AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Gao, Mu; Nakajima An, Davi; Parks, Jerry M.; Skolnick, Jeffrey

doi:10.1038/s41467-022-29394-2

Title: AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Journal Article · 01 April 2022 · Nature Communications

DOI: https://doi.org/10.1038/s41467-022-29394-2 · OSTI ID:1860537

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Center for the Study of Systems Biology, Atlanta, GA (United States)
Georgia Inst. of Technology, Atlanta, GA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Institutes of health (NIH); USDOE

Grant/Contract Number:: AC05-00OR22725; SC0021303; R35GM118039

OSTI ID:: 1860537

Alternate ID(s):: OSTI ID: 1862138

Journal Information:: Nature Communications, Vol. 13, Issue 1; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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