Computational design of thermostabilizing point mutations for G protein-coupled receptors

Petr Popov; Yao Peng; Ling Shen; Raymond C Stevens; Vadim Cherezov; Zhi-Jie Liu; Vsevolod Katritch

doi:10.7554/eLife.34729

eLife (Jun 2018)

Computational design of thermostabilizing point mutations for G protein-coupled receptors

Petr Popov,
Yao Peng,
Ling Shen,
Raymond C Stevens,
Vadim Cherezov,
Zhi-Jie Liu,
Vsevolod Katritch

Affiliations

Petr Popov: Department of Biological Sciences, University of Southern California, Los Angeles, Los Angeles, United States; Moscow Institute of Physics and Technology, Dolgoprudny, Russia
Yao Peng: iHuman Institute, ShanghaiTech University, Shanghai, China
Ling Shen: iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China
Raymond C Stevens: Department of Biological Sciences, University of Southern California, Los Angeles, Los Angeles, United States; iHuman Institute, ShanghaiTech University, Shanghai, China; Department of Chemistry, University of Southern California, Los Angeles, Los Angeles, United States; Bridge Institute, University of Southern California, Los Angeles, Los Angeles, United States
Vadim Cherezov: Department of Biological Sciences, University of Southern California, Los Angeles, Los Angeles, United States; Moscow Institute of Physics and Technology, Dolgoprudny, Russia; Department of Chemistry, University of Southern California, Los Angeles, Los Angeles, United States; Bridge Institute, University of Southern California, Los Angeles, Los Angeles, United States
Zhi-Jie Liu: iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China; Insititute of Molecular and Clinical Medicine, Kunming Medical University, Kunming, China
Vsevolod Katritch: ORCiD; Department of Biological Sciences, University of Southern California, Los Angeles, Los Angeles, United States; Moscow Institute of Physics and Technology, Dolgoprudny, Russia; Department of Chemistry, University of Southern California, Los Angeles, Los Angeles, United States; Bridge Institute, University of Southern California, Los Angeles, Los Angeles, United States

DOI: https://doi.org/10.7554/eLife.34729
Journal volume & issue: Vol. 7

Abstract

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Engineering of GPCR constructs with improved thermostability is a key for successful structural and biochemical studies of this transmembrane protein family, targeted by 40% of all therapeutic drugs. Here we introduce a comprehensive computational approach to effective prediction of stabilizing mutations in GPCRs, named CompoMug, which employs sequence-based analysis, structural information, and a derived machine learning predictor. Tested experimentally on the serotonin 5-HT2C receptor target, CompoMug predictions resulted in 10 new stabilizing mutations, with an apparent thermostability gain ~8.8°C for the best single mutation and ~13°C for a triple mutant. Binding of antagonists confers further stabilization for the triple mutant receptor, with total gains of ~21°C as compared to wild type apo 5-HT2C. The predicted mutations enabled crystallization and structure determination for the 5-HT2C receptor complexes in inactive and active-like states. While CompoMug already shows high 25% hit rate and utility in GPCR structural studies, further improvements are expected with accumulation of structural and mutation data.

Published in eLife

ISSN: 2050-084X (Online)
Publisher: eLife Sciences Publications Ltd
Country of publisher: United Kingdom
LCC subjects: Medicine; Science: Biology (General)
Website: https://elifesciences.org

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