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Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

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  • Carlo Baldassi
  • Marco Zamparo
  • Christoph Feinauer
  • Andrea Procaccini
  • Riccardo Zecchina
  • Martin Weigt
  • Andrea Pagnani

Abstract

In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation) have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids), exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i) the prediction of residue-residue contacts in proteins, and (ii) the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code.

Suggested Citation

  • Carlo Baldassi & Marco Zamparo & Christoph Feinauer & Andrea Procaccini & Riccardo Zecchina & Martin Weigt & Andrea Pagnani, 2014. "Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-12, March.
  • Handle: RePEc:plo:pone00:0092721
    DOI: 10.1371/journal.pone.0092721
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    References listed on IDEAS

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    1. Lukas Burger & Erik van Nimwegen, 2010. "Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments," PLOS Computational Biology, Public Library of Science, vol. 6(1), pages 1-18, January.
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    Cited by:

    1. Pedro L Teixeira & Jeff L Mendenhall & Sten Heinze & Brian Weiner & Marcin J Skwark & Jens Meiler, 2017. "Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-24, May.
    2. Patrick Bryant & Gabriele Pozzati & Arne Elofsson, 2022. "Improved prediction of protein-protein interactions using AlphaFold2," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Lorenzo Asti & Guido Uguzzoni & Paolo Marcatili & Andrea Pagnani, 2016. "Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-20, April.

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