Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks.

ABSTRACT: Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA structure, RNA structure is far less constrained by basepair hydrogen bonding, resulting in an explosion of potential stable states. Here, we propose a convolutional neural network that predicts all pairwise distances between residues in an RNA, using a recently described smooth parametrization of Euclidean distance matrices. We achieve high-accuracy predictions on RNAs up to 100 nt in length in fractions of a second, a factor of 10⁷ faster than existing molecular dynamics-based methods. We also convert our coarse-grained machine lear

SUBMITTER: Sha CM 

PROVIDER: S-EPMC11393712 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature