Computational design of thermostabilizing point mutations for G protein-coupled receptors.


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

Submission Details

ID: Fg7JKSCx

Submitter: Shu-Ching Ou

Submission Date: July 16, 2018, 12:40 p.m.

Version: 1

Publication Details
Popov P;Peng Y;Shen L;Stevens RC;Cherezov V;Liu ZJ;Katritch V,Elife (2018) Computational design of thermostabilizing point mutations for G protein-coupled receptors. PMID:29927385
Additional Information

Structure view and single mutant data analysis

Study data

No weblogo for data of varying length.
Colors: D E R H K S T N Q A V I L M F Y W C G P
 

Data Distribution

Studies with similar sequences (approximate matches)

Correlation with other assays (exact sequence matches)


Relevant UniProtKB Entries

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