PT - JOURNAL ARTICLE AU - Kagaya, Yuki AU - Zhang, Zicong AU - Ibtehaz, Nabil AU - Wang, Xiao AU - Nakamura, Tsukasa AU - Huang, David AU - Kihara, Daisuke TI - NuFold: A Novel Tertiary RNA Structure Prediction Method Using Deep Learning with Flexible Nucleobase Center Representation AID - 10.1101/2023.09.20.558715 DP - 2023 Jan 01 TA - bioRxiv PG - 2023.09.20.558715 4099 - http://biorxiv.org/content/early/2023/09/22/2023.09.20.558715.short 4100 - http://biorxiv.org/content/early/2023/09/22/2023.09.20.558715.full AB - RNA is not only playing a core role in the central dogma as mRNA between DNA and protein, but also many non-coding RNAs have been discovered to have unique and diverse biological functions. As genome sequences become increasingly available and our knowledge of RNA sequences grows, the study of RNA’s structure and function has become more demanding. However, experimental determination of three-dimensional RNA structures is both costly and time-consuming, resulting in a substantial disparity between RNA sequence data and structural insights. In response to this challenge, we propose a novel computational approach that harnesses state-of-the-art deep learning architecture NuFold to accurately predict RNA tertiary structures. This approach aims to offer a cost-effective and efficient means of bridging the gap between RNA sequence information and structural comprehension. NuFold implements a nucleobase center representation, which allows it to reproduce all possible nucleotide conformations accurately.Competing Interest StatementThe authors have declared no competing interest.