf/9/1/133/6201945/9-1-133.pdf

R. Lorenz, M. T. Wolfinger, A. Tanzer, and I. L. Hofacker, Predicting RNA secondary structures from sequence and probing data, Methods, vol.103, pp.86-98, 2016.
DOI : 10.1016/j.ymeth.2016.04.004

URL : https://hal.archives-ouvertes.fr/hal-01393649

D. H. Mathews and D. H. Turner, Prediction of RNA secondary structure by free energy minimization, Current Opinion in Structural Biology, vol.16, issue.3, pp.270-278, 2006.
DOI : 10.1016/j.sbi.2006.05.010

J. E. Tabaska, R. B. Cary, H. N. Gabow, and G. D. Stormo, An RNA folding method capable of identifying pseudoknots and base triples, Bioinformatics, vol.14, issue.8, pp.691-699, 1998.
DOI : 10.1093/bioinformatics/14.8.691

M. Dorn, M. B. Silva, L. S. Buriol, and L. C. Lamb, Three-dimensional protein structure prediction: Methods and computational strategies, Computational Biology and Chemistry, vol.53, pp.251-276, 2014.
DOI : 10.1016/j.compbiolchem.2014.10.001

C. Laing and T. Schlick, Analysis of Four-Way Junctions in RNA Structures, Journal of Molecular Biology, vol.390, issue.3, pp.547-559, 2009.
DOI : 10.1016/j.jmb.2009.04.084

A. Lescoute and E. Westhof, Topology of three-way junctions in folded RNAs, RNA, vol.12, issue.1, pp.83-93, 2006.
DOI : 10.1261/rna.2208106

URL : https://hal.archives-ouvertes.fr/hal-00094313

M. Djelloul and A. Denise, Automated motif extraction and classification in RNA tertiary structures, RNA, vol.14, issue.12, pp.2489-2497, 2008.
DOI : 10.1261/rna.1061108

URL : https://hal.archives-ouvertes.fr/hal-00353402

G. M. Salem, E. G. Hutchinson, C. A. Orengo, and J. M. Thornton, Correlation of observed fold frequency with the occurrence of local structural motifs, Journal of Molecular Biology, vol.287, issue.5, pp.969-981, 1999.
DOI : 10.1006/jmbi.1999.2642

C. A. Orengo, J. E. Bray, D. W. Buchan, A. Harrison, D. Lee et al., The CATH protein family database: A resource for structural and functional annotation of genomes, PROTEOMICS, vol.28, issue.1, pp.11-21, 2002.
DOI : 10.1110/ps.8.4.699

A. G. Murzin, S. E. Brenner, T. Hubbard, and C. Chothia, SCOP: A structural classification of proteins database for the investigation of sequences and structures, Journal of Molecular Biology, vol.247, issue.4, pp.536-540, 1995.
DOI : 10.1016/S0022-2836(05)80134-2

R. D. Finn, P. Coggill, R. Y. Eberhardt, S. R. Eddy, J. Mistry et al., The Pfam protein families database: towards a more sustainable future, Nucleic Acids Research, vol.44, issue.D1, pp.279-285, 2016.
DOI : 10.1093/nar/gkv1344

URL : https://hal.archives-ouvertes.fr/hal-01294685

H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat et al., The Protein Data Bank, Nucleic Acids Research, vol.28, issue.1, pp.235-242, 2000.
DOI : 10.1093/nar/28.1.235

URL : http://journals.iucr.org/d/issues/2002/06/01/an0594/an0594.pdf

J. J. Gray, S. Moughon, C. Wang, O. Schueler-furman, B. Kuhlman 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.
DOI : 10.1016/S0022-2836(03)00670-3

H. Zhou and Y. Shan, Prediction of protein interaction sites from sequence profile and residue neighbor list, Proteins: Structure, Function, and Genetics, vol.271, issue.3, pp.336-343, 2001.
DOI : 10.1006/jmbi.1997.1198

D. W. Ritchie, Recent Progress and Future Directions in Protein-Protein Docking, Current Protein & Peptide Science, vol.9, issue.1, pp.1-15, 2008.
DOI : 10.2174/138920308783565741

URL : https://hal.archives-ouvertes.fr/inria-00434268

J. Janin, S. J. Wodak, M. F. Lensink, and S. Velankar, Assessing Structural Predictions of Protein-Protein Recognition: The CAPRI Experiment, Reviews in Computational Chemistry, vol.60, issue.Web Server issu, pp.137-173, 2015.
DOI : 10.1002/prot.20551

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, en, Proteins, pp.4-14, 2016.
DOI : 10.1093/nar/gkt294

A. Mcpherson, Introduction to protein crystallization, pp.254-265, 2004.

D. Marion, An Introduction to Biological NMR Spectroscopy, Molecular & Cellular Proteomics, vol.8, issue.11, pp.3006-3025, 2013.
DOI : 10.1016/j.jmb.2007.07.008

URL : https://hal.archives-ouvertes.fr/hal-01321578

M. Billeter, A Consensus on Protein Structure Accuracy in NMR?, Structure, vol.23, issue.2, pp.255-256, 2015.
DOI : 10.1016/j.str.2015.01.007

X. Bai, G. Mcmullan, and S. H. Scheres, How cryo-EM is revolutionizing structural biology, Trends in Biochemical Sciences, vol.40, issue.1, pp.49-57, 2015.
DOI : 10.1016/j.tibs.2014.10.005

E. Callaway, The revolution will not be crystallized: a new method sweeps through structural biology, Nature, vol.525, issue.7568, p.172, 2015.
DOI : 10.1038/525172a

URL : http://www.nature.com:80/polopoly_fs/1.18335!/menu/main/topColumns/topLeftColumn/pdf/525172a.pdf

S. Yang, M. Parisien, F. Major, and B. Roux, RNA Structure Determination Using SAXS Data, The Journal of Physical Chemistry B, vol.114, issue.31, pp.1520-6106, 1021.
DOI : 10.1021/jp1057308

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3164809/pdf

D. Franke, C. M. Jeffries, D. I. Svergun-en, and N. Meth, Correlation Map, a goodnessof-fit test for one-dimensional X-ray scattering spectra, pp.419-422, 2015.
DOI : 10.1038/nmeth.3358

B. E. Suzek, Y. Wang, H. Huang, P. B. Mcgarvey, and C. H. Wu, UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches, Bioinformatics, vol.31, issue.6, pp.926-932, 2015.
DOI : 10.1093/bioinformatics/btu739

URL : https://academic.oup.com/bioinformatics/article-pdf/31/6/926/569379/btu739.pdf

C. B. Anfinsen, Principles that Govern the Folding of Protein Chains, Science, vol.181, issue.4096, pp.223-230, 1973.
DOI : 10.1126/science.181.4096.223

W. G. Noid, Perspective: Coarse-grained models for biomolecular systems, The Journal of Chemical Physics, vol.1, issue.9, pp.9-201, 2013.
DOI : 10.1063/1.3464776

URL : http://aip.scitation.org/doi/pdf/10.1063/1.4818908

S. Raman, R. Vernon, J. Thompson, M. Tyka, R. Sadreyev et al., Structure prediction for CASP8 with all-atom refinement using Rosetta, Proteins, pp.89-99, 2009.
DOI : 10.1002/(SICI)1097-0134(1999)37:3+<22::AID-PROT5>3.0.CO;2-W

URL : http://onlinelibrary.wiley.com/doi/10.1002/prot.22540/pdf

T. A. Wassenaar, K. Pluhackova, R. A. Böckmann, S. J. Marrink, and D. P. Tieleman, Going Backward: A Flexible Geometric Approach to Reverse Transformation from Coarse Grained to Atomistic Models, Journal of Chemical Theory and Computation, vol.10, issue.2, pp.676-690, 2014.
DOI : 10.1021/ct400617g

T. A. Jones, S. Thirup-eng, and E. J. , Using known substructures in protein model building and crystallography, pp.819-822, 1986.

M. Boudard, J. Bernauer, D. Barth, J. Cohen, and A. Denise, GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies, PLOS ONE, vol.19, issue.4, pp.1932-6203, 2015.
DOI : 10.1371/journal.pone.0136444.s025

URL : https://hal.archives-ouvertes.fr/hal-01201665

H. Kamisetty, S. Ovchinnikov, and D. Baker, Assessing the utility of coevolutionbased residue?residue contact predictions in a sequence-and structure-rich era, Proc Natl Acad Sci U S A, vol.110, issue.39, pp.15-674, 2013.

P. Bradley, K. M. Misura, and D. 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

J. Wang, R. M. Wolf, J. W. Caldwell, P. A. Kollman, and D. A. Case, Development and testing of a general amber force field, Journal of Computational Chemistry, vol.17, issue.9, pp.1157-1174, 2004.
DOI : 10.1002/jcc.20035

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, vol.249, issue.4, pp.671-690, 2010.
DOI : 10.1002/jcc.21367

M. Karplus and G. A. Petsko, Molecular dynamics simulations in biology, Nature, vol.347, issue.6294, pp.631-639, 1990.
DOI : 10.1038/347631a0

K. Henzler-wildman and D. Kern, Dynamic personalities of proteins, Nature, vol.124, issue.7172, pp.964-972, 2007.
DOI : 10.1038/nature06522

M. Tuckerman, Statistical mechanics: theory and molecular simulation, 2010.

E. C. Dykeman and O. F. Sankey, Normal mode analysis and applications in biological physics, Journal of Physics: Condensed Matter, vol.22, issue.42, pp.953-8984, 2010.
DOI : 10.1088/0953-8984/22/42/423202

S. M. Lavalle and J. J. Jr, Rapidly-exploring random trees: Progress and prospects, 2000.

I. Al-bluwi, T. Siméon, and J. Cortés, Motion planning algorithms for molecular simulations: A survey, Computer Science Review, vol.6, issue.4, pp.125-143, 2012.
DOI : 10.1016/j.cosrev.2012.07.002

P. Yao, L. Zhang, and J. Latombe, Sampling-based exploration of folded state of a protein under kinematic and geometric constraints, Proteins: Structure, Function, and Bioinformatics, vol.126, issue.2, pp.25-43, 2012.
DOI : 10.1063/1.2740261

D. Devaurs, M. Vaisset, T. Siméon, and J. Cortés, A multi-tree approach to compute transition paths on energy landscapes, Workshop on Artificial Intelligence and Robotics Methods in Computational Biology, AAAI'13, p.8, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00872247

D. Budday, R. Fonseca, S. Leyendecker, H. Van-den, and . Bedem, Frustration-guided motion planning reveals conformational transitions in proteins, Proteins: Structure, Function, and Bioinformatics, vol.25, issue.10, 2017.
DOI : 10.1016/j.str.2016.12.003

D. Devaurs, T. Siméon, and J. Cortés, Optimal Path Planning in Complex Cost Spaces With Sampling-Based Algorithms, IEEE Transactions on Automation Science and Engineering, vol.13, issue.2, pp.415-424, 2016.
DOI : 10.1109/TASE.2015.2487881

URL : https://hal.archives-ouvertes.fr/hal-01231482

J. R. Lopéz-blanco, J. I. Garzón, and P. Chacón, iMod: multipurpose normal mode analysis in internal coordinates, Bioinformatics, vol.27, issue.20, pp.2843-2850, 2011.
DOI : 10.1093/bioinformatics/btr497

A. Y. Sim, M. Levitt, and P. Minary, Modeling and design by hierarchical natural moves, Proceedings of the National Academy of Sciences, vol.682, issue.4, pp.2890-2895, 2012.
DOI : 10.1002/1097-0282(2000)56:4<275::AID-BIP10024>3.0.CO;2-E

URL : http://www.pnas.org/content/109/8/2890.full.pdf

J. , V. Neumann, and O. Morgenstern, Theory of games and economic behavior, 1947.

N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani, Algorithmic game theory, 2007.
DOI : 10.1017/CBO9780511800481

R. Savani and B. , Game Theory Explorer: software for the applied game theorist, Computational Management Science, vol.70, issue.1, pp.5-33, 2015.
DOI : 10.1111/1468-0262.00300

URL : http://arxiv.org/pdf/1403.3969.pdf

Y. Viossat and A. Zapechelnyuk, No-regret dynamics and fictitious play, Journal of Economic Theory, vol.148, issue.2, pp.825-842, 2013.
DOI : 10.1016/j.jet.2012.07.003

URL : https://hal.archives-ouvertes.fr/hal-00713871

R. J. Aumann, Subjectivity and correlation in randomized strategies, Journal of Mathematical Economics, vol.1, issue.1, pp.67-96, 1974.
DOI : 10.1016/0304-4068(74)90037-8

URL : http://www.ma.huji.ac.il/~raumann/pdf/Subjectivity and Correlation.pdf

D. P. Foster and R. Vohra, Regret in the On-Line Decision Problem, Games and Economic Behavior, vol.29, issue.1-2, pp.7-35, 1999.
DOI : 10.1006/game.1999.0740

S. Hart and A. Mas, A Simple Adaptive Procedure Leading to Correlated Equilibrium, pp.1127-1150, 2000.
DOI : 10.1111/1468-0262.00153

URL : http://www.dklevine.com/archive/refs4572.pdf

J. Hannan, Approximation to Bayes risk in repeated play, Contributions to the Theory of Games, pp.97-139, 1957.
DOI : 10.1515/9781400882151-006

S. Bubeck and N. Cesa-bianchi, Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems, Foundations and Trends?? in Machine Learning, vol.5, issue.1, pp.1-122, 2012.
DOI : 10.1561/2200000024

URL : http://arxiv.org/pdf/1204.5721.pdf

P. Auer, Using confidence bounds for exploitation-exploration trade-offs, Journal of Machine Learning Research, vol.3, pp.397-422, 2002.

Y. Freund and R. E. Schapire, Adaptive Game Playing Using Multiplicative Weights, Games and Economic Behavior, vol.29, issue.1-2, pp.79-103, 1999.
DOI : 10.1006/game.1999.0738

URL : http://www.cs.princeton.edu/~schapire/papers/FreundScYY.pdf

N. Littlestone and M. K. Warmuth, The Weighted Majority Algorithm, Information and Computation, vol.108, issue.2, pp.212-261, 1994.
DOI : 10.1006/inco.1994.1009

URL : https://doi.org/10.1006/inco.1994.1009

A. Héliou, D. Budday, R. Fonseca, H. Van-den, and . Bedem, Fast, Clash-Free RNA Conformational Morphing using Molecular Junctions, Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics , ACM-BCB '17, 2017.
DOI : 10.1145/3107411.3107460

H. Van-den-bedem, I. Lotan, J. Latombe, and A. M. Deacon, Real-space protein-model completion: an inverse-kinematics approach, Acta Crystallographica Section D Biological Crystallography, vol.61, issue.1, pp.2-13, 2005.
DOI : 10.1107/S0907444904025697

P. Yao, A. Dhanik, N. Marz, R. Propper, C. Kou et al., Efficient Algorithms to Explore Conformation Spaces of Flexible Protein Loops, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.5, issue.4, pp.534-545, 2008.
DOI : 10.1109/TCBB.2008.96

D. Budday, S. Leyendecker, H. Van-den, and . Bedem, Geometric analysis characterizes molecular rigidity in generic and non-generic protein configurations, Journal of the Mechanics and Physics of Solids, vol.83, pp.36-47, 2015.
DOI : 10.1016/j.jmps.2015.06.006

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4509548/pdf

R. Fonseca, D. V. Pachov, J. Bernauer, H. Van-den, and . Bedem, Characterizing RNA ensembles from NMR data with kinematic models, Nucleic Acids Research, vol.42, issue.15, 2014.
DOI : 10.1093/nar/gku707

URL : https://hal.archives-ouvertes.fr/hal-01058971

B. J. Bender, A. Cisneros, I. , A. M. Duran, J. A. Finn et al., Protocols for Molecular Modeling with Rosetta3 and RosettaScripts, Biochemistry, vol.55, issue.34, pp.4748-4763, 2016.
DOI : 10.1021/acs.biochem.6b00444

URL : http://doi.org/10.1021/acs.biochem.6b00444

P. J. Cock, T. Antao, J. T. Chang, B. A. Chapman, C. J. Cox et al., Biopython: freely available Python tools for computational molecular biology and bioinformatics, Bioinformatics, vol.25, issue.11, pp.1422-1423, 2009.
DOI : 10.1093/bioinformatics/btp163

URL : https://academic.oup.com/bioinformatics/article-pdf/25/11/1422/944180/btp163.pdf

A. Ren, Y. Xue, A. Peselis, A. Serganov, H. M. Hashimi et al., Structural and Dynamic Basis for Low-Affinity, High-Selectivity Binding of L-Glutamine by the Glutamine Riboswitch, Cell Reports, vol.13, issue.9, pp.1800-1813, 2015.
DOI : 10.1016/j.celrep.2015.10.062

X. Lu, H. J. Bussemaker, and W. K. Olson, DSSR: an integrated software tool for dissecting the spatial structure of RNA, Nucleic Acids Research, vol.43, issue.21, pp.142-142, 2015.
DOI : 10.1093/nar/gkv716

B. J. Tucker and R. R. Breaker, Riboswitches as versatile gene control elements, Current Opinion in Structural Biology, vol.15, issue.3, pp.342-348, 2005.
DOI : 10.1016/j.sbi.2005.05.003

T. D. Ames and R. R. Breaker, Bacterial aptamers that selectively bind glutamine, RNA Biology, vol.276, issue.1, pp.82-89, 2011.
DOI : 10.1261/rna.7214405

URL : http://www.tandfonline.com/doi/pdf/10.4161/rna.8.1.13864?needAccess=true

R. O. Dror, T. J. Mildorf, D. Hilger, A. Manglik, D. W. Borhani et al., Structural basis for nucleotide exchange in heterotrimeric G proteins, Science, vol.286, issue.5, pp.1361-1365, 2015.
DOI : 10.1074/jbc.M110.190496

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4968074/pdf

C. Roth, T. Dreyfus, C. H. Robert, and F. Cazals, Hybridizing rapidly exploring random trees and basin hopping yields an improved exploration of energy landscapes, Journal of Computational Chemistry, vol.124, issue.8, pp.739-752, 2016.
DOI : 10.1063/1.2148958

URL : https://hal.archives-ouvertes.fr/hal-01497994

J. R. Thomas and P. J. Hergenrother, Targeting RNA with Small Molecules, Chemical Reviews, vol.108, issue.4, pp.1171-1224, 2008.
DOI : 10.1021/cr0681546

M. H. Bailor, X. Sun, and H. M. , Topology Links RNA Secondary Structure with Global Conformation, Dynamics, and Adaptation, Science, vol.312, issue.5777, pp.202-206, 2010.
DOI : 10.1126/science.1128451

N. Furnham, T. L. Blundell, M. A. Depristo, and T. C. Terwilliger, Is one solution good enough?, Nature Structural & Molecular Biology, vol.10, issue.3, pp.184-185, 1038.
DOI : 10.1002/ijch.199400022

H. Van-den, A. Bedem, J. Dhanik, A. M. Latombe, and . Deacon, Modeling discrete heterogeneity in X-ray diffraction data by fitting multi-conformers, Acta Crystallographica Section D Biological Crystallography, vol.65, issue.10, pp.1107-1117, 2009.
DOI : 10.1107/S0907444909030613

K. Cowtan, The clipper project, Joint CCP4 and ESF-EACBM Newsletter on Protein Crystallography, vol.40, 2002.

I. I. Cplex, V12. 1: User's Manual for CPLEX, International Business Machines Corporation, vol.46, issue.53, p.157, 2009.

P. Hall and C. C. Heyde, Martingale limit theory and its application, en, 1980.

S. Kullback, R. A. Leibler, E. , and A. , On Information and Sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, pp.79-86, 1951.
DOI : 10.1214/aoms/1177729694

URL : http://doi.org/10.1214/aoms/1177729694

W. H. Sandholm, Population games and evolutionary dynamics, 2010.

R. Laraki and P. Mertikopoulos, Higher order game dynamics, Journal of Economic Theory, vol.148, issue.6, pp.2666-2695, 2013.
DOI : 10.1016/j.jet.2013.08.002

URL : https://hal.archives-ouvertes.fr/hal-01382303

R. T. Rockafellar, Convex analysis, ser. Princeton Landmarks in Mathematics, 1970.

S. Shalev-shwartz and . Others, Online Learning and Online Convex Optimization, Machine Learning, pp.107-194, 2012.
DOI : 10.1561/2200000018

P. Mertikopoulos and W. H. Sandholm, Learning in Games via Reinforcement and Regularization, Mathematics of Operations Research, vol.41, issue.4, pp.1297-1324, 2016.
DOI : 10.1287/moor.2016.0778

URL : https://hal.archives-ouvertes.fr/hal-01382286

M. Benaïm, Dynamics of stochastic approximation algorithms, Seminaire de probabilites XXXIII, pp.1-68, 1999.
DOI : 10.1007/978-1-4757-1947-5

J. C. Harsanyi, Oddness of the number of equilibrium points: A new proof, International Journal of Game Theory, vol.21, issue.1, pp.235-250, 1973.
DOI : 10.1007/BF01737572

S. Enterprises, Scilab: Free and Open Source software for numerical computation, Scilab Enterprises, p.3, 2012.

J. Hofbauer and W. H. Sandholm, Evolution in games with randomly disturbed payoffs, Journal of Economic Theory, vol.132, issue.1, pp.47-69, 2007.
DOI : 10.1016/j.jet.2005.05.011

URL : http://www.ssc.wisc.edu/%7Ewhs/research/rdp.pdf

M. Levitt and S. Lifson, Refinement of protein conformations using a macromolecular energy minimization procedure, Journal of Molecular Biology, vol.46, issue.2, pp.269-2790022, 1969.
DOI : 10.1016/0022-2836(69)90421-5

G. Wang and R. L. Dunbrack, PISCES: a protein sequence culling server, Bioinformatics, vol.19, issue.12, pp.1589-1591, 2003.
DOI : 10.1093/bioinformatics/btg224

URL : https://academic.oup.com/bioinformatics/article-pdf/19/12/1589/718266/btg224.pdf

P. C. Ng and S. Henikoff, SIFT: predicting amino acid changes that affect protein function, Nucleic Acids Research, vol.31, issue.13, pp.3812-3814, 2003.
DOI : 10.1093/nar/gkg509

URL : https://academic.oup.com/nar/article-pdf/31/13/3812/9487105/gkg509.pdf

K. Molloy, N. Buhours, M. Vaisset, T. Siméon, E. Ferré et al., A Reinforcement Learning Approach to Protein Loop Modeling, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01206128

F. Guyon and P. Tufféry, Fast protein fragment similarity scoring using a Binet???Cauchy kernel, Bioinformatics, vol.30, issue.6, pp.784-791, 2014.
DOI : 10.1093/bioinformatics/btt618

URL : https://academic.oup.com/bioinformatics/article-pdf/30/6/784/17345719/btt618.pdf