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Accueil Tous les numéros Volume 211 / Numéro 3 (2017) Biologie Aujourd'hui, 211 3 (2017) 239-244 Références
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Numéro
Biologie Aujourd'hui
Volume 211, Numéro 3, 2017
Page(s) 239 - 244
Section La biologie computationnelle parle à la biologie expérimentale
DOI https://doi.org/10.1051/jbio/2017030
Publié en ligne 7 février 2018
  • Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E. (2000). The Protein Data Bank. Nucleic Acids Res, 28, 235-242. [CrossRef] [PubMed] [Google Scholar]
  • Biasini, M., Bienert, S., Waterhouse, A., Arnold, K., Studer, G., Schmidt, T., Kiefer, F., Gallo Cassarino, T., Bertoni, M., Bordoli, L., Schwede, T. (2014). SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res, 42, W1, W252-W258. [Google Scholar]
  • Bitbol, A.F., Dwyer, R.S., Colwell, L.J., Wingreen, N.S. (2016). Inferring interaction partners from protein sequences. Proc Nat Acad Sci USA, 113, 12180-12185. [CrossRef] [Google Scholar]
  • Couce, A., Caudwell, L.V., Feinauer, C., Hindré, T., Feugeas, J.P., Weigt, M., Lenski, R.E., Schneider, D., Tenaillon, O. (2017). Mutator genomes decay, despite sustained fitness gains, in a long-term experiment with bacteria. Proc Nat Acad Sci USA, 114, E9026-E9035. [CrossRef] [Google Scholar]
  • Dago, A.E., Schug, A., Procaccini, A., Hoch, J.A., Weigt, M., Szurmant, H. (2012). Structural basis of histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and mutagenesis. Proc Nat Acad Sci USA, 109, E1733-E1742. [CrossRef] [Google Scholar]
  • De Juan, D., Pazos, F., Valencia A. (2013). Emerging methods in protein co-evolution. Nat Rev Genetics, 14, 249-261. [CrossRef] [Google Scholar]
  • Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G., Biological sequence analysis: probabilistic models of proteins and nucleic acids, Cambridge University Press, 1998. [Google Scholar]
  • Eddy, S. R. (1998). Profile hidden Markov models. Bioinformatics, 14, 755-763. [CrossRef] [PubMed] [Google Scholar]
  • Edgar, R.C., Batzoglou, S. (2006). Multiple sequence alignment. Curr Opin Struct Biol, 16, 368-373. [CrossRef] [PubMed] [Google Scholar]
  • Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., Aurell, E. (2013). Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. Phys Review, 87, 012707. [CrossRef] [Google Scholar]
  • Feinauer, C., Szurmant, H., Weigt, M., Pagnani, A. (2016). Inter-protein sequence co-evolution predicts known physical interactions in bacterial ribosomes and the Trp operon. PLoS One, 11, e0149166. [CrossRef] [PubMed] [Google Scholar]
  • Feinauer, C., Weigt, M. (2017). Context-aware prediction of pathogenicity of missense mutations involved in human disease. arXiv preprint arXiv:1701.07246 [Google Scholar]
  • Figliuzzi, M., Jacquier, H., Schug, A., Tenaillon, O., Weigt, M. (2015). Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1. Mol Biol Evol, 33, 268-280. [CrossRef] [PubMed] [Google Scholar]
  • Finn, R.D., Bateman, A., Clements, J., Coggill, P., Eberhardt, R.Y., Eddy, S.R., Heger, A., Hetherington, K., Holm, L., Mistry, J., Sonnhammer, E.L., Tate, J., Punta, M. (2014). Pfam: the protein families database. Nucleic Acids Res, 42, D222-230. [CrossRef] [PubMed] [Google Scholar]
  • Göbel, U., Sander, C., Schneider, R., Valencia, A. (1994). Correlated mutations and residue contacts in proteins. Proteins, 18, 309-317. [CrossRef] [PubMed] [Google Scholar]
  • Gueudré, T., Baldassi, C., Zamparo, M., Weigt, M., Pagnani, A. (2016). Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis. Proc Nat Acad Sci USA, 113, 12186-12191. [CrossRef] [Google Scholar]
  • Haldane, A., Flynn, W.F., He, P., Vijayan, R.S., Levy, R.M. (2016). Structural propensities of kinase family proteins from a Potts model of residue co-variation. Protein Sci, 25, 1378-1384. [CrossRef] [PubMed] [Google Scholar]
  • Hopf, T.A., Colwell, L.J., Sheridan, R., Rost, B., Sander, C., Marks, D.S. (2012). Three-dimensional structures of membrane proteins from genomic sequencing. Cell, 149, 1607-1621. [CrossRef] [PubMed] [Google Scholar]
  • Hopf, T.A., Schärfe, C.P., Rodrigues, J.P., Green, A.G., Kohlbacher, O., Sander, C., Bonvin, A.M., Marks, D.S. (2014). Sequence co-evolution gives 3D contacts and structures of protein complexes. Elife, 3, e03430. [CrossRef] [Google Scholar]
  • Hopf, T.A., Ingraham, J.B., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nat Biotechnol, 35, 128-135. [CrossRef] [PubMed] [Google Scholar]
  • Jones, D.T., Buchan, D.W., Cozzetto, D., Pontil, M. (2012) PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics, 28, 184-190. [CrossRef] [PubMed] [Google Scholar]
  • Jones, D.T., Singh, T., Kosciolek, T., Tetchner, S. (2015). MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. Bioinformatics, 31, 999-1006. [CrossRef] [PubMed] [Google Scholar]
  • Kamisetty, H., Ovchinnikov, S., Baker, D. (2013). Assessing the utility of coevolution-based residue–residue contact predictions in a sequence-and structure-rich era. Proc Nat Acad Sci USA, 110, 15674-15679. [CrossRef] [Google Scholar]
  • Mann, J.K., Barton, J.P., Ferguson, A.L., Omarjee, S., Walker, B.D., Chakraborty, A., Ndung'u, T. (2014). The fitness landscape of HIV-1 gag: advanced modeling approaches and validation of model predictions by in vitro testing. PLoS Comput Biol, 10, e1003776. [CrossRef] [Google Scholar]
  • Marks, D.S., Colwell, L.J., Sheridan, R., Hopf, T.A., Pagnani, A., Zecchina, R., Sander, C. (2011). Protein 3D structure computed from evolutionary sequence variation. PLoS One, 6, e28766. [CrossRef] [PubMed] [Google Scholar]
  • Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D.S., Sander, C., Zecchina, R., Onuchic, J.N., Hwa, T., Weigt, M. (2011). Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc Nat Acad Sci USA, 108, E1293-E1301. [CrossRef] [Google Scholar]
  • Morcos, F., Schafer, N.P., Cheng, R.R., Onuchic, J.N., Wolynes, P.G. (2014). Coevolutionary information, protein folding landscapes, and the thermodynamics of natural selection. Proc Nat Acad Sci USA, 111, 12408-12413. [CrossRef] [Google Scholar]
  • Neher, E. (1994). How frequent are correlated changes in families of protein sequences? Proc Nat Acad Sci USA, 91, 98-102. [CrossRef] [Google Scholar]
  • Nugent, T., Jones, D.T. (2012). Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis. Proc Nat Acad Sci USA, 109, E1540-E1547. [CrossRef] [Google Scholar]
  • Ovchinnikov, S., Kamisetty, H., Baker, D. (2014). Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information. Elife, 3, e02030. [CrossRef] [PubMed] [Google Scholar]
  • Ovchinnikov, S., Park, H., Varghese, N., Huang, P.S., Pavlopoulos, G.A., Kim, D.E., Kamisetty, H., Kyrpides, N.C., Baker, D. (2017). Protein structure determination using metagenome sequence data. Science, 355, 294-298. [CrossRef] [PubMed] [Google Scholar]
  • Schug, A., Weigt, M., Onuchic, J.N., Hwa, T., Szurmant, H. (2009). High-resolution protein complexes from integrating genomic information with molecular simulation. Proc Nat Acad Sci USA, 106, 22124-22129. [CrossRef] [Google Scholar]
  • Socolich, M., Lockless, S.W., Russ, W.P., Lee, H., Gardner, K.H., Ranganathan, R. (2005). Evolutionary information for specifying a protein fold. Nature, 437, 512-518. [CrossRef] [PubMed] [Google Scholar]
  • Söding, J. (2004). Protein homology detection by HMM-HMM comparison. Bioinformatics, 21, 951-960. [CrossRef] [PubMed] [Google Scholar]
  • Sułkowska, J.I., Morcos, F., Weigt, M., Hwa, T., Onuchic, J.N. (2012). Genomics-aided structure prediction. Proc Nat Acad Sci USA, 109, 10340-10345. [CrossRef] [Google Scholar]
  • Sutto, L., Marsili, S., Valencia, A., Gervasio, F.L. (2015). From residue coevolution to protein conformational ensembles and functional dynamics. Proc Nat Acad Sci USA, 112, 13567-13572. [CrossRef] [Google Scholar]
  • Uguzzoni, G., John Lovis, S., Oteri, F., Schug, A., Szurmant, H., Weigt, M. (2017). Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis. Proc Nat Acad Sci USA, 114, E2662-E2671. [CrossRef] [Google Scholar]
  • UniProt Consortium. UniProt: a hub for protein information. (2015). Nucleic Acids Res, 43, D204–212. [Google Scholar]
  • Wang, S., Sun, S., Li, Z., Zhang, R., Xu, J. (2017). Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput Biol, 13, e1005324. [CrossRef] [Google Scholar]
  • Webb B., Sali A. Protein Structure Modeling with MODELLER, in: Kihara D. (ed.), Protein Structure Prediction. Methods in Molecular Biology (Methods and Protocols), vol 1137, Humana Press, New York, 2014. [Google Scholar]
  • Weigt, M., White, R.A., Szurmant, H., Hoch, J.A., Hwa, T. (2009). Identification of direct residue contacts in protein-protein interaction by message passing. Proc Nat Acad Sci USA, 106, 67-72. [CrossRef] [Google Scholar]

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