期刊
CURRENT OPINION IN STRUCTURAL BIOLOGY
卷 55, 期 -, 页码 25-33出版社
CURRENT BIOLOGY LTD
DOI: 10.1016/j.sbi.2019.02.010
关键词
-
资金
- GPCR Consortium
- Russian Science Foundation (RSF) [18-74-00117]
- Russian Science Foundation [18-74-00117] Funding Source: Russian Science Foundation
GPCR superfamily is the largest clinically relevant family of targets in human genome; however, low thermostability and high conformational plasticity of these integral membrane proteins make them notoriously hard to handle in biochemical, biophysical, and structural experiments. Here, we describe the recent advances in computational approaches to design stabilizing mutations for GPCR that take advantage of the structural and sequence conservation properties of the receptors, and employ machine learning on accumulated mutation data for the superfamily. The fast and effective computational tools can provide a viable alternative to existing experimental mutation screening and are poised for further improvements with expansion of thermostability datasets for training the machine learning models. The rapidly growing practical applications of computational stability design streamline GPCR structure determination and may contribute to more efficient drug discovery.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据