Prediction of DNA-binding proteins from relational features
Typ dokumentu
článek z elektronického periodikaAutor
Szabóová, Andrea
Kuželka, Ondřej
Železný, Filip
Tolar, Jakub
Práva
open accessMetadata
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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.
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