ArticleNucleic acids research2023
Fitness functions for RNA structure design.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Probabilistic RNA designability via interpretable ensemble approximation and dynamic decomposition.Bioinformatics (Oxford, England) · 2026Article
- Fine-scale structural information substantially improves mRNA therapeutic stability prediction.Molecular therapy. Nucleic acids · 2026Article
- Factorization machine with quadratic-optimization annealing for RNA inverse folding and evaluation of binary-integer encoding and nucleotide assignment.Scientific reports · 2026Article
- RNA Folding Nearest Neighbor Parameters Including the Modification 1-Methyl-Pseudouridine.bioRxiv : the preprint server for biology · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- SamplingDesign: RNA design via continuous optimization with coupled variables and Monte-Carlo sampling.Nature communications · 2026Article
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
- mRNA folding algorithms for structure and codon optimization.Briefings in bioinformatics · 2025Review
- Analysis of natural structures and chemical mapping data reveals local stability compensation in RNA.Nucleic acids research · 2025Article
- Analysis of natural structures and chemical mapping data reveals local stability compensation in RNA.bioRxiv : the preprint server for biology · 2025Article
- gRNAde: Geometric Deep Learning for 3D RNA inverse design.bioRxiv : the preprint server for biology · 2025Article
- DesiRNA: structure-based design of RNA sequences with a replica exchange Monte Carlo approach.Nucleic acids research · 2025Article
- Sequence Design Using RNAstructure.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Monte Carlo Inverse RNA Folding.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Context-dependent structure formation of hairpin motifs in bacteriophage MS2 genomic RNA.Biophysical journal · 2024Article
- Targeting and engineering long non-coding RNAs for cancer therapy.Nature reviews. Genetics · 2024Review
- Differentiable partition function calculation for RNA.Nucleic acids research · 2024Article
- DNA Structure Design Is Improved Using an Artificially Expanded Alphabet of Base Pairs Including Loop and Mismatch Thermodynamic Parameters.ACS synthetic biology · 2023Article
- RNA design via structure-aware multifrontier ensemble optimization.Bioinformatics (Oxford, England) · 2023Article
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3 authors.
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Abstract
An RNA design algorithm takes a target RNA structure and finds a sequence that folds into that structure. This is fundamentally important for engineering therapeutics using RNA. Computational RNA design algorithms are guided by fitness functions, but not much research has been done on the merits of these functions. We survey current RNA design approaches with a particular focus on the fitness functions used. We experimentally compare the most widely used fitness functions in RNA design algorithms on both synthetic and natural sequences. It has been almost 20 years since the last comparison was published, and we find similar results with a major new result: maximizing probability outperforms minimizing ensemble defect. The probability is the likelihood of a structure at equilibrium and the ensemble defect is the weighted average number of incorrect positions in the ensemble. We find that maximizing probability leads to better results on synthetic RNA design puzzles and agrees more often than other fitness functions with natural sequences and structures, which were designed by evolution. Also, we observe that many recently published approaches minimize structure distance to the minimum free energy prediction, which we find to be a poor fitness function.
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