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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of heuristics Jg. 27; H. 4; S. 695 - 717
Hauptverfasser: Lewis, Mark W., Verma, Amit, Eckdahl, Todd T.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.08.2021
Springer Nature B.V
Schlagworte:
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
ISSN:1381-1231, 1572-9397
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1381-1231
1572-9397
DOI:10.1007/s10732-021-09471-3