Knowledge based potentials: the reverse Boltzmann methodology, virtual screening and molecular weight dependence

C K Kirtay, John Blayney Owen Mitchell, J A Lumley

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

We discuss the rationale for using the reverse Boltzmann methodology to convert atom atom distance distributions from a knowledge base of protein-ligand complexes into energy-like functions. We also generate an updated version of the BLEEP statistical potential, using a dataset of 196 complexes. This performs similarly to the existing BLEEP. An algorithm is implemented to allow the automatic calculation of bond orders, and hence of the appropriate numbers of hydrogen atoms present. An attempt is made to generate a potential specific to strongly bound complexes; however, we find no evidence that this improves the prediction of binding affinities. We also discuss the range of binding energies available as a function of ligand molecular weight and derive some simple functions describing this behaviour.

Original languageEnglish
Pages (from-to)527-536
Number of pages10
JournalQSAR and Combinatorial Science
Volume24
Issue number4
DOIs
Publication statusPublished - Jun 2005

Keywords

  • binding affinity
  • scoring functions
  • knowledge-based potentials
  • virtual screening
  • reverse Boltzmann
  • molecular weight
  • PROTEIN-LIGAND INTERACTIONS
  • DE-NOVO DESIGN
  • BINDING AFFINITIES
  • SCORING FUNCTION
  • MEAN FORCE
  • FLEXIBLE DOCKING
  • 3-DIMENSIONAL STRUCTURES
  • STATISTICAL POTENTIALS
  • PREDICTION
  • DATABASES

Fingerprint

Dive into the research topics of 'Knowledge based potentials: the reverse Boltzmann methodology, virtual screening and molecular weight dependence'. Together they form a unique fingerprint.

Cite this