Development of a protein property descriptor that are no superimposition required and are robust to conformational changes with 3D grid points

Abstract

There has been reported several works about in silico comprehensive predictions of compound-target interactions across a variety of target proteins and ligands based on descriptors of proteins and ligands. Here we propose a new protein descriptors based on the 3D grid points around proteins in order to take account of 3D shapes and/or physicochemical properties of proteins. Essentially proteins should be aligned before calculating grid point data and the conformational change of protein should be take into account. It makes difficult to use 3D grid point data for comprehensive activity prediction. In order to solve this problem, we discussed to use the principal component score as the descriptor after performing principal component analysis on a set of 3D grid point created from structures with different orientations, and utilize wider grid point spacing than used for common docking or CoMFA in order to reduce sensitivity to conformational changes. In our work, we performed two validation to confirm these.