Abstract
We contribute a novel, ball-histogram approach to DNA-binding propensity prediction of proteins. Unlike state-of-the-art methods based on constructing an ad-hoc set of features describing the charged patches of the proteins, the ball-histogram technique enables a systematic, Monte-Carlo exploration of the spatial distribution of charged amino acids, capturing joint probabilities of specified amino acids occurring in certain distances from each other. This exploration yields a model for the prediction of DNA binding propensity. We validate our method in prediction experiments, achieving favorable accuracies. Moreover, our method also provides interpretable features involving spatial distributions of selected amino acids.
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Szabóová, A., Kuželka, O., Morales E., S., Železný, F., Tolar, J. (2011). Prediction of DNA-Binding Propensity of Proteins by the Ball-Histogram Method. In: Chen, J., Wang, J., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2011. Lecture Notes in Computer Science(), vol 6674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21260-4_34
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DOI: https://doi.org/10.1007/978-3-642-21260-4_34
Publisher Name: Springer, Berlin, Heidelberg
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