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

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…

• NS
  National Science Foundation

  Awards: DMS2151678, ACI-1548562, -1548562, 1548562, DBI2146026, MCB1925643, DBI2003635
• NI
  National Institutes of Health

  Awards: 1548562, IIS2211598, ACI-1548562, R01GM123055, R01GM133840, -1548562
• NI
  National Institute of General Medical Sciences

  Award: R01GM133840, R01GM123055
• DO
  Division of Mathematical Sciences

  Award: DMS2151678
• DO
  Division of Information and Intelligent Systems

  Award: IIS2211598
• DO
  Division of Civil, Mechanical and Manufacturing Innovation

  Award: CMMI1825941
• DO
  Division of Molecular and Cellular Biosciences

  Award: MCB1925643
• DO
  Division of Biological Infrastructure

  Award: DBI2003635, DBI2146026