AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Mu Gao (), Davi Nakajima An, Jerry M. Parks and Jeffrey Skolnick ()
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Mu Gao: Center for the Study of Systems Biology, School of Biological Sciences
Davi Nakajima An: School of Computer Science, Georgia Institute of Technology
Jerry M. Parks: Biosciences Division, Oak Ridge National Laboratory
Jeffrey Skolnick: Center for the Study of Systems Biology, School of Biological Sciences

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-29394-2

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