Selection of sequence motifs and generative Hopfield-Potts models for protein families.

Statistical models for families of evolutionary related proteins have recently gained interest: In particular, pairwise Potts models as those inferred by the direct-coupling analysis have been able to extract information about the three-dimensional structure of folded proteins and about the effect of amino acid substitutions in proteins. These models are typically requested to reproduce the one- and two-point statistics of the amino acid usage in a protein family, i.e., to capture the so-called residue conservation and covariation statistics of proteins of common evolutionary origin. Pairwise Potts models are the maximum-entropy models achieving this. Although being successful, these models depend on huge numbers of ad hoc introduced parameters, which have to be estimated from finite amounts of data and whose biophysical interpretation remains unclear. Here, we propose an approach to parameter reduction, which is based on selecting collective sequence motifs. It naturally leads to the formulation of statistical sequence models in terms of Hopfield-Potts models. These models can be accurately inferred using a mapping to restricted Boltzmann machines and persistent contrastive divergence. We show that, when applied to protein data, even 20-40 patterns are sufficient to obtain statistically close-to-generative models. The Hopfield patterns form interpretable sequence motifs and may be used to clusterize amino acid sequences into functional subfamilies. However, the distributed collective nature of these motifs intrinsically limits the ability of Hopfield-Potts models in predicting contact maps, showing the necessity of developing models going beyond the Hopfield-Potts models discussed here.