NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation.

ABSTRACT: RNA plays a crucial role not only in information transfer as messenger RNA during gene expression but also in various biological functions as non-coding RNAs. Understanding mechanical mechanisms of function needs tertiary structure information; however, experimental determination of three-dimensional RNA structures is costly and time-consuming, leading to a substantial gap between RNA sequence and structural data. To address this challenge, we developed NuFold, a novel computational approach that leverages state-of-the-art deep learning architecture to accurately predict RNA tertiary structures. NuFold is a deep neural network trained end-to-end for the output structure from the input sequence. NuFold incorporates a nucleobase center representation, which enables flexible conformation of r

SUBMITTER: Kagaya Y 

PROVIDER: S-EPMC11751094 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature