Deep learning reveals many more inter-protein residue-residue contacts than direct coupling analysis

Tian Ming Zhou, Sheng Wang, Jinbo Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


AbstractIntra-protein residue-level contact prediction has drawn a lot of attentions in recent years and made very good progress, but much fewer methods are dedicated to inter-protein contact prediction, which are important for understanding how proteins interact at structure and residue level. Direct coupling analysis (DCA) is popular for intra-protein contact prediction, but extending it to inter-protein contact prediction is challenging since it requires too many interlogs (i.e., interacting homologs) to be effective, which cannot be easily fulfilled especially for a putative interacting protein pair in eukaryotes. We show that deep learning, even trained by only intra-protein contact maps, works much better than DCA for inter-protein contact prediction. We also show that a phylogeny-based method can generate a better multiple sequence alignment for eukaryotes than existing genome-based methods and thus, lead to better inter-protein contact prediction. Our method shall be useful for protein docking, protein interaction prediction and protein interaction network construction.
Original languageEnglish (US)
Title of host publication22nd International Conference on Research in Computational Molecular Biology, RECOMB 2018
PublisherCold Spring Harbor Laboratory
Number of pages2
ISBN (Print)9783319899282
StatePublished - Dec 30 2017

Bibliographical note

KAUST Repository Item: Exported on 2021-04-19


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