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Open Access Research

Prediction of DNA-binding proteins from relational features

Andrea Szabóová1*, Ondřej Kuželka1, Filip Železný1 and Jakub Tolar2

Author Affiliations

1 Czech Technical University, Prague, Czech Republic

2 University of Minnesota, Minneapolis, USA

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Proteome Science 2012, 10:66  doi:10.1186/1477-5956-10-66

Published: 12 November 2012

Abstract

Background

The process of protein-DNA binding has an essential role in the biological processing of genetic information. We use relational machine learning to predict DNA-binding propensity of proteins from their structures. Automatically discovered structural features are able to capture some characteristic spatial configurations of amino acids in proteins.

Results

Prediction based only on structural relational features already achieves competitive results to existing methods based on physicochemical properties on several protein datasets. Predictive performance is further improved when structural features are combined with physicochemical features. Moreover, the structural features provide some insights not revealed by physicochemical features. Our method is able to detect common spatial substructures. We demonstrate this in experiments with zinc finger proteins.

Conclusions

We introduced a novel approach for DNA-binding propensity prediction using relational machine learning which could potentially be used also for protein function prediction in general.

Keywords:
DNA-binding propensity prediction; DNA-binding proteins; Relational machine learning