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
ID: Fg7JKSCx
Submitter: Shu-Ching Ou
Submission Date: July 16, 2018, 12:40 p.m.
Version: 1
Colors: | D | E | R | H | K | S | T | N | Q | A | V | I | L | M | F | Y | W | C | G | P |
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Percent Identity | Matching Chains | Protein | Accession | Entry Name |
---|---|---|---|---|
100.0 | 5-hydroxytryptamine receptor 2C | P28335 | 5HT2C_HUMAN | |
99.6 | 5-hydroxytryptamine receptor 2C | Q5IS66 | 5HT2C_PANTR | |
95.0 | 5-hydroxytryptamine receptor 2C | Q60F97 | 5HT2C_CANLF | |
90.9 | 5-hydroxytryptamine receptor 2C | P34968 | 5HT2C_MOUSE | |
90.4 | 5-hydroxytryptamine receptor 2C | P08909 | 5HT2C_RAT |