Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning

Jianfeng Sun, Dmitrij Frishman

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Interactions between transmembrane (TM) proteins are fundamental for a wide spectrum of cellular functions, but precise molecular details of these interactions remain largely unknown due to the scarcity of experimentally determined three-dimensional complex structures. Computational techniques are therefore required for a large-scale annotation of interaction sites in TM proteins. Here, we present a novel deep-learning approach, DeepTMInter, for sequence-based prediction of interaction sites in α-helical TM proteins based on their topological, physiochemical, and evolutionary properties. Using a combination of ultra-deep residual neural networks with a stacked generalization ensemble technique DeepTMInter significantly outperforms existing methods, achieving the AUC/AUCPR values of 0.689/0.598. Across the main functional families of human transmembrane proteins, the percentage of amino acid sites predicted to be involved in interactions typically ranges between 10% and 25%, and up to 30% in ion channels. DeepTMInter is available as a standalone package at https://github.com/2003100127/deeptminter. The training and benchmarking datasets are available at https://data.mendeley.com/datasets/2t8kgwzp35.

Original languageEnglish
Pages (from-to)1512-1530
Number of pages19
JournalComputational and Structural Biotechnology Journal
Volume19
DOIs
StatePublished - Jan 2021

Keywords

  • Deep learning
  • Molecular evolution
  • Protein function
  • Protein structure
  • Protein-protein interactions
  • Sequence annotation

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