Literature DB >> 8577696

Empirical free energy calculations of ligand-protein crystallographic complexes. I. Knowledge-based ligand-protein interaction potentials applied to the prediction of human immunodeficiency virus 1 protease binding affinity.

G Verkhivker1, K Appelt, S T Freer, J E Villafranca.   

Abstract

The steadily increasing number of high-resolution human immunodeficiency virus (HIV) 1 protease complexes has been the impetus for the elaboration of knowledge-based mean field ligand-protein interaction potentials. These potentials have been linked with the hydrophobicity and conformational entropy scales developed originally to explain protein folding and stability. Empirical free energy calculations of a diverse set of HIV-1 protease crystallographic complexes have enabled a detailed analysis of binding thermodynamics. The thermodynamic consequences of conformational changes that HIV-1 protease undergoes upon binding to all inhibitors, and a substantial concomitant loss of conformational entropy by the part of HIV-1 protease that forms the ligand-protein interface, have been examined. The quantitative breakdown of the entropy-driven changes occurring during ligand-protein association, such as the hydrophobic contribution, the conformational entropy term and the entropy loss due to a reduction of rotational and translational degrees of freedom, of a system composed to ligand, protein and crystallographic water molecules at the ligand-protein interface has been carried out. The proposed approach provides reasonable estimates of distinctions in binding affinity and gives an insight into the nature of enthalpyentropy compensation factors detected in the binding process.

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Year:  1995        PMID: 8577696     DOI: 10.1093/protein/8.7.677

Source DB:  PubMed          Journal:  Protein Eng        ISSN: 0269-2139


  27 in total

1.  Analysis of knowledge-based protein-ligand potentials using a self-consistent method.

Authors:  J Shimada; A V Ishchenko; E I Shakhnovich
Journal:  Protein Sci       Date:  2000-04       Impact factor: 6.725

2.  Scoring functions: a view from the bench.

Authors:  J R Tame
Journal:  J Comput Aided Mol Des       Date:  1999-03       Impact factor: 3.686

3.  Selecting near-native conformations in homology modeling: the role of molecular mechanics and solvation terms.

Authors:  A Janardhan; S Vajda
Journal:  Protein Sci       Date:  1998-08       Impact factor: 6.725

4.  Protein ligand docking based on empirical method for binding affinity estimation.

Authors:  P Tao; L Lai
Journal:  J Comput Aided Mol Des       Date:  2001-05       Impact factor: 3.686

5.  A Bayesian molecular interaction library.

Authors:  Ville-Veikko Rantanen; Mats Gyllenberg; Timo Koski; Mark S Johnson
Journal:  J Comput Aided Mol Des       Date:  2003-07       Impact factor: 3.686

6.  Inhibition and substrate recognition--a computational approach applied to HIV protease.

Authors:  H M Vinkers; M R de Jonge; E D Daeyaert; J Heeres; L M H Koymans; J H van Lenthe; P J Lewi; H Timmerman; P A J Janssen
Journal:  J Comput Aided Mol Des       Date:  2003-09       Impact factor: 3.686

7.  Scoring and lessons learned with the CSAR benchmark using an improved iterative knowledge-based scoring function.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2011-08-31       Impact factor: 4.956

8.  Processing multimode binding situations in simulation-based prediction of ligand-macromolecule affinities.

Authors:  Akash Khandelwal; Viera Lukacova; Daniel M Kroll; Soumyendu Raha; Dogan Comez; Stefan Balaz
Journal:  J Phys Chem A       Date:  2005-07-28       Impact factor: 2.781

9.  Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes.

Authors:  M D Eldridge; C W Murray; T R Auton; G V Paolini; R P Mee
Journal:  J Comput Aided Mol Des       Date:  1997-09       Impact factor: 3.686

10.  Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2010-02-22       Impact factor: 4.956

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