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

By Petr Popov, Yao Peng, Ling Shen, Raymond C Stevens, Vadim Cherezov1, Zhi-Jie Liu, Vsevolod Katritch

1. Bridge Institute - University of Southern California

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Type

journal-article

Author

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

Citation

Popov, P. et al., 2018. Computational design of thermostabilizing point mutations for G protein-coupled receptors. eLife, 7. Available at: http://dx.doi.org/10.7554/elife.34729.

Abstract

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.

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