Inferring interaction partners from protein sequences

Abstract

Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multi-protein 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 which proteins are specific interaction partners from sequence data alone. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to predict the three-dimensional 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 non-interacting ones, using only sequence data.

25 pages, 19 figures, published version

References in corpus (4)

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Cited by in corpus (21)

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• Inferring interaction partners from protein sequences using mutual information
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• Revealing evolutionary constraints on proteins through sequence analysis
• Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers
• Quantifying Relevance in Learning and Inference
• Aligning biological sequences by exploiting residue conservation and coevolution
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• Building blocks of protein structures -- Physics meets Biology
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• Statistical physics of interacting proteins: impact of dataset size and quality assessed in synthetic sequences
• On generative models of T-cell receptor sequences
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• DiffPaSS -- High-performance differentiable pairing of protein sequences using soft scores
• Impact of phylogeny on the inference of functional sectors from protein sequence data
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