TY - JOUR TI - Computational design of thermostabilizing point mutations for G protein-coupled receptors AU - Popov, Petr AU - Peng, Yao AU - Shen, Ling AU - Stevens, Raymond C AU - Cherezov, Vadim AU - Liu, Zhi-Jie AU - Katritch, Vsevolod A2 - Ben-Tal, Nir VL - 7 PY - 2018 DA - 2018/06/21 SP - e34729 C1 - eLife 2018;7:e34729 DO - 10.7554/eLife.34729 UR - https://doi.org/10.7554/eLife.34729 AB - 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. KW - GPCR KW - stabilizing mutations KW - machine learning JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -