Qfold: a new modeling paradigm for the RNA folding problem

Ribonucleic acid (RNA) molecules play informational, structural, and metabolic roles in all living cells. RNAs are chains of nucleotides containing bases {A, C, G, U} that interact via base pairings to determine higher order structure and functionality. The RNA folding problem is to predict one or m...

Full description

Saved in:
Bibliographic Details
Published inJournal of heuristics Vol. 27; no. 4; pp. 695 - 717
Main Authors Lewis, Mark W., Verma, Amit, Eckdahl, Todd T.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.08.2021
Springer Nature B.V
Subjects
Accuracy
Algorithms
Artificial Intelligence
Biology
Calculus of Variations and Optimal Control; Optimization
Folding
Free energy
Heuristic
Heuristic methods
Management Science
Mathematics
Mathematics and Statistics
Neural networks
Nucleotides
Operations Research
Operations Research/Decision Theory
Optimization
Ribonucleic acid
RNA
Variables
RNA folding
Structure prediction
Path relinking
QUBO
Online AccessGet full text

Cover

More Information
Summary:Ribonucleic acid (RNA) molecules play informational, structural, and metabolic roles in all living cells. RNAs are chains of nucleotides containing bases {A, C, G, U} that interact via base pairings to determine higher order structure and functionality. The RNA folding problem is to predict one or more secondary RNA structures from a given primary sequence of bases. From a mathematical modeling perspective, solutions to the RNA folding problem come from minimizing the thermodynamic free energy of a structure by selecting which bases will be paired, subject to a set of constraints. Here we report on a Quadratic Unconstrained Binary Optimization (QUBO) modeling paradigm that fits naturally with the parameters and constraints required for RNA folding prediction. Three QUBO models are presented along with a hybrid metaheuristic algorithm. Extensive testing results show a strong positive correlation with benchmark results.
ISSN:1381-1231
1572-9397
DOI:10.1007/s10732-021-09471-3