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.

Finally, my paper on describing dsDNA's conformational behavior with normalizing flow models has been published in JCTC!

pubs.acs.org/doi/full/10....

Membrane Remodeling by the Collective Action of Caveolin-1 Caveolin-1 proteins scaffold 50-100nm large invaginations in the plasma membrane to mediate critical cellular processes. As revealed recently by cryo-electron microscopy, several caveolin-1 protomers ...

Our new work "Membrane Remodeling by the Collective Action of Caveolin-1" is now online on biorxiv!
We developed a bottom-up coarse-grained model to show how caveolin complexes cooperatively bend the membrane and form large invaginations.
www.biorxiv.org/content/10.1...

A Hybrid Bottom-Up and Data-Driven Machine Learning Approach for Accurate Coarse-Graining of Large Molecular Complexes Bottom-up coarse-graining refers to the development of low-resolution simulation models that are thermodynamically consistent with certain distributions from fully atomistic simulations. Force-matchin...

Our new paper is out:

A Hybrid Bottom-Up and Data-Driven Machine Learning Approach for Accurate Coarse-Graining of Large Molecular Complexes | Journal of Chemical Theory and Computation pubs.acs.org/doi/10.1021/...

I have developed a new AI model to describe the sequence-dependent structure and deformability of DNA. Check out previous post!

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 appr...

www.biorxiv.org/content/10.1...