Semi-supervised segmentation of RNA 3D structures using density-based clustering.

ABSTRACT: A growing body of evidence shows that the biological activity of RNA molecules is not only due to their primary and secondary structures, but also to their spatial conformation. This is analogous to proteins, where investigating function, folding, or evolution often requires dividing the three-dimensional (3D) structure into subparts that can be studied individually. These independent substructures, known as protein "3D domains", are geometrically defined as compact and spatially separate regions of the polypeptide chain. In RNA macromolecules, however, and to the best of our knowledge, no equivalent 3D-based concept has yet been formulated. We present RNA3DClust, an application of the Mean Shift clustering algorithm to the RNA 3D structure partitioning problem. For this work, a dedicated

SUBMITTER: Le QK 

PROVIDER: S-EPMC12800371 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature