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PMID: 15562990 Published · ppublish English Journal Article

PLASS: protein-ligand affinity statistical score--a knowledge-based force-field model of interaction derived from the PDB.

Journal of computer-aided molecular design ·Vol. 18 ·No. 4 ·2004-04-00 ·Pages 261-70

Ozrin VD, Subbotin MV, Nikitin SM

Abstract

We have developed PLASS (Protein-Ligand Affinity Statistical Score), a pair-wise potential of mean-force for rapid estimation of the binding affinity of a ligand molecule to a protein active site. This scoring function is derived from the frequency of occurrence of atom-type pairs in crystallographic complexes taken from the Protein Data Bank (PDB). Statistical distributions are converted into distance-dependent contributions to the Gibbs free interaction energy for 10 atomic types using the Boltzmann hypothesis, with only one adjustable parameter. For a representative set of 72 protein-ligand structures, PLASS scores correlate well with the experimentally measured dissociation constants: a correlation coefficient R of 0.82 and RMS error of 2.0 kcal/mol. Such high accuracy results from our novel treatment of the volume correction term, which takes into account the inhomogeneous properties of the protein-ligand complexes. PLASS is able to rank reliably the affinity of complexes which have as much diversity as in the PDB.

MeSH Terms
Computational Biology Data Interpretation, Statistical Databases, Protein Ligands Models, Molecular Protein Binding Proteins/chemistry,metabolism Software
Chemicals
Ligands Proteins
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Ozrin V D
Algodign LLC, Bolshaya Sadovaya 8, Moscow 123379, Russian Federation. Vladimir.Ozrin@Algodign.com
Subbotin M V
Nikitin S M
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6 references, click to expand
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Article Info
Journal
Journal of computer-aided molecular design
Abbr.
J Comput Aided Mol Des
ISSN
0920-654X
Published
2004-04-00
Pages
261-70
Language
English
Region
Netherlands
NLM ID
8710425
Subset
IM
Analysis Services
Analysis Services

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