Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis

By Guido Uguzzoni, Shalini John Lovis, Francesco Oteri, Alexander Schug, H. Szurmant, Martin Weigt. 2017

97 citations recorded by the source corpus.

Significance Protein–protein interactions are important to all facets of life, but their experimental and computational characterization is arduous and frequently of uncertain outcome. The current study demonstrates both the power and limitation to study protein interactions by utilizing sophisticated statistical inference technology to derive protein contacts from available sequence databases, more precisely from the coevolution between residues, that are in contact across the interaction interface of two proteins. By studying homo-oligomeric protein interactions, the current study expands from anecdotal evidence of the performance of this technology to systematic evidence of its value across close to 2,000 interacting protein families. Proteins have evolved to perform diverse cellular functions, from serving as reaction catalysts to coordinating cellular propagation and development. Frequently, proteins do not exert their full potential as monomers but rather undergo concerted interactions as either homo-oligomers or with other proteins as hetero-oligomers. The experimental study of such protein complexes and interactions has been arduous. Theoretical structure prediction methods are an attractive alternative. Here, we investigate homo-oligomeric interfaces by tracing residue coevolution via the global statistical direct coupling analysis (DCA). DCA can accurately infer spatial adjacencies between residues. These adjacencies can be included as constraints in structure prediction techniques to predict high-resolution models. By taking advantage of the ongoing exponential growth of sequence databases, we go significantly beyond anecdotal cases of a few protein families and apply DCA to a systematic large-scale study of nearly 2,000 Pfam protein families with sufficient sequence information and structurally resolved homo-oligomeric interfaces. We find that large interfaces are commonly identified by DCA. We further demonstrate that DCA can differentiate between subfamilies with different binding modes within one large Pfam family. Sequence-derived contact information for the subfamilies proves sufficient to assemble accurate structural models of the diverse protein-oligomers. Thus, we provide an approach to investigate oligomerization for arbitrary protein families leading to structural models complementary to often-difficult experimental methods. Combined with ever more abundant sequential data, we anticipate that this study will be instrumental to allow the structural description of many heteroprotein complexes in the future.

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Predicting multiple conformations via sequence clustering and AlphaFold2 (2023, positive): “Indeed, methods for inferring evolutionary couplings have already demonstrated that contacts corresponding to multiple states can be observed at the level of entire MSAs for membrane proteins 15 , ligand-induced conformational changes 16 and multimerization-induced conformational changes 17 .”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “…applied to protein complex assembly and interactions, disordered region structure prediction, RNA structure prediction, and mutagenesis analysis (Ovchinnikov et al., 2014; Uguzzoni et al., 2017; Hopf et al., 2014, 2017; Toth-Petroczy et al., 2016; De Leonardis et al., 2015; Weinreb et al., 2016).”
Coevolutionary Analysis of Protein Subfamilies by Sequence Reweighting (2019, background): “Taken together, these results illustrate that even if the splitting strategy works efficiently when sufficient sequence data is present in subfamilies [9], DCA predictions obtained with subfamily alignments might become unreliable and unable to identify subfamily specific structural features in the ”
Prediction of multiple conformational states by combining sequence clustering with AlphaFold2 (2022, methodology): “However, simply subdividing a MSA and making predictions for portions of the MSA has also been used to detect variations in coevolutionary signal within a protein family 24,27,28 .”
Protein language model embeddings for fast, accurate, alignment-free protein structure prediction (2021, background): “The differentiation of co-evolving residue pairs arising from intra-protein contacts or interprotein interactions further complicates de novo structure prediction (Uguzzoni et al., 2017).”
Inter-residue, inter-protein and inter-family coevolution: bridging the scales (2017, background): “, one protein family contains several sub-families with distinct interaction modes [34].”
Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “However, experimental verification of these models is needed for each specific protein of interest because homomer structures can differ between subfamilies of the same overall fold [98].”