Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model
by
Kirill Batmanov and Junbai Wang *
Kirill Batmanov and Junbai Wang *
Department of Pathology, Oslo University Hospital—Norwegian Radium Hospital, Montebello, 0310 Oslo,Norway
*
Author to whom correspondence should be addressed.
Genes 2017, 8(9), 233; https://doi.org/10.3390/genes8090233
Received: 31 July 2017 / Revised: 13 September 2017 / Accepted: 13 September 2017 / Published: 18 September 2017
(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)
DNA shape readout is an important mechanism of transcription factor target site recognition, in addition to the sequence readout. Several machine learning-based models of transcription factor–DNA interactions, considering DNA shape features, have been developed in recent years. Here, we present a new biophysical model of protein–DNA interactions by integrating the DNA shape properties. It is based on the neighbor dinucleotide dependency model BayesPI2, where new parameters are restricted to a subspace spanned by the dinucleotide form of DNA shape features. This allows a biophysical interpretation of the new parameters as a position-dependent preference towards specific DNA shape features. Using the new model, we explore the variation of DNA shape preferences in several transcription factors across various cancer cell lines and cellular conditions. The results reveal that there are DNA shape variations at FOXA1 (Forkhead Box Protein A1) binding sites in steroid-treated MCF7 cells. The new biophysical model is useful for elucidating the finer details of transcription factor–DNA interaction, as well as for predicting cancer mutation effects in the future.
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Keywords:
transcription factors; DNA shape; protein–DNA interaction
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Batmanov, K.; Wang, J. Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model. Genes 2017, 8, 233.
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