Deciphering DNA’s sequence-dependent structure and deformability with normalizing flows

The sequence-dependent structure and deformability of double-stranded DNA plays a key role in many cellular processes. Accurate description thereof has thus been a long-standing problem. Previous approaches to this problem assume a specific functional form for the elastic energy in terms of internal coordinates of the DNA double-helix. The conformational flexibility of DNA, however, is strongly impacted by several stereo-chemical effects that complicate the formulation of an accurate functional form. In this work, I propose an entirely new, AI-based method to decipher the sequence-dependent structure and deformability of double-stranded DNA. This method employs normalizing flows that capture multimodal and correlation effects between internal coordinates of the DNA double helix excellently, and hence allows one to accurately quantify deformation energies for any double-stranded DNA structure and sequence. Thus, it offers a wide range of future applications, and speaks in favor of AI-based elasticity-descriptions also for other molecules.