Alice Coucke | bioRxiv Science

bioRxiv2016

Direct coevolutionary couplings reflect biophysical residue interactions in proteins

Coevolution of residues in contact imposes strong statistical constraints on the sequence variability between homologous proteins. Direct-Coupling Analysis (DCA), a global statistical inference method, successfully models this variability across homologous protein families to infer structural information about proteins. For each residue pair, DCA infers 21x21 matrices describing the coevolutionary coupling for each pair of amino acids (or gaps). To achieve the residue-residue contact prediction, these matrices are mapped onto simple scalar parameters; the full information they contain gets lost. Here, we perform a detailed spectral analysis of the coupling matrices resulting from 70 protein families, to show that they contain quantitative information about the physico-chemical properties of amino-acid interactions. Results for protein families are corroborated by the analysis of synthetic data from lattice-protein models, which emphasizes the critical effect of sampling quality and regularization on the biochemical features of the statistical coupling matrices.

Biophysics↗

bioRxiv2016

ACE: adaptive cluster expansion for maximum entropy graphical model inference

MotivationGraphical models are often employed to interpret patterns of correlations observed in data through a network of interactions between the variables. Recently, Ising/Potts models, also known as Markov random fields, have been productively applied to diverse problems in biology, including the prediction of structural contacts from protein sequence data and the description of neural activity patterns. However, inference of such models is a challenging computational problem that cannot be solved exactly. Here we describe the adaptive cluster expansion (ACE) method to quickly and accurately infer Ising or Potts models based on correlation data. ACE avoids overfitting by constructing a sparse network of interactions sufficient to reproduce the observed correlation data within the statistical error expected due to finite sampling. When convergence of the ACE algorithm is slow, we combine it with a Boltzmann Machine Learning algorithm (BML). We illustrate this method on a variety of biological and artificial data sets and compare it to state-of-the-art approximate methods such as Gaussian and pseudo-likelihood inference.\n\nResultsWe show that ACE accurately reproduces the true parameters of the underlying model when they are known, and yields accurate statistical descriptions of both biological and artificial data. Models inferred by ACE have substantially better statistical performance compared to those obtained from faster Gaussian and pseudo-likelihood methods, which only precisely recover the structure of the interaction network.\n\nAvailabilityThe ACE source code, user manual, and tutorials with example data are freely available on GitHub at https://github.com/johnbarton/ACE.\n\nContactsjpbarton@gmail.com, cocco@lps.ens.fr\n\nSupplementary informationSupplementary data are available

Bioinformatics↗