title: PLASS: protein-ligand affinity statistical score--a knowledge-based force-field model of interaction derived from the PDB.
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.
authors.affiliation: Algodign LLC, Bolshaya Sadovaya 8, Moscow 123379, Russian Federation. Vladimir.Ozrin@Algodign.com
authors.forename: V D
authors.initials: VD
authors.lastname: Ozrin
authors.forename: M V
authors.initials: MV
authors.lastname: Subbotin
authors.forename: S M
authors.initials: SM
authors.lastname: Nikitin
mesh_terms: D019295:Computational Biology; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D008024:Ligands; D008958:Models, Molecular; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012984:Software
chemical_list: D008024:Ligands; D011506:Proteins