Predicting protein-ligand binding affinities: a low scoring game?
We have investigated the performance of five well known scoring functions in predicting the binding affinities of a diverse set of 205 protein-ligand complexes with known experimental binding constants, and also on subsets of mutually similar complexes. We have found that the overall performance of the scoring functions on the diverse set is disappointing, with none of the functions achieving r(2) values above 0.32 on the whole dataset. Performance on the subsets was mixed, with four of the five functions predicting fairly well the binding affinities of 35 proteinases, but none of the functions producing any useful correlation on a set of 38 aspartic proteinases. We consider two algorithms for producing consensus scoring functions, one based on a linear combination of scores from the five individual functions and the other on averaging the rankings produced by the five functions. We find that both algorithms produce consensus functions that generally perform slightly better than the best individual scoring function on a given dataset.
Marsden
Philip M
PM
Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
Puvanendrampillai
Dushyanthan
D
Mitchell
John B O
JB
Glen
Robert C
RC
D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D008024:Ligands; D008958:Models, Molecular; D008968:Molecular Conformation; D011485:Protein Binding; D011506:Proteins / Q000378:metabolism*; D021281:Quantitative Structure-Activity Relationship
D008024:Ligands; D011506:Proteins