When will RNA get its AlphaFold moment?

0582988 - BTÚ 2024 RIV US eng J - Journal Article
Schneider, Bohdan - Sweeney, B. A. - Bateman, A. - Černý, Jiří - Zok, T. - Szachniuk, M.
When will RNA get its AlphaFold moment?
Nucleic Acids Research. Roč. 51, č. 18 (2023), s. 9522-9352. ISSN 0305-1048. E-ISSN 1362-4962
R&D Projects: GA MŠMT(CZ) LM2023055
Research Infrastructure: ELIXIR-CZ II - 90131
Institutional support: RVO:86652036
Keywords : conformation-dependent restraints * 3-dimensional structure * structure prediction * ribosomal-subunit
OECD category: Biochemistry and molecular biology
Impact factor: 14.9, year: 2022
Method of publishing: Open access
https://academic.oup.com/nar/article/51/18/9522/7272628?login=true

The protein structure prediction problem has been solved for many types of proteins by AlphaFold. Recently, there has been considerable excitement to build off the success of AlphaFold and predict the 3D structures of RNAs. RNA prediction methods use a variety of techniques, from physics-based to machine learning approaches. We believe that there are challenges preventing the successful development of deep learning-based methods like AlphaFold for RNA in the short term. Broadly speaking, the challenges are the limited number of structures and alignments making data-hungry deep learning methods unlikely to succeed. Additionally, there are several issues with the existing structure and sequence data, as they are often of insufficient quality, highly biased and missing key information. Here, we discuss these challenges in detail and suggest some steps to remedy the situation. We believe that it is possible to create an accurate RNA structure prediction method, but it will require solving several data quality and volume issues, usage of data beyond simple sequence alignments, or the development of new less data-hungry machine learning methods.
Permanent Link: https://hdl.handle.net/11104/0351031