Deciphering DNA's Sequence-Dependent Structure and Deformability with Normalizing Flows

The sequence-dependent structure and deformability of double-stranded DNA play key roles in many cellular processes. Accurate description of DNA's conformational behavior has thus been a long-standing problem. Previous approaches to this problem assume a specific functional form for the elastic energy in terms of the internal coordinates of the DNA double-helix. The conformational flexibility of DNA, however, is strongly impacted by several stereochemical 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, this approach offers a wide range of future applications and can also be extended to model the conformational flexibility of other biomolecules with similar complexity.