Literature DB >> 21554077

The use of G-protein coupled receptor models in lead optimization.

Christofer S Tautermann1.   

Abstract

With the emerging new crystal structures of G-protein coupled receptors (GPCRs), the number of reported in silico receptor models vastly increases every year. The use of these models in lead optimization (LO) is investigated here. Although there are many studies where GPCR models are used to identify new chemotypes by virtual screening, the classical application in LO is rarely reported. The reason for this may be that the quality of a model, which is appropriate for atomistic modeling, must be very high, and the biology of GPCR ligand-dependent signaling is still not fully understood. However, the few reported studies show that GPCR models can be used efficiently in LO for various problems, such as affinity optimization or tuning of physicochemical parameters.

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Year:  2011        PMID: 21554077     DOI: 10.4155/fmc.11.24

Source DB:  PubMed          Journal:  Future Med Chem        ISSN: 1756-8919            Impact factor:   3.808


  3 in total

1.  Developing chemical genetic approaches to explore G protein-coupled receptor function: validation of the use of a receptor activated solely by synthetic ligand (RASSL).

Authors:  Elisa Alvarez-Curto; Rudi Prihandoko; Christofer S Tautermann; Jurriaan M Zwier; John D Pediani; Martin J Lohse; Carsten Hoffmann; Andrew B Tobin; Graeme Milligan
Journal:  Mol Pharmacol       Date:  2011-08-31       Impact factor: 4.436

2.  Optimization of adenosine 5'-carboxamide derivatives as adenosine receptor agonists using structure-based ligand design and fragment screening.

Authors:  Dilip K Tosh; Khai Phan; Zhan-Guo Gao; Andrei A Gakh; Fei Xu; Francesca Deflorian; Ruben Abagyan; Raymond C Stevens; Kenneth A Jacobson; Vsevolod Katritch
Journal:  J Med Chem       Date:  2012-04-30       Impact factor: 7.446

3.  What can we learn from molecular dynamics simulations for GPCR drug design?

Authors:  Christofer S Tautermann; Daniel Seeliger; Jan M Kriegl
Journal:  Comput Struct Biotechnol J       Date:  2014-12-10       Impact factor: 7.271

  3 in total

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