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

V D Ozrin1, M V Subbotin, S M Nikitin

  • 1Algodign LLC, Bolshaya Sadovaya 8, Moscow 123379, Russian Federation. Vladimir.Ozrin@Algodign.com

Journal of Computer-Aided Molecular Design
|November 26, 2004
PubMed
Summary

We created PLASS (Protein-Ligand Affinity Statistical Score), a fast method to predict how strongly molecules bind to proteins. It uses protein-ligand structures from the PDB to accurately estimate binding affinity.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Estimating protein-ligand binding affinity is crucial for drug discovery.
  • Existing methods can be computationally expensive or lack accuracy.
  • A need exists for rapid and reliable binding affinity prediction tools.

Purpose of the Study:

  • To develop a novel scoring function, PLASS (Protein-Ligand Affinity Statistical Score), for rapid estimation of protein-ligand binding affinity.
  • To validate the accuracy and reliability of PLASS against experimental data.
  • To provide a tool capable of handling diverse protein-ligand complexes.

Main Methods:

  • PLASS is a pair-wise potential of mean-force scoring function.
  • It is derived from atom-type pair frequencies in Protein Data Bank (PDB) crystallographic complexes.

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  • Statistical distributions are converted to distance-dependent Gibbs free energy contributions using the Boltzmann hypothesis, with a novel volume correction term.

    • Main Results:

    • PLASS scores show a strong correlation (R=0.82) with experimentally measured dissociation constants for 72 protein-ligand structures.
    • Achieved a Root Mean Square (RMS) error of 2.0 kcal/mol.
    • Demonstrated reliable ranking of binding affinities across diverse PDB complexes.

    Conclusions:

    • PLASS provides a rapid and accurate method for estimating protein-ligand binding affinity.
    • The novel treatment of volume correction enhances accuracy by accounting for complex inhomogeneity.
    • PLASS is a valuable tool for virtual screening and drug design in computational chemistry and structural biology.