Machine Learning in RNA Structure Prediction: Advances and Challenges

Overview

Journal Biophys J

Publisher Cell Press

Specialty Biophysics

Date 2024 Feb 1

PMID 38297836

Authors

Sicheng Zhang

Jun Li

Shi-Jie Chen

Affiliations

Soon will be listed here.

Abstract

RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges. In addition, we highlight the advantages and challenges of constructing RNA language models. Given the rapid advances of machine learning techniques, we anticipate that machine learning-based models will serve as important tools for predicting RNA structures, thereby enriching our understanding of RNA structures and their corresponding functions.

Citing Articles

Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.

Bahai A, Keong Kwoh C, Mu Y, Li Y PLoS Comput Biol. 2025; 20(12):e1012715.

PMID: 39775239 PMC: 11723642. DOI: 10.1371/journal.pcbi.1012715.

mRNA vaccine sequence and structure design and optimization: Advances and challenges.

Jin L, Zhou Y, Zhang S, Chen S J Biol Chem. 2024; 301(1):108015.

PMID: 39608721 PMC: 11728972. DOI: 10.1016/j.jbc.2024.108015.

Machine learning tools advance biophysics.

Schlick T, Wei G Biophys J. 2024; 123(17):E1-E3.

PMID: 39173628 PMC: 11393673. DOI: 10.1016/j.bpj.2024.07.036.

References

Poblete S, Bottaro S, Bussi G . A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs. Nucleic Acids Res. 2017; 46(4):1674-1683. PMC: 5829650. DOI: 10.1093/nar/gkx1269. View

Xiong P, Wu R, Zhan J, Zhou Y . Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement. Nat Commun. 2021; 12(1):2777. PMC: 8119458. DOI: 10.1038/s41467-021-23100-4. View

Singh J, Paliwal K, Zhang T, Singh J, Litfin T, Zhou Y . Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning. Bioinformatics. 2021; 37(17):2589-2600. DOI: 10.1093/bioinformatics/btab165. View

Wang J, Wang J, Huang Y, Xiao Y . 3dRNA v2.0: An Updated Web Server for RNA 3D Structure Prediction. Int J Mol Sci. 2019; 20(17). PMC: 6747482. DOI: 10.3390/ijms20174116. View

Li Y, Zhang C, Feng C, Pearce R, Freddolino P, Zhang Y . Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction. Nat Commun. 2023; 14(1):5745. PMC: 10505173. DOI: 10.1038/s41467-023-41303-9. View

Watkins A, Rangan R, Das R . FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds. Structure. 2020; 28(8):963-976.e6. PMC: 7415647. DOI: 10.1016/j.str.2020.05.011. View

Mattick J, Amaral P, Carninci P, Carpenter S, Chang H, Chen L . Long non-coding RNAs: definitions, functions, challenges and recommendations. Nat Rev Mol Cell Biol. 2023; 24(6):430-447. PMC: 10213152. DOI: 10.1038/s41580-022-00566-8. View

Xu X, Chen S . A Method to Predict the Structure and Stability of RNA/RNA Complexes. Methods Mol Biol. 2016; 1490:63-72. PMC: 5508871. DOI: 10.1007/978-1-4939-6433-8_5. View

Sloma M, Mathews D . Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures. RNA. 2016; 22(12):1808-1818. PMC: 5113201. DOI: 10.1261/rna.053694.115. View

10.

Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W . Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023; 379(6637):1123-1130. DOI: 10.1126/science.ade2574. View

11.

Sulc P, Romano F, Ouldridge T, Doye J, Louis A . A nucleotide-level coarse-grained model of RNA. J Chem Phys. 2014; 140(23):235102. DOI: 10.1063/1.4881424. View

12.

Zhang D, Chen S . IsRNA: An Iterative Simulated Reference State Approach to Modeling Correlated Interactions in RNA Folding. J Chem Theory Comput. 2018; 14(4):2230-2239. PMC: 6311103. DOI: 10.1021/acs.jctc.7b01228. View

13.

Popenda M, Szachniuk M, Antczak M, Purzycka K, Lukasiak P, Bartol N . Automated 3D structure composition for large RNAs. Nucleic Acids Res. 2012; 40(14):e112. PMC: 3413140. DOI: 10.1093/nar/gks339. View

14.

Das R, Karanicolas J, Baker D . Atomic accuracy in predicting and designing noncanonical RNA structure. Nat Methods. 2010; 7(4):291-4. PMC: 2854559. DOI: 10.1038/nmeth.1433. View

15.

Wu K, Zou J, Chang H . Machine learning modeling of RNA structures: methods, challenges and future perspectives. Brief Bioinform. 2023; 24(4). DOI: 10.1093/bib/bbad210. View

16.

Zhang Y, Lang M, Jiang J, Gao Z, Xu F, Litfin T . Multiple sequence alignment-based RNA language model and its application to structural inference. Nucleic Acids Res. 2023; 52(1):e3. PMC: 10783488. DOI: 10.1093/nar/gkad1031. View

17.

Reuter J, Mathews D . RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinformatics. 2010; 11:129. PMC: 2984261. DOI: 10.1186/1471-2105-11-129. View

18.

Lorenz R, Bernhart S, Honer Zu Siederdissen C, Tafer H, Flamm C, Stadler P . ViennaRNA Package 2.0. Algorithms Mol Biol. 2011; 6:26. PMC: 3319429. DOI: 10.1186/1748-7188-6-26. View

19.

Cheng Y, Zhang S, Xu X, Chen S . Vfold2D-MC: A Physics-Based Hybrid Model for Predicting RNA Secondary Structure Folding. J Phys Chem B. 2021; 125(36):10108-10118. PMC: 8903033. DOI: 10.1021/acs.jpcb.1c04731. View

20.

AlQuraishi M . AlphaFold at CASP13. Bioinformatics. 2019; 35(22):4862-4865. PMC: 6907002. DOI: 10.1093/bioinformatics/btz422. View