ArticleProceedings of the National Academy of Sciences of the United States of America2004
Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 838 papers.
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838 citing papers in PubMed, 1,665 citations in OpenAlex.
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- RNA design: update on computational frameworks and programs for inverse RNA folding.Briefings in bioinformatics · 2026Review
- Factorization machine with quadratic-optimization annealing for RNA inverse folding and evaluation of binary-integer encoding and nucleotide assignment.Scientific reports · 2026Article
- Spark: sparse hierarchical energy minimization for scalable prediction of RNA pseudoknots.Bioinformatics (Oxford, England) · 2026Article
778 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 2 institutions in 1 country.
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Abstract
A dynamic programming algorithm for prediction of RNA secondary structure has been revised to accommodate folding constraints determined by chemical modification and to include free energy increments for coaxial stacking of helices when they are either adjacent or separated by a single mismatch. Furthermore, free energy parameters are revised to account for recent experimental results for terminal mismatches and hairpin, bulge, internal, and multibranch loops. To demonstrate the applicability of this method, in vivo modification was performed on 5S rRNA in both Escherichia coli and Candida albicans with 1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate, dimethyl sulfate, and kethoxal. The percentage of known base pairs in the predicted structure increased from 26.3% to 86.8% for the E. coli sequence by using modification constraints. For C. albicans, the accuracy remained 87.5% both with and without modification data. On average, for these sequences and a set of 14 sequences with known secondary structure and chemical modification data taken from the literature, accuracy improves from 67% to 76%. This enhancement primarily reflects improvement for three sequences that are predicted with <40% accuracy on the basis of energetics alone. For these sequences, inclusion of chemical modification constraints improves the average accuracy from 28% to 78%. For the 11 sequences with <6% pseudoknotted base pairs, structures predicted with constraints from chemical modification contain on average 84% of known canonical base pairs.
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