Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences

Anna G. Green, Hadeer Elhabashy, Kelly P. Brock, Rohan Maddamsetti, Oliver Kohlbacher () and Debora S. Marks ()
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Anna G. Green: Harvard Medical School
Hadeer Elhabashy: Biomolecular Interactions, Max Planck Institute for Developmental Biology
Kelly P. Brock: Harvard Medical School
Rohan Maddamsetti: Harvard Medical School
Oliver Kohlbacher: Biomolecular Interactions, Max Planck Institute for Developmental Biology
Debora S. Marks: Institute for Bioinformatics and Medical Informatics, University of Tübingen

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Increasing numbers of protein interactions have been identified in high-throughput experiments, but only a small proportion have solved structures. Recently, sequence coevolution-based approaches have led to a breakthrough in predicting monomer protein structures and protein interaction interfaces. Here, we address the challenges of large-scale interaction prediction at residue resolution with a fast alignment concatenation method and a probabilistic score for the interaction of residues. Importantly, this method (EVcomplex2) is able to assess the likelihood of a protein interaction, as we show here applied to large-scale experimental datasets where the pairwise interactions are unknown. We predict 504 interactions de novo in the E. coli membrane proteome, including 243 that are newly discovered. While EVcomplex2 does not require available structures, coevolving residue pairs can be used to produce structural models of protein interactions, as done here for membrane complexes including the Flagellar Hook-Filament Junction and the Tol/Pal complex.

Date: 2021
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DOI: 10.1038/s41467-021-21636-z

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