Exploiting a reduced set of weighted average features to improve prediction of DNA-binding residues from 3D structures.

ABSTRACT: Predicting DNA-binding residues from a protein three-dimensional structure is a key task of computational structural proteomics. In the present study, based on machine learning technology, we aim to explore a reduced set of weighted average features for improving prediction of DNA-binding residues on protein surfaces. Via constructing the spatial environment around a DNA-binding residue, a novel weighting factor is first proposed to quantify the distance-dependent contribution of each neighboring residue in determining the location of a binding residue. Then, a weighted average scheme is introduced to represent the surface patch of the considering residue. Finally, the classifier is trained on the reduced set of these weighted average features, consisting of evolutionary profile, interface

SUBMITTER: Xiong Y 

PROVIDER: S-EPMC3234263 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature