In-silico drug screening method based on the protein-compound affinity matrix using the factor selection technique.

We have developed a new in-silico drug screening method, a modified version of a docking score index (DSI) method, based on a protein-compound docking affinity matrix. By using this method, the docking scores are converted to the docking score indexes by the principal component analysis (PCA) method and each compound is projected into a PCA space. In this study, we propose a method to select a set of suitable principal component axes and evaluate the database enrichment for 12 target proteins.

An efficient in silico screening method based on the protein-compound affinity matrix and its application to the design of a focused library for cytochrome P450 (CYP) ligands.

A new method has been developed to design a focused library based on available active compounds using protein-compound docking simulations. This method was applied to the design of a focused library for cytochrome P450 (CYP) ligands, not only to distinguish CYP ligands from other compounds but also to identify the putative ligands for a particular CYP. Principal component analysis (PCA) was applied to the protein-compound affinity matrix, which was obtained by thorough docking calculations between a large set of protein pockets and chemical compounds.

Computational and NMR analyses for the identification of bound water molecules in ribonuclease T1.

A structural characterization of bound water molecules in ribonuclease T1 (RNase T1) was carried out by nuclear magnetic resonance spectroscopy and molecular dynamics simulation. Amide protons of residues Trp59, Leu62, Tyr68 and Phe100 were found to cross-relax with protons of bound waters. Molecular dynamics simulations of the 120 water molecules observed in the free form of the crystal structure indicate that these amide protons donate hydrogen bonds to the less mobile water molecules.