Inverse statistical physics of protein sequences: a key issues review

By Simona Cocco, Christoph Feinauer, Matteo Figliuzzi, Rémi Monasson, Martin Weigt. 2017

242 citations recorded by the source corpus.

In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e. evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. Finally, we discuss some open questions, which we expect to be addressed over the next years.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

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Citations in the corpus

Efficient generative modeling of protein sequences using simple autoregressive models (2021, methodology): “(2) has striking similarities to standard DCA [13], but also important differences.”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “The use of such genomic information has recently lead to predictions of the three-dimensional structure of many protein families inaccessible to other methods [167], for a review see [51].”
How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins? (2018, background): “A second level of information is contained in the co-variation between pairs of residues, measurable via the correlated amino-acid usage in pairs of MSA columns [5, 7].”
Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “A recent review provides a comparison of these different statistical methods [28].”
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction (2021, methodology): “349 Next, a sequence dependent weight w(S) was computed so as to i) address sampling redundancy [66] and ii) focus the 350 alignment around the wild type [67]: 351”
Coevolutionary Analysis of Protein Subfamilies by Sequence Reweighting (2019, background): “, [41] for a full review on DCA and its applications).”
Inter-residue, inter-protein and inter-family coevolution: bridging the scales (2017, background): “How do the proteins interact? Can we identify the interaction interface? Can we predict residues being in contact across this interface? Can we assemble the complex from the individual protein structures? In the last few years, significant progress in coevolutionary modelling [6,7] and the rapid gro”
Learning protein constitutive motifs from sequence data (2018, methodology): “RBM also outperforms the Hopfield-Potts framework Cocco et al. (2013), an approach previously introduced to capture both collective and localized structural modes.”