Learning protein binding affinity using privileged information.

ABSTRACT:

Background

Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data.

Results

In this study, we propose a novel machine learning method for predicting binding affinity that uses protein 3D structure as privileged information at training time while expecting only protein sequence informatio

SUBMITTER: Abbasi WA 

PROVIDER: S-EPMC6238365 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature