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

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 muta...

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Main Authors:	Petr Popov, Yao Peng, Ling Shen, Raymond C Stevens, Vadim Cherezov, Zhi-Jie Liu, Vsevolod Katritch
Format:	Article
Language:	English
Published:	eLife Sciences Publications Ltd 2018-06-01
Series:	eLife
Subjects:	GPCR stabilizing mutations machine learning
Online Access:	https://elifesciences.org/articles/34729

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spelling	doaj-b8940c7a6e0345e6acf1ba771959fe132021-05-05T15:58:11ZengeLife Sciences Publications LtdeLife2050-084X2018-06-01710.7554/eLife.34729Computational design of thermostabilizing point mutations for G protein-coupled receptorsPetr Popov0Yao Peng1Ling Shen2Raymond C Stevens3Vadim Cherezov4Zhi-Jie Liu5Vsevolod Katritch6https://orcid.org/0000-0003-3883-4505Department of Biological Sciences, University of Southern California, Los Angeles, Los Angeles, United States; Moscow Institute of Physics and Technology, Dolgoprudny, RussiaiHuman Institute, ShanghaiTech University, Shanghai, ChinaiHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, ChinaDepartment 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 StatesDepartment 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 StatesiHuman 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, ChinaDepartment 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 StatesEngineering 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.https://elifesciences.org/articles/34729GPCRstabilizing mutationsmachine learning
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Petr Popov Yao Peng Ling Shen Raymond C Stevens Vadim Cherezov Zhi-Jie Liu Vsevolod Katritch
spellingShingle	Petr Popov Yao Peng Ling Shen Raymond C Stevens Vadim Cherezov Zhi-Jie Liu Vsevolod Katritch Computational design of thermostabilizing point mutations for G protein-coupled receptors eLife GPCR stabilizing mutations machine learning
author_facet	Petr Popov Yao Peng Ling Shen Raymond C Stevens Vadim Cherezov Zhi-Jie Liu Vsevolod Katritch
author_sort	Petr Popov
title	Computational design of thermostabilizing point mutations for G protein-coupled receptors
title_short	Computational design of thermostabilizing point mutations for G protein-coupled receptors
title_full	Computational design of thermostabilizing point mutations for G protein-coupled receptors
title_fullStr	Computational design of thermostabilizing point mutations for G protein-coupled receptors
title_full_unstemmed	Computational design of thermostabilizing point mutations for G protein-coupled receptors
title_sort	computational design of thermostabilizing point mutations for g protein-coupled receptors
publisher	eLife Sciences Publications Ltd
series	eLife
issn	2050-084X
publishDate	2018-06-01
description	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.
topic	GPCR stabilizing mutations machine learning
url	https://elifesciences.org/articles/34729
work_keys_str_mv	AT petrpopov computationaldesignofthermostabilizingpointmutationsforgproteincoupledreceptors AT yaopeng computationaldesignofthermostabilizingpointmutationsforgproteincoupledreceptors AT lingshen computationaldesignofthermostabilizingpointmutationsforgproteincoupledreceptors AT raymondcstevens computationaldesignofthermostabilizingpointmutationsforgproteincoupledreceptors AT vadimcherezov computationaldesignofthermostabilizingpointmutationsforgproteincoupledreceptors AT zhijieliu computationaldesignofthermostabilizingpointmutationsforgproteincoupledreceptors AT vsevolodkatritch computationaldesignofthermostabilizingpointmutationsforgproteincoupledreceptors
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Computational design of thermostabilizing point mutations for G protein-coupled receptors

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