Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Abstract

Protein-protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein-protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein-protein interfaces. We show that sequencebased coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein-protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequencebased accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.coevolution | protein-protein interaction | protein complex | homology modeling | contact prediction C ells function as a remarkably synchronized orchestra of finely tuned molecular interactions, and establishing this molecular network has become a major goal of molecular biology. Important methodological and technical advances have led to the identification of a large number of novel protein-protein interactions and to major contributions to our understanding of the functioning of cells and organisms (1, 2). In contrast, and despite relevant advances in EM (3) and crystallography (4), the molecular details of a large number of interactions remain unknown.When experimental structural data are absent or incomplete, template-based homology modeling of protein complexes represents the most reliable option (5, 6). Similarly to modeling of tertiary structure for single-chain proteins, homology modeling of protein-protein interactions follows a conservation-based approach, in which the quaternary structure of one or more experimentally solved complexes with enough sequence similarity to a target complex (the templates) is projected onto the target. Templatebased techniques have provided models for a large number of protein complexes with structurally solved homologous complexes (7-10). Unfortunately, proteins involved in homologous protein dimers tend to systema...

The EVcouplings Python framework for coevolutionary sequence analysis

“…Pairwise graphical models of biological sequences are used to identify evolutionary couplings (ECs) between sites, which frequently correspond to physical contacts between nucleotides or amino acids in the molecule's three-dimensional structure. ECs have been used successfully to predict the tertiary contacts (Marks, et al, 2011;Morcos, et al, 2011) and full 3D structure of proteins (Marks, et al, 2011) and RNA (Weinreb, et al, 2016), as well as protein-protein and protein-RNA interactions, disorder and conformational changes (Weigt, et al, 2009;Hopf, et al, 2012;Hopf, et al, 2014;Ovchinnikov, et al, 2014;Rodriguez-Rivas, et al, 2016;Toth-Petroczy, et al, 2016) and the effects of mutations (Figliuzzi, et al, 2015;Hopf, et al, 2017). More recently, hybrid approaches have integrated ECs with experimental data for improved structure determination using NMR, cryo-EM and X-ray crystallography data (Tang, et al, 2015;Sjodt, et al, 2018).…”

A multi-scale coevolutionary approach to predict interactions between protein domains

“…Concerning interacting proteins, they have triggered a breakthrough in using sequence covariation for inter-protein residue-residue contact prediction [16,17], which in turn is used to guide computational quaternary structure prediction [22][23][24][25].…”

Deep learning reveals many more inter-protein residue-residue contacts than direct coupling analysis

“…It is much more challenging to concatenate two individual MSAs for a protein pair in eukaryotes. This is because an individual MSA may contain abundant paralogs and two genes may interact with each other even if they are not close by genomic distance [40,42]. Here we propose to concatenate two individual MSAs using phylogeny and homology information.…”

“…This problem becomes more serious for inter-protein contact prediction since it is challenging to find so many interlogs (i.e., interacting homologs) for an interacting protein pair. Because of this, currently DCA for inter-protein contact prediction mainly focuses on prokaryotes and mitochondria [16,25] since it is relatively easy to find interlogs in prokaryotes, but does not fare well on eukaryotes with abundant paralogs because it is challenging to identify correct interlogs [42].…”