The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective
Open accessAbstract
Any method for RNA secondary structure prediction is determined by four ingredients. The architecture is the choice of features implemented by the model (such as stacked basepairs, loop length distributions, etc.). The architecture determines the number of parameters in the model. The scoring scheme is the nature of those parameters (whether thermodynamic, probabilistic, or weights). The parameterization stands for the specific values assigned to the parameters. These three ingredients are referred to as "the model." The fourth ingredient is the folding algorithms used to predict plausible secondary structures given the model and the sequence of a structural RNA. Here, I make several unifying observations drawn from looking at more than 40 years of methods for RNA secondary structure prediction in the light of this classification. As a final observation, there seems to be a performance ceiling that affects all methods with complex architectures, a ceiling that impacts all scoring schemes with remarkable similarity. This suggests that modeling RNA secondary structure by using intrinsic sequence-based plausible "foldability" will require the incorporation of other forms of information in order to constrain the folding space and to improve prediction accuracy. This could give an advantage to probabilistic scoring systems since a probabilistic framework is a natural platform to incorporate different sources of information into one single inference problem.
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SCFGs can represent the RNA folding energy model.
“SCFGs can be used to represent the RNA folding energy model39.”
From Algorithm for optimized mRNA design improves stability and immunogenicity (2023) · cites this paper as [39]
The Architecture and Scoring Scheme sections say that a context-free grammar (CFG) can represent RNA folding methods and can use thermodynamic free-energy scores. But the paper distinguishes CFGs from stochastic CFGs (SCFGs), which use probabilistic scores; it does not say that an SCFG directly represents the thermodynamic energy model. Page numbers were not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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Algorithm for optimized mRNA design improves stability and immunogenicity
Zhang, H., Zhang, L., Lin, A., Xu, C., Li, Z., Liu, K., Liu, B., Ma, X., Zhao, F., Jiang, H., et al. (2023) · Nature · doi:10.1038/s41586-023-06127-z · Cited by 498 · see citing works
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- Authors
- Rivas, E.
- Affiliations
- Howard Hughes Medical Institute (US)
- Corresponding author
- Rivas, Elena
- ORCID
- Rivas, Elena
- Venue
- RNA Biology
- Publisher
- Informa UK Limited
- Published
- 2013-07
- Volume
- 10
- Issue
- 7
- Pages
- 1185-1196
- ISSN
- 1547-6286 (ISSN-L)
- 1555-8584
- PMC
- PMC3849167
- OpenAlex
- W2020546070
- Cited by
- 57
- References
- 100
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 4 · 2023: 9 · 2022: 8 · 2021: 3 · 2020: 3 · 2019: 5 · 2018: 2 · 2017: 1 · 2016: 4 · 2015: 4 · 2014: 3 · 2013: 1
- FWCI
- 1.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- RNA Biology (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- RNA secondary structure prediction, single-sequence prediction, model architecture, scoring system, probabilistic scoring, folding algorithm, performance ceiling, foldability, loop length distribution
- MeSH terms
- Algorithms, Nucleic Acid Conformation, RNA, Software, Computational Biology, RNA Folding
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