mRNA codon optimization with quantum computers.

Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is t...

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Autores principales:	Dillion M Fox, Kim M Branson, Ross C Walker
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/013f6a0582994918a5bcad4764693e16
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spelling	oai:doaj.org-article:013f6a0582994918a5bcad4764693e162021-12-02T20:13:21ZmRNA codon optimization with quantum computers.1932-620310.1371/journal.pone.0259101https://doaj.org/article/013f6a0582994918a5bcad4764693e162021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259101https://doaj.org/toc/1932-6203Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. A Quantum Annealer (QA) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The QA is found to be competitive in identifying optimal solutions. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.Dillion M FoxKim M BransonRoss C WalkerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0259101 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dillion M Fox Kim M Branson Ross C Walker mRNA codon optimization with quantum computers.
description	Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. A Quantum Annealer (QA) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The QA is found to be competitive in identifying optimal solutions. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.
format	article
author	Dillion M Fox Kim M Branson Ross C Walker
author_facet	Dillion M Fox Kim M Branson Ross C Walker
author_sort	Dillion M Fox
title	mRNA codon optimization with quantum computers.
title_short	mRNA codon optimization with quantum computers.
title_full	mRNA codon optimization with quantum computers.
title_fullStr	mRNA codon optimization with quantum computers.
title_full_unstemmed	mRNA codon optimization with quantum computers.
title_sort	mrna codon optimization with quantum computers.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/013f6a0582994918a5bcad4764693e16
work_keys_str_mv	AT dillionmfox mrnacodonoptimizationwithquantumcomputers AT kimmbranson mrnacodonoptimizationwithquantumcomputers AT rosscwalker mrnacodonoptimizationwithquantumcomputers
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mRNA codon optimization with quantum computers.

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