A New, Improved Hybrid Scoring Function for Molecular Docking and Scoring Based on AutoDock and AutoDock Vina.

Automated docking is one of the most important tools for structure-based drug design that allows prediction of ligand binding poses and also provides an estimate of how well small molecules fit in the binding site of a protein. A new scoring function based on AutoDock and AutoDock Vina has been introduced. The new hybrid scoring function is a linear combination of the two scoring function components derived from a multiple linear regression fitting procedure. The scoring function was built on a training set of 2412 protein-ligand complexes from pdbbind database (www.pdbbind.org.cn, version 2012). A test set of 313 complexes that appeared in the 2013 version was used for validation purposes. The new hybrid scoring function performed better than the original functions, both on training and test sets of protein-ligand complexes, as measured by the non-parametric Pearson correlation coefficient, R, mean absolute error (MAE), and root-mean-square error (RMSE) between the experimental binding affinities and the docking scores. The function also gave one of the best results among more than 20 scoring functions tested on the core set of the pdbbind database. The new AutoDock hybrid scoring function will be implemented in modified version of AutoDock.

A New Scoring Function for Molecular Docking Based on AutoDock and AutoDock Vina.

Molecular docking of small molecules in the protein binding sites is the most widely used computational technique in modern structure-based drug discovery. Although accurate prediction of binding modes of small molecules can be achieved in most cases, estimation of their binding affinities remains mediocre at best. As an attempt to improve the correlation between the inhibitory constants, pKi, and scoring, we created a new, hybrid scoring function. The new function is a linear combination of the terms of the scoring functions of AutoDock and AutoDock Vina. It was trained on 2,412 protein-ligand complexes from the PDBbind database (www.pdbbind.org.cn, version 2012) and validated on a set of 313 complexes released in the 2013 version as a test set. The new function was included in a modified version of AutoDock. The hybrid scoring function showed a statistically significant improvement in both training and test sets in terms of correlation with and root mean square and mean absolute errors in prediction of pKi values. It was also tested on the CSAR 2014 Benchmark Exercise dataset (team T) and produced reasonably good results.

Classification of binding site conformations of protein tyrosine phosphatase 1B.

Hundred and two binding sites from 91 Protein Data Bank files for protein tyrosine phosphatase 1B with different ligands have been compared. It was found that they can be divided into five clusters. Additional clusters were formed by the unliganded and oxidized enzyme. The centroids of the clusters can be used as starting points for further studies of enzyme-inhibitor interaction by computer simulations. A special software tool has been created for the investigation of protein tyrosine phosphatase 1B and other enzymes. It performs multiple comparisons of selected parts of Protein Data Bank files, as well as further clustering, and determines mobility of separate residues.