Repository logo

Inferring interaction partners from protein sequences.

Accepted version



Change log


Bitbol, Anne-Florence 
Dwyer, Robert S 
Colwell, Lucy J 
Wingreen, Ned S 


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.



coevolution, direct coupling analysis, maximum entropy, paralogs, protein−protein interactions, ATP-Binding Cassette Transporters, Algorithms, Bacteria, Entropy, Histidine Kinase, Protein Binding, Protein Interaction Maps, Sequence Analysis, Protein, Signal Transduction

Journal Title

Proc Natl Acad Sci U S A

Conference Name

Journal ISSN


Volume Title



Proceedings of the National Academy of Sciences
European Commission (631609)
Human Frontier Science Program, National Institutes of Health (Grant ID: R01-GM082938), National Science Foundation (Grant ID: PHY-1305525), Marie Curie (Career Integration Grant ID: 631609), Next Generation Fellowship, Eric and Wendy Schmidt Transformative Technology Fund