An experimentally derived confidence score for binary protein-protein interactions, Nat Methods, vol.6, issue.1, pp.91-98, 2009. ,
Predicting biological networks from genomic data, FEBS Lett, vol.582, issue.8, pp.1251-1259, 2008. ,
Computational methods for the prediction of protein interactions, Curr Opin Struct Biol, vol.12, issue.3, pp.368-73, 2002. ,
Assigning protein functions by comparative genome analysis: protein phylogenetic profiles, Proc Natl Acad Sci, vol.96, issue.8, pp.4285-4293, 1999. ,
,
Essentials of cell biology, In: Cambridge, MA: NPG Education, vol.1, p.3, 2010. ,
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.
Principles that govern the folding of protein chains, In: Science, vol.181, p.5, 1973. ,
The protein data bank, In: Nucleic acids research, vol.28, p.5, 2000. ,
Announcing the worldwide protein data bank, Nature Structural & Molecular Biology, vol.10, p.980, 2003. ,
Emerging methods in protein co-evolution, Nature Reviews Genetics, vol.14, p.5, 2013. ,
UniProt: the universal protein knowledgebase, In: Nucleic acids research, vol.32, pp.115-119, 2004. ,
Biological sequence analysis: Probabilistic models of proteins and nucleic acids, vol.25, pp.6-8, 1998. ,
HMMER web server: interactive sequence similarity searching, Nucleic acids research, vol.39, pp.29-37, 2011. ,
HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment, Nature methods, vol.9, issue.2, p.173, 2012. ,
Detection of conserved evolutionary units by profile hidden Markov Models (HMM), vol.8 ,
HMMER user's guide ,
Where did the BLOSUM62 alignment score matrix come from?, In: Nature biotechnology, vol.22, p.1035, 2004. ,
The Pfam protein families database, Nucleic acids research, vol.32, p.9, 2004. ,
URL : https://hal.archives-ouvertes.fr/hal-01294685
A comprehensive biophysical description of pairwise epistasis throughout an entire protein domain, Current Biology, vol.24, p.10, 2014. ,
Inferring protein 3D structure from deep mutation scans, Nature Genetics, p.1, 2019. ,
Deep mutational scanning: a new style of protein science, Nature methods, vol.11, p.33, 2014. ,
Correlated mutations and residue contacts in proteins, In: Proteins: Structure, Function, and Bioinformatics, vol.18, pp.309-317, 1994. ,
Ab initio folding of proteins using restraints derived from evolutionary information, Proteins: Structure, Function, and Bioinformatics, vol.37, p.177, 1999. ,
Ab initio folding of proteins using restraints derived from evolutionary information, Proteins: Structure, Function, and Bioinformatics, vol.37, p.11, 1999. ,
Influence of conservation on calculations of amino acid covariance in multiple sequence alignments, Proteins: Structure, Function, and Bioinformatics, vol.56, p.11, 2004. ,
Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction, In: Bioinformatics, vol.24, p.11, 2007. ,
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. ,
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
On the various contrivances by which British and foreign orchids are fertilised by insects, John Murray, p.11 ,
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
Functional connectivity models for decoding of spatial representations from hippocampal CA1 recordings, Journal of Computational Neuroscience, vol.43, p.13, 2017. ,
Weak pairwise correlations imply strongly correlated network states in a neural population, Nature, vol.440, p.13, 2006. ,
Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information, In: Elife, vol.3, 2014. ,
Scale-free correlations in starling flocks, Proceedings of the National Academy of Sciences, vol.107, p.13, 2010. ,
The STARFLAG handbook on collective animal behaviour: Part I, empirical methods, p.13, 2008. ,
Inverse statistical problems: from the inverse Ising problem to data science, In: Advances in Physics, vol.66, p.13, 2017. ,
Information theory and statistical mechanics, In: Physical review, vol.106, p.14, 1957. ,
How pairwise coevolutionary models capture the collective residue variability in proteins?, In: Molecular biology and evolution, vol.35, p.23, 2018. ,
Inferring contacting residues within and between proteins: what do the probabilities mean?, In: PLoS computational biology, vol.12, p.17, 2016. ,
The maximum entropy fallacy redux?, In: PLoS computational biology, vol.12, p.17, 2016. ,
DCA for genome-wide epistasis analysis: the statistical genetics perspective, In: Physical biology, vol.36, p.17, 2019. ,
A learning algorithm for Boltzmann machines, Cognitive science, vol.9, p.20, 1985. ,
From residue coevolution to protein conformational ensembles and functional dynamics, Proceedings of the National Academy of Sciences, vol.112, p.21, 2015. ,
Structural propensities of kinase family proteins from a Potts model of residue co-variation, In: Protein Science, vol.25, p.21, 2016. ,
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
Statistical analysis of non-lattice data, In: Journal of the Royal Statistical Society: Series D (The Statistician), vol.24, p.22, 1975. ,
High-dimensional Ising model selection using l1-regularized logistic regression, In: The Annals of Statistics, vol.38, p.22, 2010. ,
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. ,
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
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. ,
Disentangling direct from indirect co-evolution of residues in protein alignments, PLoS computational biology, vol.6, p.27, 2010. ,
Inverse statistical physics of protein sequences: a key issues review, In: Reports on Progress in Physics, vol.81, pp.34-36, 2018. ,
Protein structure determination using metagenome sequence data, In: Science, vol.355, p.29, 2017. ,
Learning generative models for protein fold families, Proteins: Structure, Function, and Bioinformatics, vol.79, p.29, 2011. ,
Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age, Proteins: Structure, Function, and Bioinformatics, vol.86, p.29, 2018. ,
DESTINI: A deep-learning approach to contact-driven protein structure prediction, In: Scientific reports, vol.9, p.30, 2019. ,
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. ,
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
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
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
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
Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design, In: Immunity, vol.38, p.33, 2013. ,
Coevolutionary information, protein folding landscapes, and the thermodynamics of natural selection, Proceedings of the National Academy of Sciences, vol.111, p.33, 2014. ,
Mutation effects predicted from sequence covariation, Nature biotechnology, vol.35, p.33, 2017. ,
Context-aware prediction of pathogenicity of missense mutations involved in human disease, vol.88, p.33, 2017. ,
Evolutionary information for specifying a protein fold, Nature, vol.437, p.34, 2005. ,
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
ACE: adaptive cluster expansion for maximum entropy graphical model inference, Bioinformatics, vol.32, p.34, 2016. ,
SuperDCA for genome-wide epistasis analysis, In: Microbial genomics, vol.4, p.36, 2018. ,
Interacting networks of resistance, virulence and core machinery genes identified by genomewide epistasis analysis, PLoS genetics, vol.13, p.36, 2017. ,
Genome-wide discovery of epistatic loci affecting antibiotic resistance in Neisseria gonorrhoeae using evolutionary couplings, Nature microbiology, vol.4, p.36, 2019. ,
The landscape of coadaptation in Vibrio parahaemolyticus, bioRxiv (2019), p.36 ,
A comprehensive two-hybrid analysis to explore the yeast protein interactome, Proceedings of the National Academy of Sciences 98, vol.8, p.41, 2001. ,
Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry, Nature, vol.415, p.41, 2002. ,
An experimentally derived confidence score for binary protein-protein interactions, Nature methods, vol.6, p.41, 2009. ,
Assigning protein functions by comparative genome analysis: protein phylogenetic profiles, Proceedings of the National Academy of Sciences 96, vol.8, p.41, 1999. ,
Using phylogenetic profiles to predict functional relationships, p.41, 2012. ,
Sequence co-evolution gives 3D contacts and structures of protein complexes, In: Elife, vol.3, p.65, 2014. ,
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. ,
Deep learning reveals many more inter-protein residue-residue contacts than direct coupling analysis, In: bioRxiv, p.240754, 2018. ,
Accurate de novo prediction of protein contact map by ultradeep learning model, PLoS computational biology, vol.13, 2017. ,
De novo structure prediction with deeplearning based scoring, Annu Rev Biochem, vol.77, p.66, 2018. ,
AlphaFold at CASP13, In: Bioinformatics, p.66, 2019. ,
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins, In: Bioinformatics, vol.31, p.67, 2014. ,
A review on deep convolutional neural networks, 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE. 2017, p.66 ,
Improved contact predictions using the recognition of protein like contact patterns, PLoS computational biology, vol.10, p.66, 2014. ,
3did: a catalog of domain-based interactions of known three-dimensional structure, Nucleic acids research, vol.42, p.69, 2013. ,
A series of PDB related databases for everyday needs, Nucleic acids research 39.suppl_1 (2010), p.71 ,
Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features, Biopolymers: Original Research on Biomolecules, vol.22, p.71, 1983. ,
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
PconsC4: fast, accurate and hassle-free contact predictions, In: Bioinformatics, vol.119, p.79, 2018. ,
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. ,
The roles of mutation, inbreeding, crossbreeding, and selection in evolution, vol.1, p.85, 1932. ,
Rock-paper-scissors: Engineered population dynamics increase genetic stability, In: Science, vol.365, p.85, 2019. ,
Longterm dynamics of adaptation in asexual populations, In: Science, vol.342, p.89, 2013. ,
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. ,
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. ,
Epistasis as the primary factor in molecular evolution, Nature, vol.490, p.90, 2012. ,
Evolutionary biochemistry: revealing the historical and physical causes of protein properties, Nature Reviews Genetics, vol.14, p.90, 2013. ,
Empirical fitness landscapes and the predictability of evolution, Nature Reviews Genetics, vol.15, p.90, 2014. ,
Pervasive degeneracy and epistasis in a protein-protein interface, Science, vol.347, p.90, 2015. ,
What can we learn from over 100,000 Escherichia coli genomes?, In: bioRxiv, p.92, 2019. ,
U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computerassisted intervention, p.120, 2015. ,
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
The style was inspired by Robert Bringhurst's seminal book on typography "The Elements of Typographic Style". Final Version as of, 2019. ,