Literature DB >> 15139037

Binding affinity prediction with different force fields: examination of the linear interaction energy method.

Martin Almlöf1, Bjørn O Brandsdal, Johan Aqvist.   

Abstract

A systematic study of the linear interaction energy (LIE) method and the possible dependence of its parameterization on the force field and system (receptor binding site) is reported. We have calculated the binding free energy for nine different ligands in complex with P450cam using three different force fields (Amber95, Gromos87, and OPLS-AA). The results from these LIE calculations using our earlier parameterization give relative free energies of binding that agree remarkably well with the experimental data. However, the absolute energies are too positive for all three force fields, and it is clear that an additional constant term (gamma) is required in this case. Out of five examined LIE models, the same one emerges as the best for all three force fields, and this, in fact, corresponds to our earlier one apart from the addition of the constant gamma, which is almost identical for the three force fields. Thus, the present free energy calculations clearly indicate that the coefficients of the LIE method are independent of the force field used. Their relation to solvation free energies is also demonstrated. The only free parameter of the best model is gamma, which is found to depend on the hydrophobicity of the binding site. We also attempt to quantify the binding site hydrophobicity of four different proteins which shows that the ordering of gamma's for these sites reflects the fraction of hydrophobic surface area. Copyright 2004 Wiley Periodicals, Inc. J Comput Chem 10: 1242-1254, 2004

Year:  2004        PMID: 15139037     DOI: 10.1002/jcc.20047

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  33 in total

1.  Binding affinities in the SAMPL3 trypsin and host-guest blind tests estimated with the MM/PBSA and LIE methods.

Authors:  Paulius Mikulskis; Samuel Genheden; Patrik Rydberg; Lars Sandberg; Lars Olsen; Ulf Ryde
Journal:  J Comput Aided Mol Des       Date:  2011-12-25       Impact factor: 3.686

2.  Improved ligand-protein binding affinity predictions using multiple binding modes.

Authors:  Eva Stjernschantz; Chris Oostenbrink
Journal:  Biophys J       Date:  2010-06-02       Impact factor: 4.033

3.  Computational design of a lipase for catalysis of the Diels-Alder reaction.

Authors:  Mats Linder; Anders Hermansson; John Liebeschuetz; Tore Brinck
Journal:  J Mol Model       Date:  2010-06-24       Impact factor: 1.810

4.  Probing the effect of point mutations at protein-protein interfaces with free energy calculations.

Authors:  Martin Almlöf; Johan Aqvist; Arne O Smalås; Bjørn O Brandsdal
Journal:  Biophys J       Date:  2005-11-04       Impact factor: 4.033

5.  Molecular dynamics of a protein surface: ion-residues interactions.

Authors:  Ran Friedman; Esther Nachliel; Menachem Gutman
Journal:  Biophys J       Date:  2005-05-13       Impact factor: 4.033

6.  A combination of docking, QM/MM methods, and MD simulation for binding affinity estimation of metalloprotein ligands.

Authors:  Akash Khandelwal; Viera Lukacova; Dogan Comez; Daniel M Kroll; Soumyendu Raha; Stefan Balaz
Journal:  J Med Chem       Date:  2005-08-25       Impact factor: 7.446

7.  Ligand binding to the voltage-gated Kv1.5 potassium channel in the open state--docking and computer simulations of a homology model.

Authors:  Martin Andér; Victor B Luzhkov; Johan Aqvist
Journal:  Biophys J       Date:  2007-09-28       Impact factor: 4.033

8.  Computational design of a Diels-Alderase from a thermophilic esterase: the importance of dynamics.

Authors:  Mats Linder; Adam Johannes Johansson; Tjelvar S G Olsson; John Liebeschuetz; Tore Brinck
Journal:  J Comput Aided Mol Des       Date:  2012-09-16       Impact factor: 3.686

9.  Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity.

Authors:  Peng Zhou; Congcong Wang; Feifei Tian; Yanrong Ren; Chao Yang; Jian Huang
Journal:  J Comput Aided Mol Des       Date:  2013-01-10       Impact factor: 3.686

10.  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

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