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 mutations in GPCRs， named CompoMug， which employs sequence-based analysis， structural information， and a derived machine learning predictor. Tested experimentally on the serotonin 5-HT 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-HT. The predicted mutations enabled crystallization and structure determination for the 5-HT 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.