Abstract
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 (~93%). 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. We are hopeful that our methodology can be employed on segments of the genomic sequence to understand the prospective biological roles played by the A-form of DNA.