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Qfold: a new modeling paradigm for the RNA folding problem

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Abstract

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.

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Authors and Affiliations

  1. Craig School of Business, Missouri Western State University, 4525 Downs Dr., Saint Joseph, MO, 64507, USA

    Mark W. Lewis & Amit Verma

  2. Department of Biology, Missouri Western State University, 4525 Downs Dr., Saint Joseph, MO, 64507, USA

    Todd T. Eckdahl

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  1. Mark W. Lewis
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  2. Amit Verma
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  3. Todd T. Eckdahl
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Correspondence to Mark W. Lewis.

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Lewis, M.W., Verma, A. & Eckdahl, T.T. Qfold: a new modeling paradigm for the RNA folding problem. J Heuristics 27, 695–717 (2021). https://doi.org/10.1007/s10732-021-09471-3

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  • DOI: https://doi.org/10.1007/s10732-021-09471-3

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