Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.

Oligonucleotide-based aptamers, which have a three-dimensional structure with a single-stranded fragment, feature various characteristics with respect to size, toxicity, and permeability. Accordingly, aptamers are advantageous in terms of diagnosis and treatment and are materials that can be produce...

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Autores principales:	Gwangho Lee, Gun Hyuk Jang, Ho Young Kang, Giltae Song
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/51ef32496859423ea43c1f84b68ec2eb
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id	oai:doaj.org-article:51ef32496859423ea43c1f84b68ec2eb
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spelling	oai:doaj.org-article:51ef32496859423ea43c1f84b68ec2eb2021-12-02T20:10:01ZPredicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.1932-620310.1371/journal.pone.0253760https://doaj.org/article/51ef32496859423ea43c1f84b68ec2eb2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253760https://doaj.org/toc/1932-6203Oligonucleotide-based aptamers, which have a three-dimensional structure with a single-stranded fragment, feature various characteristics with respect to size, toxicity, and permeability. Accordingly, aptamers are advantageous in terms of diagnosis and treatment and are materials that can be produced through relatively simple experiments. Systematic evolution of ligands by exponential enrichment (SELEX) is one of the most widely used experimental methods for generating aptamers; however, it is highly expensive and time-consuming. To reduce the related costs, recent studies have used in silico approaches, such as aptamer-protein interaction (API) classifiers that use sequence patterns to determine the binding affinity between RNA aptamers and proteins. Some of these methods generate candidate RNA aptamer sequences that bind to a target protein, but they are limited to producing candidates of a specific size. In this study, we present a machine learning approach for selecting candidate sequences of various sizes that have a high binding affinity for a specific sequence of a target protein. We applied the Monte Carlo tree search (MCTS) algorithm for generating the candidate sequences using a score function based on an API classifier. The tree structure that we designed with MCTS enables nucleotide sequence sampling, and the obtained sequences are potential aptamer candidates. We performed a quality assessment using the scores of docking simulations. Our validation datasets revealed that our model showed similar or better docking scores in ZDOCK docking simulations than the known aptamers. We expect that our method, which is size-independent and easy to use, can provide insights into searching for an appropriate aptamer sequence for a target protein during the simulation step of SELEX.Gwangho LeeGun Hyuk JangHo Young KangGiltae SongPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253760 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gwangho Lee Gun Hyuk Jang Ho Young Kang Giltae Song Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.
description	Oligonucleotide-based aptamers, which have a three-dimensional structure with a single-stranded fragment, feature various characteristics with respect to size, toxicity, and permeability. Accordingly, aptamers are advantageous in terms of diagnosis and treatment and are materials that can be produced through relatively simple experiments. Systematic evolution of ligands by exponential enrichment (SELEX) is one of the most widely used experimental methods for generating aptamers; however, it is highly expensive and time-consuming. To reduce the related costs, recent studies have used in silico approaches, such as aptamer-protein interaction (API) classifiers that use sequence patterns to determine the binding affinity between RNA aptamers and proteins. Some of these methods generate candidate RNA aptamer sequences that bind to a target protein, but they are limited to producing candidates of a specific size. In this study, we present a machine learning approach for selecting candidate sequences of various sizes that have a high binding affinity for a specific sequence of a target protein. We applied the Monte Carlo tree search (MCTS) algorithm for generating the candidate sequences using a score function based on an API classifier. The tree structure that we designed with MCTS enables nucleotide sequence sampling, and the obtained sequences are potential aptamer candidates. We performed a quality assessment using the scores of docking simulations. Our validation datasets revealed that our model showed similar or better docking scores in ZDOCK docking simulations than the known aptamers. We expect that our method, which is size-independent and easy to use, can provide insights into searching for an appropriate aptamer sequence for a target protein during the simulation step of SELEX.
format	article
author	Gwangho Lee Gun Hyuk Jang Ho Young Kang Giltae Song
author_facet	Gwangho Lee Gun Hyuk Jang Ho Young Kang Giltae Song
author_sort	Gwangho Lee
title	Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.
title_short	Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.
title_full	Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.
title_fullStr	Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.
title_full_unstemmed	Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.
title_sort	predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a monte carlo tree search approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/51ef32496859423ea43c1f84b68ec2eb
work_keys_str_mv	AT gwangholee predictingaptamersequencesthatinteractwithtargetproteinsusinganaptamerproteininteractionclassifierandamontecarlotreesearchapproach AT gunhyukjang predictingaptamersequencesthatinteractwithtargetproteinsusinganaptamerproteininteractionclassifierandamontecarlotreesearchapproach AT hoyoungkang predictingaptamersequencesthatinteractwithtargetproteinsusinganaptamerproteininteractionclassifierandamontecarlotreesearchapproach AT giltaesong predictingaptamersequencesthatinteractwithtargetproteinsusinganaptamerproteininteractionclassifierandamontecarlotreesearchapproach
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Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.

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