P. Braun, M. Tasan, M. Dreze, M. Barrios-rodiles, I. Lemmens et al., An experimentally derived confidence score for binary protein-protein interactions, Nat Methods, vol.6, issue.1, pp.91-98, 2009.

E. D. Harrington, L. J. Jensen, and P. Bork, Predicting biological networks from genomic data, FEBS Lett, vol.582, issue.8, pp.1251-1259, 2008.

A. Valencia and F. Pazos, Computational methods for the prediction of protein interactions, Curr Opin Struct Biol, vol.12, issue.3, pp.368-73, 2002.

M. Pellegrini, E. M. Marcotte, M. J. Thompson, D. Eisenberg, and T. O. Yeates, Assigning protein functions by comparative genome analysis: protein phylogenetic profiles, Proc Natl Acad Sci, vol.96, issue.8, pp.4285-4293, 1999.

. B-i-b-l-i-o-g-r-a-p-h-y,

M. Clare, J. U. Connor, J. Adams, and . Fairman, Essentials of cell biology, In: Cambridge, MA: NPG Education, vol.1, p.3, 2010.

O. Rodolfo, M. Esquivel, F. Molina-espiritu, C. Salas, C. Soriano et al., Decoding the Building Blocks of Life from the Perspective of Quantum Information, Advances in Quantum Mechanics. IntechOpen, p.4, 2013.

, Wikipedia contributors. Protein structure -Wikipedia, The Free Encyclopedia, 2004.

. Christian-b-anfinsen, Principles that govern the folding of protein chains, In: Science, vol.181, p.5, 1973.

M. Helen, J. Berman, Z. Westbrook, G. Feng, . Gilliland et al., The protein data bank, In: Nucleic acids research, vol.28, p.5, 2000.

H. Berman, K. Henrick, and H. Nakamura, Announcing the worldwide protein data bank, Nature Structural & Molecular Biology, vol.10, p.980, 2003.

F. David-de-juan, A. Pazos, and . Valencia, Emerging methods in protein co-evolution, Nature Reviews Genetics, vol.14, p.5, 2013.

R. Apweiler, UniProt: the universal protein knowledgebase, In: Nucleic acids research, vol.32, pp.115-119, 2004.

R. Durbin, S. Eddy, A. S. Krogh, and G. Mitchison, Biological sequence analysis: Probabilistic models of proteins and nucleic acids, vol.25, pp.6-8, 1998.

J. Robert-d-finn, S. R. Clements, and . Eddy, HMMER web server: interactive sequence similarity searching, Nucleic acids research, vol.39, pp.29-37, 2011.

M. Remmert, A. Biegert, A. Hauser, and J. Söding, HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment, Nature methods, vol.9, issue.2, p.173, 2012.

S. El-gebali, L. Richardson, and R. Finn, Detection of conserved evolutionary units by profile hidden Markov Models (HMM), vol.8

S. Eddy, HMMER user's guide

R. Sean and . Eddy, Where did the BLOSUM62 alignment score matrix come from?, In: Nature biotechnology, vol.22, p.1035, 2004.

A. Bateman, The Pfam protein families database, Nucleic acids research, vol.32, p.9, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01294685

A. Olson, C. Nicholas, R. Wu, and . Sun, A comprehensive biophysical description of pairwise epistasis throughout an entire protein domain, Current Biology, vol.24, p.10, 2014.

J. Nathan, . Rollins, P. Kelly, . Brock, J. Frank et al., Inferring protein 3D structure from deep mutation scans, Nature Genetics, p.1, 2019.

M. Douglas, S. Fowler, and . Fields, Deep mutational scanning: a new style of protein science, Nature methods, vol.11, p.33, 2014.

U. Göbel, C. Sander, R. Schneider, and A. Valencia, Correlated mutations and residue contacts in proteins, In: Proteins: Structure, Function, and Bioinformatics, vol.18, pp.309-317, 1994.

A. Angel-r-ortiz, P. Kolinski, B. Rotkiewicz, J. Ilkowski, and . Skolnick, Ab initio folding of proteins using restraints derived from evolutionary information, Proteins: Structure, Function, and Bioinformatics, vol.37, p.177, 1999.

A. Angel-r-ortiz, P. Kolinski, B. Rotkiewicz, J. Ilkowski, and . Skolnick, Ab initio folding of proteins using restraints derived from evolutionary information, Proteins: Structure, Function, and Bioinformatics, vol.37, p.11, 1999.

A. Anthony and R. Fodor, Influence of conservation on calculations of amino acid covariance in multiple sequence alignments, Proteins: Structure, Function, and Bioinformatics, vol.56, p.11, 2004.

L. M. Stanley-d-dunn, G. B. Wahl, and . Gloor, Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction, In: Bioinformatics, vol.24, p.11, 2007.

M. Weigt, A. Robert, H. White, . Szurmant, A. James et al., Identification of direct residue contacts in protein-protein interaction by message passing, In: Proceedings of the National Academy of Sciences, vol.106, pp.67-72, 2009.

F. Morcos, Direct-coupling analysis of residue coevolution captures native contacts across many protein families, Proceedings of the National Academy of Sciences, vol.22, pp.25-27, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01589010

C. Darwin, On the various contrivances by which British and foreign orchids are fertilised by insects, John Murray, p.11

U. Ferrari, S. Deny, M. Chalk, G. Tka?ik, O. Marre et al., Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons, Physical Review E, vol.98, p.13, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01912303

L. Posani and S. Cocco, Functional connectivity models for decoding of spatial representations from hippocampal CA1 recordings, Journal of Computational Neuroscience, vol.43, p.13, 2017.

E. Schneidman, I. I. Berry, R. Segev, and W. Bialek, Weak pairwise correlations imply strongly correlated network states in a neural population, Nature, vol.440, p.13, 2006.

S. Ovchinnikov, H. Kamisetty, and D. Baker, Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information, In: Elife, vol.3, 2014.

A. Cavagna, A. Cimarelli, I. Giardina, G. Parisi, R. Santagati et al., Scale-free correlations in starling flocks, Proceedings of the National Academy of Sciences, vol.107, p.13, 2010.

A. Cavagna, I. Giardina, A. Orlandi, G. Parisi, A. Procaccini et al., The STARFLAG handbook on collective animal behaviour: Part I, empirical methods, p.13, 2008.

R. H-chau-nguyen, J. Zecchina, and . Berg, Inverse statistical problems: from the inverse Ising problem to data science, In: Advances in Physics, vol.66, p.13, 2017.

T. Edwin and . Jaynes, Information theory and statistical mechanics, In: Physical review, vol.106, p.14, 1957.

M. Figliuzzi, P. Barrat-charlaix, and M. Weigt, How pairwise coevolutionary models capture the collective residue variability in proteins?, In: Molecular biology and evolution, vol.35, p.23, 2018.

E. Van-nimwegen, Inferring contacting residues within and between proteins: what do the probabilities mean?, In: PLoS computational biology, vol.12, p.17, 2016.

E. Aurell, The maximum entropy fallacy redux?, In: PLoS computational biology, vol.12, p.17, 2016.

C. Gao, F. Cecconi, A. Vulpiani, H. Zhou, and E. Aurell, DCA for genome-wide epistasis analysis: the statistical genetics perspective, In: Physical biology, vol.36, p.17, 2019.

H. David, G. E. Ackley, T. J. Hinton, and . Sejnowski, A learning algorithm for Boltzmann machines, Cognitive science, vol.9, p.20, 1985.

L. Sutto, S. Marsili, A. Valencia, and F. L. Gervasio, From residue coevolution to protein conformational ensembles and functional dynamics, Proceedings of the National Academy of Sciences, vol.112, p.21, 2015.

A. Haldane, F. William, P. Flynn, . He, R. Vijayan et al., Structural propensities of kinase family proteins from a Potts model of residue co-variation, In: Protein Science, vol.25, p.21, 2016.

P. Barrat-charlaix, M. Figliuzzi, and M. Weigt, Improving landscape inference by integrating heterogeneous data in the inverse Ising problem, In: Scientific reports, vol.6, p.21, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01405150

J. Besag, Statistical analysis of non-lattice data, In: Journal of the Royal Statistical Society: Series D (The Statistician), vol.24, p.22, 1975.

P. Ravikumar, J. Martin, J. D. Wainwright, and . Lafferty, High-dimensional Ising model selection using l1-regularized logistic regression, In: The Annals of Statistics, vol.38, p.22, 2010.

M. Ekeberg, T. Hartonen, and E. Aurell, Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences, In: Journal of Computational Physics, vol.276, pp.341-356, 2014.

M. Ekeberg, C. Lövkvist, Y. Lan, M. Weigt, and E. Aurell, Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models, In: Physical Review E, vol.87, p.12707, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01528418

P. John, S. Barton, . Cocco, R. De-leonardis, and . Monasson, Large pseudocounts and l2-norm penalties are necessary for the mean-field inference of Ising and Potts models, In: Physical Review E, vol.90, p.24, 2014.

L. Burger and E. Van-nimwegen, Disentangling direct from indirect co-evolution of residues in protein alignments, PLoS computational biology, vol.6, p.27, 2010.

S. Cocco, C. Feinauer, M. Figliuzzi, R. Monasson, and M. Weigt, Inverse statistical physics of protein sequences: a key issues review, In: Reports on Progress in Physics, vol.81, pp.34-36, 2018.

S. Ovchinnikov, H. Park, N. Varghese, P. Huang, A. Georgios et al., Protein structure determination using metagenome sequence data, In: Science, vol.355, p.29, 2017.

S. Balakrishnan, H. Kamisetty, G. Jaime, S. Carbonell, C. J. Lee et al., Learning generative models for protein fold families, Proteins: Structure, Function, and Bioinformatics, vol.79, p.29, 2011.

J. Schaarschmidt, B. Monastyrskyy, A. Kryshtafovych, and . Bonvin, Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age, Proteins: Structure, Function, and Bioinformatics, vol.86, p.29, 2018.

M. Gao, H. Zhou, and J. Skolnick, DESTINI: A deep-learning approach to contact-driven protein structure prediction, In: Scientific reports, vol.9, p.30, 2019.

T. Gueudré, C. Baldassi, M. Zamparo, M. Weigt, and A. Pagnani, Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis, Proceedings of the National Academy of Sciences, vol.113, pp.12186-12191, 2016.

A. Bitbol, R. S. Dwyer, L. J. Colwell, and N. S. Wingreen, Inferring interaction partners from protein sequences, Proceedings of the National Academy of Sciences, vol.113, p.31, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01636994

C. Feinauer, H. Szurmant, M. Weigt, and A. Pagnani, Inter-protein sequence co-evolution predicts known physical interactions in bacterial ribosomes and the Trp operon, PloS one, vol.11, p.31, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01294651

G. Uguzzoni, J. Shalini, F. Lovis, A. Oteri, H. Schug et al., Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis, Proceedings of the National Academy of Sciences, vol.114, p.33, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01566982

M. Figliuzzi, H. Jacquier, A. Schug, O. Tenaillon, and M. Weigt, Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1, In: Molecular biology and evolution, vol.33, pp.87-89, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01284957

J. K. Andrew-l-ferguson, S. Mann, T. Omarjee, . Ndung'u, D. Bruce et al., Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design, In: Immunity, vol.38, p.33, 2013.

F. Morcos, P. Nicholas, R. R. Schafer, . Cheng, N. José et al., Coevolutionary information, protein folding landscapes, and the thermodynamics of natural selection, Proceedings of the National Academy of Sciences, vol.111, p.33, 2014.

A. Thomas, J. B. Hopf, . Ingraham, J. Frank, . Poelwijk et al., Mutation effects predicted from sequence covariation, Nature biotechnology, vol.35, p.33, 2017.

C. Feinauer and M. Weigt, Context-aware prediction of pathogenicity of missense mutations involved in human disease, vol.88, p.33, 2017.

M. Socolich, W. Steve, . Lockless, P. William, H. Russ et al., Evolutionary information for specifying a protein fold, Nature, vol.437, p.34, 2005.

S. Cocco and R. Monasson, Adaptive cluster expansion for inferring Boltzmann machines with noisy data, In: Physical review letters, vol.106, p.34, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00566281

P. John, E. D. Barton, A. Leonardis, S. Coucke, and . Cocco, ACE: adaptive cluster expansion for maximum entropy graphical model inference, Bioinformatics, vol.32, p.34, 2016.

M. Santeri-puranen, J. Pesonen, Y. Y. Pensar, J. A. Xu, . Lees et al., SuperDCA for genome-wide epistasis analysis, In: Microbial genomics, vol.4, p.36, 2018.

J. Marcin and . Skwark, Interacting networks of resistance, virulence and core machinery genes identified by genomewide epistasis analysis, PLoS genetics, vol.13, p.36, 2017.

B. Schubert, R. Maddamsetti, J. Nyman, R. Maha, D. S. Farhat et al., Genome-wide discovery of epistatic loci affecting antibiotic resistance in Neisseria gonorrhoeae using evolutionary couplings, Nature microbiology, vol.4, p.36, 2019.

Y. Cui, C. Yang, H. Qiu, H. Wang, R. Yang et al., The landscape of coadaptation in Vibrio parahaemolyticus, bioRxiv (2019), p.36

T. Ito, T. Chiba, R. Ozawa, M. Yoshida, M. Hattori et al., A comprehensive two-hybrid analysis to explore the yeast protein interactome, Proceedings of the National Academy of Sciences 98, vol.8, p.41, 2001.

Y. Ho, Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry, Nature, vol.415, p.41, 2002.

P. Braun, An experimentally derived confidence score for binary protein-protein interactions, Nature methods, vol.6, p.41, 2009.

M. Pellegrini, M. Edward, . Marcotte, J. Michael, D. Thompson et al., Assigning protein functions by comparative genome analysis: protein phylogenetic profiles, Proceedings of the National Academy of Sciences 96, vol.8, p.41, 1999.

M. Pellegrini, Using phylogenetic profiles to predict functional relationships, p.41, 2012.

A. Thomas, C. P. Hopf, . Schärfe, P. João, A. G. Rodrigues et al., Sequence co-evolution gives 3D contacts and structures of protein complexes, In: Elife, vol.3, p.65, 2014.

D. Malinverni, S. Marsili, A. Barducci, and P. Rios, Large-scale conformational transitions and dimerization are encoded in the amino-acid sequences of Hsp70 chaperones, PLoS computational biology, vol.11, issue.6, p.65, 2015.

T. Zhou, S. Wang, and J. Xu, Deep learning reveals many more inter-protein residue-residue contacts than direct coupling analysis, In: bioRxiv, p.240754, 2018.

S. Wang, S. Sun, Z. Li, R. Zhang, and J. Xu, Accurate de novo prediction of protein contact map by ultradeep learning model, PLoS computational biology, vol.13, 2017.

R. Evans, De novo structure prediction with deeplearning based scoring, Annu Rev Biochem, vol.77, p.66, 2018.

M. Alquraishi, AlphaFold at CASP13, In: Bioinformatics, p.66, 2019.

T. David, T. Jones, T. Singh, S. Kosciolek, and . Tetchner, MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins, In: Bioinformatics, vol.31, p.67, 2014.

N. Aloysius and . Geetha, A review on deep convolutional neural networks, 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE. 2017, p.66

J. Marcin, D. Skwark, M. Raimondi, A. Michel, and . Elofsson, Improved contact predictions using the recognition of protein like contact patterns, PLoS computational biology, vol.10, p.66, 2014.

R. Mosca, A. Ceol, A. Stein, R. Olivella, and P. Aloy, 3did: a catalog of domain-based interactions of known three-dimensional structure, Nucleic acids research, vol.42, p.69, 2013.

T. Robbie-p-joosten, E. Beek, . Krieger, L. Maarten, . Hekkelman et al., A series of PDB related databases for everyday needs, Nucleic acids research 39.suppl_1 (2010), p.71

W. Kabsch and C. Sander, Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features, Biopolymers: Original Research on Biomolecules, vol.22, p.71, 1983.

F. Pedregosa, Scikit-learn: Machine Learning in Python, In: Journal of Machine Learning Research, vol.12, p.77, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

M. Michel, D. M. Hurtado, and A. Elofsson, PconsC4: fast, accurate and hassle-free contact predictions, In: Bioinformatics, vol.119, p.79, 2018.

A. Couce, L. V. Caudwell, C. Feinauer, T. Hindré, J. Feugeas et al., Mutator genomes decay, despite sustained fitness gains, in a longterm experiment with bacteria, Proceedings of the National Academy of Sciences, vol.114, pp.9026-9035, 2017.

S. Wright, The roles of mutation, inbreeding, crossbreeding, and selection in evolution, vol.1, p.85, 1932.

J. Michael, . Liao, L. Omar-din, J. Tsimring, and . Hasty, Rock-paper-scissors: Engineered population dynamics increase genetic stability, In: Science, vol.365, p.85, 2019.

J. Michael, N. Wiser, R. E. Ribeck, and . Lenski, Longterm dynamics of adaptation in asexual populations, In: Science, vol.342, p.89, 2013.

H. Lee, E. Popodi, H. Tang, and P. L. Foster, Rate and molecular spectrum of spontaneous mutations in the bacterium Escherichia coli as determined by whole-genome sequencing, Proceedings of the National Academy of Sciences, vol.109, p.89, 2012.

L. Patricia, H. Foster, E. Lee, J. P. Popodi, H. Townes et al., Determinants of spontaneous mutation in the bacterium Escherichia coli as revealed by whole-genome sequencing, Proceedings of the National Academy of Sciences, vol.112, p.89, 2015.

C. Michael-s-breen, . Kemena, K. Peter, C. Vlasov, F. A. Notredame et al., Epistasis as the primary factor in molecular evolution, Nature, vol.490, p.90, 2012.

J. Michael, J. Harms, and . Thornton, Evolutionary biochemistry: revealing the historical and physical causes of protein properties, Nature Reviews Genetics, vol.14, p.90, 2013.

J. Visser and J. Krug, Empirical fitness landscapes and the predictability of evolution, Nature Reviews Genetics, vol.15, p.90, 2014.

I. Anna, M. T. Podgornaia, and . Laub, Pervasive degeneracy and epistasis in a protein-protein interface, Science, vol.347, p.90, 2015.

Z. Kaleb-z-zion-abram, C. Udaondo, V. Bleker, T. M. Wanchai, D. W. Wassenaar et al., What can we learn from over 100,000 Escherichia coli genomes?, In: bioRxiv, p.92, 2019.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computerassisted intervention, p.120, 2015.

C. Baldassi, M. Zamparo, C. Feinauer, A. Procaccini, R. Zecchina et al., Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners, PloS one, vol.9, p.120, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01344551

A. Miede, The style was inspired by Robert Bringhurst's seminal book on typography "The Elements of Typographic Style". Final Version as of, 2019.