Accurate Prediction of B-form/A-form DNA Conformation Propensity from Primary Sequence: A Machine Learning and Free energy Handshake
Preprints are manuscripts made publicly available before they have been submitted for formal peer review and publication. They might contain new research findings or data. Preprints can be a draft or final version of an author's research but must not have been accepted for publication at the time of submission.
DNA carries the genetic code of life. Different conformations of DNA are associated with various biological functions. Predicting the conformation of DNA from its primary sequence, although desirable, is a challenging problem owing to the polymorphic nature of DNA. Although a few efforts were made in this regard, currently there exists no method that can accurately predict the conformation of right- handed DNA solely from the sequence. In this study, we present a novel approach based on machine learning that predicts A-DNA and B-DNA conformational propensities of a sequence with high accuracy (~95%). In addition, we show that the impact of the dinucleotide steps in determining the conformation agrees qualitatively with the free energy cost for A-DNA formation in water. This method enables us to examine the genomic sequence to understand the prospective biological roles played by the A-form of DNA.