Literature DB >> 26627170

Linear Interaction Energy (LIE) Models for Ligand Binding in Implicit Solvent:  Theory and Application to the Binding of NNRTIs to HIV-1 Reverse Transcriptase.

Yang Su1, Emilio Gallicchio1, Kalyan Das1, Eddy Arnold1, Ronald M Levy1.   

Abstract

Expressions for Linear Interaction Energy (LIE) estimators for the binding of ligands to a protein receptor in implicit solvent are derived based on linear response theory and the cumulant expansion expression for the free energy. Using physical arguments, values of the LIE linear response proportionality coefficients are predicted for the explicit and implicit solvent electrostatic and van der Waals terms. Motivated by the fact that the receptor and solution media may respond differently to the introduction of the ligand, a novel form of the LIE regression equation is proposed to model independently the processes of insertion of the ligand in the receptor and in solution. We apply these models to the problem of estimating the binding free energy of two non-nucleoside classes of inhibitors of HIV-1 RT (HEPT and TIBO analogues). We develop novel regression models with greater predictive ability than more standard LIE formulations. The values of the regression coefficients generally conform to linear response predictions, and we use this fact to develop a LIE regression equation with only one adjustable parameter (excluding the intercept parameter) which is superior to the other models we tested and to previous results in terms of predictive accuracy for the HEPT and TIBO compounds individually. The new models indicate that, due to the different effects of induced steric strain of the receptor, an increase of ligand size alone opposes binding for ligands of the HEPT class, whereas it favors binding for ligands of the TIBO class.

Entities:  

Year:  2007        PMID: 26627170     DOI: 10.1021/ct600258e

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  15 in total

1.  Retention-time prediction for polycyclic aromatic compounds in reversed-phase capillary electro-chromatography.

Authors:  Peter Feenstra; Heidrun Gruber-Wölfler; Michael Brunsteiner; Johannes Khinast
Journal:  J Mol Model       Date:  2015-04-24       Impact factor: 1.810

2.  Conformational Transitions and Convergence of Absolute Binding Free Energy Calculations.

Authors:  Mauro Lapelosa; Emilio Gallicchio; Ronald M Levy
Journal:  J Chem Theory Comput       Date:  2012-01-10       Impact factor: 6.006

3.  Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge.

Authors:  Emilio Gallicchio; Nanjie Deng; Peng He; Lauren Wickstrom; Alexander L Perryman; Daniel N Santiago; Stefano Forli; Arthur J Olson; Ronald M Levy
Journal:  J Comput Aided Mol Des       Date:  2014-02-07       Impact factor: 3.686

4.  The linear interaction energy method for the prediction of protein stability changes upon mutation.

Authors:  Lauren Wickstrom; Emilio Gallicchio; Ronald M Levy
Journal:  Proteins       Date:  2011-10-31

5.  The Binding Energy Distribution Analysis Method (BEDAM) for the Estimation of Protein-Ligand Binding Affinities.

Authors:  Emilio Gallicchio; Mauro Lapelosa; Ronald M Levy
Journal:  J Chem Theory Comput       Date:  2010-09-14       Impact factor: 6.006

6.  Quantitative three dimensional structure linear interaction energy model of 5'-O-[N-(salicyl)sulfamoyl]adenosine and the aryl acid adenylating enzyme MbtA.

Authors:  Nicholas P Labello; Eric M Bennett; David M Ferguson; Courtney C Aldrich
Journal:  J Med Chem       Date:  2008-11-27       Impact factor: 7.446

7.  Molecular simulations study of novel 1,4-dihydropyridines derivatives with a high selectivity for Cav3.1 calcium channel.

Authors:  Xiaoguang Liu; Hui Yu; Xi Zhao; Xu-Ri Huang
Journal:  Protein Sci       Date:  2015-08-25       Impact factor: 6.725

8.  Prediction of the water content in protein binding sites.

Authors:  Julien Michel; Julian Tirado-Rives; William L Jorgensen
Journal:  J Phys Chem B       Date:  2009-10-08       Impact factor: 2.991

Review 9.  Implicit solvent methods for free energy estimation.

Authors:  Sergio Decherchi; Matteo Masetti; Ivan Vyalov; Walter Rocchia
Journal:  Eur J Med Chem       Date:  2014-08-25       Impact factor: 6.514

Review 10.  Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization.

Authors:  Claudio N Cavasotto; Natalia S Adler; Maria G Aucar
Journal:  Front Chem       Date:  2018-05-29       Impact factor: 5.221

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.