Journal
ELIFE
Volume 7, Issue -, Pages -Publisher
eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.34729
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Funding
- National Institutes of Health [P01DA035764]
- Russian Science Foundation [16-14-10273]
- National Natural Science Foundation of China [31330019]
- Ministry of Science and Technology of China [2014CB910400, 2015CB910104]
- Russian Science Foundation [16-14-10273] Funding Source: Russian Science Foundation
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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 similar to 8.8 degrees C for the best single mutation and similar to 13 degrees C for a triple mutant. Binding of antagonists confers further stabilization for the triple mutant receptor, with total gains of similar to 21 degrees 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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