Introduction

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.

Publications

  1. Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners.
    Cite this
    Baldassi C, Zamparo M, Feinauer C, Procaccini A, Zecchina R, Weigt M, Pagnani A, 2014-01-01 - PloS one

Credits

  1. Carlo Baldassi
    Developer

    Department of Applied Science and Technology and Center for Computational Sciences, Politecnico di Torino, Italy

  2. Marco Zamparo
    Developer

    Department of Applied Science and Technology and Center for Computational Sciences, Politecnico di Torino, Italy

  3. Christoph Feinauer
    Developer

    Department of Applied Science and Technology and Center for Computational Sciences, Politecnico di Torino, Italy

  4. Andrea Procaccini
    Developer

    Human Genetics Foundation-Torino, Torino, Italy

  5. Riccardo Zecchina
    Developer

    Department of Applied Science and Technology and Center for Computational Sciences, Politecnico di Torino, Italy

  6. Martin Weigt
    Developer

    Sorbonne Universités, Université Pierre et Marie Curie Paris 06, France

  7. Andrea Pagnani
    Investigator

    Department of Applied Science and Technology and Center for Computational Sciences, Politecnico di Torino, Italy

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Summary
AccessionBT000105
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesC
User InterfaceTerminal Command Line
Download Count0
Country/RegionItaly
Submitted ByAndrea Pagnani