A Web-Based Protocol for Interprotein Contact Prediction by Deep Learning

  • Xiaoyang Jing
  • Hong Zeng
  • Sheng Wang
  • Jinbo Xu
Part of the Methods in Molecular Biology book series (MIMB, volume 2074)


Identifying residue–residue contacts in protein–protein interactions or complex is crucial for understanding protein and cell functions. DCA (direct-coupling analysis) methods shed some light on this, but they need many sequence homologs to yield accurate prediction. Inspired by the success of our deep-learning method for intraprotein contact prediction, we have developed RaptorX-ComplexContact, a web server for interprotein residue–residue contact prediction. Given a pair of interacting protein sequences, RaptorX-ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA) based on genomic distance and phylogeny information, respectively. Then, RaptorX-ComplexContact uses two deep convolutional residual neural networks (ResNet) to predict interprotein contacts from sequential features and coevolution information of paired MSAs. RaptorX-ComplexContact shall be useful for protein docking, protein–protein interaction prediction, and protein interaction network construction.

Key words

Interprotein contact prediction Protein–protein interaction (PPI) prediction Protein interaction network Protein complex Deep learning (DL) Direct-coupling analysis (DCA) Multiple sequence alignment (MSA) Protein docking 



This work was supported by National Institutes of Health grant R01GM089753 to JX and National Science Foundation grant DBI-1564955 to JX.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Xiaoyang Jing
    • 1
    • 2
  • Hong Zeng
    • 3
  • Sheng Wang
    • 1
  • Jinbo Xu
    • 1
  1. 1.Toyota Technological Institute at ChicagoChicagoUSA
  2. 2.School of Computer ScienceFudan UniversityShanghaiChina
  3. 3.School of Computer Science and TechnologyHangzhou Dianzi UniversityHangzhouChina

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