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Computational protocol for predicting the binding affinities of zinc containing metalloprotein–ligand complexes
Author(s) -
Jain Tarun,
Jayaram B.
Publication year - 2007
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21332
Subject(s) - metalloprotein , chemistry , zinc , affinities , ligand (biochemistry) , binding affinities , computational chemistry , protein ligand , crystallography , metal , stereochemistry , biochemistry , receptor , organic chemistry
Zinc is one of the most important metal ions found in proteins performing specific functions associated with life processes. Coordination geometry of the zinc ion in the active site of the metalloprotein–ligand complexes poses a challenge in determining ligand binding affinities accurately in structure‐based drug design. We report here an all atom force field based computational protocol for estimating rapidly the binding affinities of zinc containing metalloprotein–ligand complexes, considering electrostatics, van der Waals, hydrophobicity, and loss in conformational entropy of protein side chains upon ligand binding along with a nonbonded approach to model the interactions of the zinc ion with all the other atoms of the complex. We examined the sensitivity of the binding affinity predictions to the choice of Lennard‐Jones parameters, partial atomic charges, and dielectric treatments adopted for system preparation and scoring. The highest correlation obtained was R 2 = 0.77 ( r = 0.88) for the predicted binding affinity against the experiment on a heterogenous dataset of 90 zinc containing metalloprotein–ligand complexes consisting of five unique protein targets. Model validation and parameter analysis studies underscore the robustness and predictive ability of the scoring function. The high correlation obtained suggests the potential applicability of the methodology in designing novel ligands for zinc–metalloproteins. The scoring function has been web enabled for free access at www.scfbio‐iitd.res.in/software/drugdesign/bapplz.jsp as BAPPL‐Z server (Binding Affinity Prediction of Protein–Ligand complexes containing Zinc metal ions). Proteins 2007. © 2007 Wiley‐Liss, Inc.