title: 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.
authors.affiliation: Department of Biology, Long Island University, Brooklyn, New York, United States of America. lmarsh@liu.edu
authors.forename: Lorraine
authors.initials: L
authors.lastname: Marsh
mesh_terms: D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D004353:Drug Evaluation, Preclinical; D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D014584:User-Computer Interface
chemical_list: D008024:Ligands; D011506:Proteins