GRID/CPCA: A new computational tool to design selective ligands

We present a computational procedure aimed at understanding enzyme selectiv ity and guiding the design of drugs with respect to selectivity. It starts from a set of 3D structures of the target proteins characterized by the pro gram GRID. In the multivariate description proposed, the variables are orga nized and scaled in a different way than previously published methodologies . Then, consensus principal component analysis (CPCA) is used to analyze th e GRID descriptors, allowing the straightforward identification of possible modifications in the ligand to improve its selectivity toward a chosen tar get. As an important new feature the computational method is able to work w ith more than two target proteins and with several 3D structures for each p rotein. Additionally, the use of a 'cutout tool' allows to focus on the imp ortant regions around the active site. The method is validated for a total number of nine structures of the three homologous serine proteases thrombin , trypsin, and factor Xa. The regions identified by the method as being imp ortant for selectivity are in excellent agreement with available experiment al data and inhibitor structure-activity relationships.