A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers.

ABSTRACT:

Motivation

Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field. Because interchain residue-residue contacts can be used as distance restraints to guide quaternary structure modeling, here we develop a deep dilated convolutional residual network method (DRCon) to predict interchain residue-residue contacts in homodimers from residue-residue co-evolutionary signals derived from multiple sequence alignments of monomers, intrachain

SUBMITTER: Roy RS 

PROVIDER: S-EPMC8963319 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature