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Prediction of ligand binding using an approach designed to accommodate diversity in protein-ligand interactions.

ABSTRACT: Computational determination of protein-ligand interaction potential is important for many biological applications including virtual screening for therapeutic drugs. The novel internal consensus scoring strategy is an empirical approach with an extended set of 9 binding terms combined with a neural network capable of analysis of diverse complexes. Like conventional consensus methods, internal consensus is capable of maintaining multiple distinct representations of protein-ligand interactions. In a typical use the method was trained using ligand classification data (binding/no binding) for a single receptor. The internal consensus analyses successfully distinguished protein-ligand complexes from decoys (r², 0.895 for a series of typical proteins). Results are superior to other tested empirical methods. In virtual screening experiments, internal consensus analyses provide consistent enrichment as determined by ROC-AUC and pROC metrics.

SUBMITTER: Marsh L

PROVIDER: S-EPMC3157911 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Prediction of ligand binding using an approach designed to accommodate diversity in protein-ligand interactions.

Marsh Lorraine L

PloS one 20110810 8

Computational determination of protein-ligand interaction potential is important for many biological applications including virtual screening for therapeutic drugs. The novel internal consensus scoring strategy is an empirical approach with an extended set of 9 binding terms combined with a neural network capable of analysis of diverse complexes. Like conventional consensus methods, internal consensus is capable of maintaining multiple distinct representations of protein-ligand interactions. In ...[more]

PMID: 21860668

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