Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning

Abstract We developed a method to improve protein thermostability, “loop-walking method”. Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as a hot-spot loop having an impact on therm...

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Autores principales:	Kazunori Yoshida, Shun Kawai, Masaya Fujitani, Satoshi Koikeda, Ryuji Kato, Tadashi Ema
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dfc2e4cb376e4750879e94b4c7a05a1d
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spelling	oai:doaj.org-article:dfc2e4cb376e4750879e94b4c7a05a1d2021-12-02T18:25:04ZEnhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning10.1038/s41598-021-91339-42045-2322https://doaj.org/article/dfc2e4cb376e4750879e94b4c7a05a1d2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91339-4https://doaj.org/toc/2045-2322Abstract We developed a method to improve protein thermostability, “loop-walking method”. Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as a hot-spot loop having an impact on thermostability, and the P233G/L234E/V235M mutant was found from 214 variants in the L7 library. Although a more excellent mutant might be discovered by screening all the 8000 P233X/L234X/V235X mutants, it was difficult to assay all of them. We therefore employed machine learning. Using thermostability data of the 214 mutants, a computational discrimination model was constructed to predict thermostability potentials. Among 7786 combinations ranked in silico, 20 promising candidates were selected and assayed. The P233D/L234P/V235S mutant retained 66% activity after heat treatment at 60 °C for 30 min, which was higher than those of the wild-type enzyme (5%) and the P233G/L234E/V235M mutant (35%).Kazunori YoshidaShun KawaiMasaya FujitaniSatoshi KoikedaRyuji KatoTadashi EmaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kazunori Yoshida Shun Kawai Masaya Fujitani Satoshi Koikeda Ryuji Kato Tadashi Ema Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning
description	Abstract We developed a method to improve protein thermostability, “loop-walking method”. Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as a hot-spot loop having an impact on thermostability, and the P233G/L234E/V235M mutant was found from 214 variants in the L7 library. Although a more excellent mutant might be discovered by screening all the 8000 P233X/L234X/V235X mutants, it was difficult to assay all of them. We therefore employed machine learning. Using thermostability data of the 214 mutants, a computational discrimination model was constructed to predict thermostability potentials. Among 7786 combinations ranked in silico, 20 promising candidates were selected and assayed. The P233D/L234P/V235S mutant retained 66% activity after heat treatment at 60 °C for 30 min, which was higher than those of the wild-type enzyme (5%) and the P233G/L234E/V235M mutant (35%).
format	article
author	Kazunori Yoshida Shun Kawai Masaya Fujitani Satoshi Koikeda Ryuji Kato Tadashi Ema
author_facet	Kazunori Yoshida Shun Kawai Masaya Fujitani Satoshi Koikeda Ryuji Kato Tadashi Ema
author_sort	Kazunori Yoshida
title	Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning
title_short	Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning
title_full	Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning
title_fullStr	Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning
title_full_unstemmed	Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning
title_sort	enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dfc2e4cb376e4750879e94b4c7a05a1d
work_keys_str_mv	AT kazunoriyoshida enhancementofproteinthermostabilitybythreeconsecutivemutationsusingloopwalkingmethodandmachinelearning AT shunkawai enhancementofproteinthermostabilitybythreeconsecutivemutationsusingloopwalkingmethodandmachinelearning AT masayafujitani enhancementofproteinthermostabilitybythreeconsecutivemutationsusingloopwalkingmethodandmachinelearning AT satoshikoikeda enhancementofproteinthermostabilitybythreeconsecutivemutationsusingloopwalkingmethodandmachinelearning AT ryujikato enhancementofproteinthermostabilitybythreeconsecutivemutationsusingloopwalkingmethodandmachinelearning AT tadashiema enhancementofproteinthermostabilitybythreeconsecutivemutationsusingloopwalkingmethodandmachinelearning
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Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning

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