A graph kernel approach for alignment-free domain-peptide interaction prediction with an application to human SH3 domains.

ABSTRACT:

Motivation

State-of-the-art experimental data for determining binding specificities of peptide recognition modules (PRMs) is obtained by high-throughput approaches like peptide arrays. Most prediction tools applicable to this kind of data are based on an initial multiple alignment of the peptide ligands. Building an initial alignment can be error-prone, especially in the case of the proline-rich peptides bound by the SH3 domains.

Results

Here, we present a machine-learning approach based on an efficient graph-kernel technique to predict the specificity of a large set of 70 human SH3 domains, which are an important class of PRMs. The graph-kernel strategy allows us to (i) integrate several types of physico-chemical information for each amino acid, (ii) consider high-order cor

SUBMITTER: Kundu K 

PROVIDER: S-EPMC3694653 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature