title: Prediction of DNA-binding proteins from relational features.
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.
authors.affiliation: Czech Technical University, Prague, Czech Republic. szaboand@fel.cvut.cz.
authors.forename: Andrea
authors.initials: A
authors.lastname: Szabóová
authors.forename: Ondřej
authors.initials: O
authors.lastname: Kuželka
authors.forename: Filip
authors.initials: F
authors.lastname: Zelezný
authors.forename: Jakub
authors.initials: J
authors.lastname: Tolar