Literature DB >> 32941011

Binding Characterization of GPCRs-Modulator by Molecular Complex Characterizing System (MCCS).

Zhiwei Feng1, Tianjian Liang1, Siyi Wang1, Maozi Chen1, Tianling Hou1, Jack Zhao1, Hui Chen1, Yuehan Zhou1, Xiang-Qun Xie2.   

Abstract

Increasing attention has been devoted to allosteric modulators as the preferred therapeutic agents for their colossal advantages such as higher selectivity, fewer side effects, and lower toxicity since they bind at allosteric sites that are topographically distinct from the classic orthosteric sites. However, the allosteric binding pockets are not conserved and there are no cogent methods to comprehensively characterize the features of allosteric sites with the binding of modulators. To overcome this limitation, our lab has developed a novel algorithm that can quantitatively characterize the receptor-ligand binding feature named Molecular Complex Characterizing System (MCCS). To illustrate the methodology and application of MCCS, we take G protein coupled receptors (GPCRs) as an example. First, we summarized and analyzed the reported allosteric binding pockets of class A GPCRs using MCCS. Sequentially, a systematic study was conducted between cannabinoid receptor type 1 (CB1) and its allosteric modulators, where we used MCCS to analyze the residue energy contribution and the interaction pattern. Finally, we validated the predicted allosteric binding site in CB2 via MCCS in combination with molecular dynamics (MD) simulation. Our results demonstrate that the MCCS program is advantageous in recapitulating the allosteric regulation pattern of class A GPCRs of the reported pockets as well as in predicting potential allosteric binding pockets. This MCCS program can serve as a valuable tool for the discovery of small-molecule allosteric modulators for class A GPCRs.

Entities:  

Keywords:  Allosteric modulator; GPCRs; MCCS; feature characterization; residue energy contribution

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Year:  2020        PMID: 32941011     DOI: 10.1021/acschemneuro.0c00457

Source DB:  PubMed          Journal:  ACS Chem Neurosci        ISSN: 1948-7193            Impact factor:   4.418


  2 in total

1.  SYNBIP: synthetic binding proteins for research, diagnosis and therapy.

Authors:  Xiaona Wang; Fengcheng Li; Wenqi Qiu; Binbin Xu; Yanlin Li; Xichen Lian; Hongyan Yu; Zhao Zhang; Jianxin Wang; Zhaorong Li; Weiwei Xue; Feng Zhu
Journal:  Nucleic Acids Res       Date:  2022-01-07       Impact factor: 16.971

2.  In Silico Prediction and Validation of CB2 Allosteric Binding Sites to Aid the Design of Allosteric Modulators.

Authors:  Jiayi Yuan; Chen Jiang; Junmei Wang; Chih-Jung Chen; Yixuan Hao; Guangyi Zhao; Zhiwei Feng; Xiang-Qun Xie
Journal:  Molecules       Date:  2022-01-11       Impact factor: 4.411

  2 in total

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