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

• Main Authors: Jianfeng Sun, Dmitrij Frishman
• Format: Article
• Language: English
• Published: Elsevier 2021-01-01
• Series: Computational and Structural Biotechnology Journal
• Subjects: Protein-protein interactions; Protein structure; Protein function; Molecular evolution; Sequence annotation; Deep learning
• Online Access: http://www.sciencedirect.com/science/article/pii/S2001037021000775

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.