Dataset Information

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

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.

SUBMITTER: Sun J

PROVIDER: S-EPMC7985279 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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

Sun Jianfeng J Frishman Dmitrij D

Computational and structural biotechnology journal 20210309

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 ...[more]

PMID: 33815689

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Project description:Membrane proteins, particularly those that are α-helical, such as transporters and G-protein-coupled receptors (GPCRs), have significant biological relevance. However, their expression and purification pose difficulties because of their poor water solubilities, which impedes progress in this field. The QTY method, a code-based protein-engineering approach, was recently developed to produce soluble transmembrane proteins. Here, we describe a comprehensive Web server built for QTY design and its relevance for in silico analyses. Typically, the simple design model is expected to require only 2 to 4 min of computer time, and the library design model requires 2 to 5 h, depending on the target protein size and the number of transmembrane helices. Detailed protocols for using the server with both the simple design and library design modules are provided. Methods for experiments following the QTY design are also included to facilitate the implementation of this approach. The design pipeline was further evaluated using microbial transmembrane proteins and structural alignment between the designed proteins and their origins by employing AlphaFold2. The results reveal that mutants generated by the developed pipeline were highly identical to their origins in terms of three-dimensional (3D) structures. In summary, the utilization of our Web server and associated protocols will enable QTY-based protein engineering to be implemented in a convenient, fast, accurate, and rational manner. The Protein Solubilizing Server (PSS) is publicly available at http://pss.sjtu.edu.cn. IMPORTANCE Water-soluble expression and purification are of considerable importance for protein identification and characterization. However, there has been a lack of an effective method for water-soluble expression of membrane proteins, which has severely hampered their studies. Here, an enabling comprehensive Web server, PSS, was developed for designing water-soluble mutants of α-helical membrane proteins, based on QTY design, a code-based protein-engineering approach. With microbial transmembrane proteins and GPCRs as examples, we systematically evaluated the server and demonstrated its successful performance. PSS is readily available for worldwide users as a Web-based tool, rendering QTY-based protein engineering convenient, efficient, accurate, and rational.

Dataset Information

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

Publications

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

Similar Datasets

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