Computational design of thermostabilizing point mutations for G protein-coupled receptors.
Journal:
eLife
Published Date:
Jun 21, 2018
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-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.
Authors
Keywords
Amino Acid Sequence
Animals
Binding Sites
Crystallography, X-Ray
Gene Expression
Humans
Kinetics
Machine Learning
Models, Molecular
Point Mutation
Protein Binding
Protein Conformation, alpha-Helical
Protein Conformation, beta-Strand
Protein Engineering
Protein Interaction Domains and Motifs
Protein Stability
Receptor, Serotonin, 5-HT2C
Sequence Alignment
Sequence Homology, Amino Acid
Serotonin 5-HT2 Receptor Agonists
Serotonin 5-HT2 Receptor Antagonists
Thermodynamics