Literature DB >> 22976028

LITiCon: a discrete conformational sampling computational method for mapping various functionally selective conformational states of transmembrane helical proteins.

Supriyo Bhattacharya1, Nagarajan Vaidehi.   

Abstract

G-Protein-coupled receptors (GPCRs) are seven helical transmembrane proteins that mediate cell signaling thereby controlling many important physiological and pathological functions. GPCRs get activated upon ligand binding and trigger the signal transduction process. GPCRs exist in multiple inactive and active conformations, and there is a finite population of the active and inactive states even in the ligand-free condition. An understanding of the nature of the conformational ensemble sampled by GPCRs and the atomic level mechanism of the conformational transitions require a combination of computational methods and experimental techniques. We have developed a coarse grained discrete conformational sampling computational method called "LITiCon" to map the conformational ensemble sampled by GPCRs in the presence and absence of ligands. The LITiCon method can also be used to predict functional selective conformational states starting from the inactive state of the receptor. LITiCon has been applied to map the conformational ensemble of β2-adrenergic receptor, a class A GPCR. We have shown that β2-adrenergic receptor samples a larger conformational space in the ligand-free state and that different ligands select and stabilize conformations from this ensemble. In this review we describe the LITiCon method in detail and elucidate the uses and pitfalls of this method.

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Year:  2012        PMID: 22976028     DOI: 10.1007/978-1-62703-023-6_10

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  4 in total

1.  Prediction of Conformation Specific Thermostabilizing Mutations for Class A G Protein-Coupled Receptors.

Authors:  Suvamay Jana; Soumadwip Ghosh; Sanychen Muk; Benjamin Levy; Nagarajan Vaidehi
Journal:  J Chem Inf Model       Date:  2019-08-27       Impact factor: 4.956

2.  Machine Learning for Prioritization of Thermostabilizing Mutations for G-Protein Coupled Receptors.

Authors:  Sanychen Muk; Soumadwip Ghosh; Srisairam Achuthan; Xiaomin Chen; XiaoJie Yao; Manbir Sandhu; Matthew C Griffor; Kimberly F Fennell; Ye Che; Veerabahu Shanmugasundaram; Xiayang Qiu; Christopher G Tate; Nagarajan Vaidehi
Journal:  Biophys J       Date:  2019-10-24       Impact factor: 4.033

3.  Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors.

Authors:  Supriyo Bhattacharya; Sangbae Lee; Reinhard Grisshammer; Christopher G Tate; Nagarajan Vaidehi
Journal:  J Chem Theory Comput       Date:  2014-10-14       Impact factor: 6.006

4.  Improving virtual screening of G protein-coupled receptors via ligand-directed modeling.

Authors:  Thomas Coudrat; John Simms; Arthur Christopoulos; Denise Wootten; Patrick M Sexton
Journal:  PLoS Comput Biol       Date:  2017-11-13       Impact factor: 4.475

  4 in total

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