Inferring interaction partners from protein sequences

By Anne-Florence Bitbol, R. S. Dwyer, Lucy J. Colwell, Ned S. Wingreen. 2016

133 citations recorded by the source corpus.

Significance Specific protein−protein interactions play crucial roles in the stability of multiprotein complexes and in signal transduction. Thus, mapping these interactions is key to a systems-level understanding of cells. Systematic experimental identification of protein interaction partners is still challenging. However, a large and rapidly growing amount of sequence data is now available. Is it possible to identify which proteins interact just from their sequences? We propose an approach based on sequence covariation, building on methods used with success to predict the three-dimensional structures of proteins from sequences alone. Our method identifies specific interaction partners with high accuracy among the members of several ubiquitous prokaryotic protein families, and provides a way to predict protein−protein interactions directly from sequence data. Specific protein−protein interactions are crucial in the cell, both to ensure the formation and stability of multiprotein complexes and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners, causing their sequences to be correlated. Here we exploit these correlations to accurately identify, from sequence data alone, which proteins are specific interaction partners. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to predict the 3D structures of proteins from sequences. Thus inspired, we introduce an iterative algorithm to predict specific interaction partners from two protein families whose members are known to interact. We first assess the algorithm’s performance on histidine kinases and response regulators from bacterial two-component signaling systems. We obtain a striking 0.93 true positive fraction on our complete dataset without any a priori knowledge of interaction partners, and we uncover the origin of this success. We then apply the algorithm to proteins from ATP-binding cassette (ABC) transporter complexes, and obtain accurate predictions in these systems as well. Finally, we present two metrics that accurately distinguish interacting protein families from noninteracting ones, using only sequence data.

Subfields: Molecular Biology

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Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, neutral): “A small-coupling approximation [37,38,50,51] (S1 Appendix) gives C−1 ll′ ≈ (1− δll′) κ∆l∆l′ + δll′ ( 1 Pl + 1 1− Pl ) , (9)”
Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers (2020, neutral): “This follows recent works employing simpler statistical models that predict protein quaternary structure, protein-protein interactions and protein interaction networks from evolutionary sequence data [52, 26, 36, 3, 14].”
Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis (2016, background): “Wingreen have prepared a related paper on predicting interacting paralog pairs (46).”
Inferring interaction partners from protein sequences using mutual information (2018, methodology): “In contrast to the previ- 30 ous pairwise maximum entropy-based approaches [16, 17], the present method is based on an 31 approximate maximization of mutual information between the sequences from the two protein 32 families.”
Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis (2017, background): “Computationally this becomes a hard problem by itself due to the existence of paralogs (26-28), and most works so far have concentrated on systems where interaction partners have co-localized genes, e.”
Origins of coevolution between residues distant in protein 3D structures (2017, unknown): “There has been considerable recent success in using direct couplings to infer residue–residue contacts for protein structure prediction (6–9), protein–protein complex prediction (10–12), identification of specific interaction partners among two sets of paralogous proteins (13), resolution of ambigui”
Power law tails in phylogenetic systems (2018, background): “Recently, with the growth of protein sequence databases (5) and the introduction of sophisticated analyses (6–8), it has become clear that covariance analysis of protein sequences can yield exciting biological insights in a wide range of contexts (9– 27).”
Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “These algorithms were extended to subunits of ABC transporters [95] and to members of the tryptophan biosynthesis machinery [96] and they will likely be generally applicable to determine whether two proteins interact, as long as enough sequence information is available.”