Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

ABSTRACT:

Background

Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only.

Results

This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of th

SUBMITTER: Srinivasulu YS 

PROVIDER: S-EPMC4682391 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature