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Proteome Science

, 10:66

First Online: 12 November 2012Received: 28 March 2012Accepted: 26 October 2012


BackgroundThe 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.

ResultsPrediction 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.

ConclusionsWe 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.

KeywordsDNA-binding propensity prediction DNA-binding proteins Relational machine learning Electronic supplementary materialThe online version of this article doi:10.1186-1477-5956-10-66 contains supplementary material, which is available to authorized users.

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Autor: Andrea Szabóová - Ondřej Kuželka - Filip Železný - Jakub Tolar


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