Antibody design using LSTM based deep generative model from phage display library for affinity maturation

Abstract Molecular evolution is an important step in the development of therapeutic antibodies. However, the current method of affinity maturation is overly costly and labor-intensive because of the repetitive mutation experiments needed to adequately explore sequence space. Here, we employed a long...

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Autores principales:	Koichiro Saka, Taro Kakuzaki, Shoichi Metsugi, Daiki Kashiwagi, Kenji Yoshida, Manabu Wada, Hiroyuki Tsunoda, Reiji Teramoto
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b637d0a8f2934168ad948f2310d11803
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id	oai:doaj.org-article:b637d0a8f2934168ad948f2310d11803
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spelling	oai:doaj.org-article:b637d0a8f2934168ad948f2310d118032021-12-02T13:16:19ZAntibody design using LSTM based deep generative model from phage display library for affinity maturation10.1038/s41598-021-85274-72045-2322https://doaj.org/article/b637d0a8f2934168ad948f2310d118032021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85274-7https://doaj.org/toc/2045-2322Abstract Molecular evolution is an important step in the development of therapeutic antibodies. However, the current method of affinity maturation is overly costly and labor-intensive because of the repetitive mutation experiments needed to adequately explore sequence space. Here, we employed a long short term memory network (LSTM)—a widely used deep generative model—based sequence generation and prioritization procedure to efficiently discover antibody sequences with higher affinity. We applied our method to the affinity maturation of antibodies against kynurenine, which is a metabolite related to the niacin synthesis pathway. Kynurenine binding sequences were enriched through phage display panning using a kynurenine-binding oriented human synthetic Fab library. We defined binding antibodies using a sequence repertoire from the NGS data to train the LSTM model. We confirmed that likelihood of generated sequences from a trained LSTM correlated well with binding affinity. The affinity of generated sequences are over 1800-fold higher than that of the parental clone. Moreover, compared to frequency based screening using the same dataset, our machine learning approach generated sequences with greater affinity.Koichiro SakaTaro KakuzakiShoichi MetsugiDaiki KashiwagiKenji YoshidaManabu WadaHiroyuki TsunodaReiji TeramotoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Koichiro Saka Taro Kakuzaki Shoichi Metsugi Daiki Kashiwagi Kenji Yoshida Manabu Wada Hiroyuki Tsunoda Reiji Teramoto Antibody design using LSTM based deep generative model from phage display library for affinity maturation
description	Abstract Molecular evolution is an important step in the development of therapeutic antibodies. However, the current method of affinity maturation is overly costly and labor-intensive because of the repetitive mutation experiments needed to adequately explore sequence space. Here, we employed a long short term memory network (LSTM)—a widely used deep generative model—based sequence generation and prioritization procedure to efficiently discover antibody sequences with higher affinity. We applied our method to the affinity maturation of antibodies against kynurenine, which is a metabolite related to the niacin synthesis pathway. Kynurenine binding sequences were enriched through phage display panning using a kynurenine-binding oriented human synthetic Fab library. We defined binding antibodies using a sequence repertoire from the NGS data to train the LSTM model. We confirmed that likelihood of generated sequences from a trained LSTM correlated well with binding affinity. The affinity of generated sequences are over 1800-fold higher than that of the parental clone. Moreover, compared to frequency based screening using the same dataset, our machine learning approach generated sequences with greater affinity.
format	article
author	Koichiro Saka Taro Kakuzaki Shoichi Metsugi Daiki Kashiwagi Kenji Yoshida Manabu Wada Hiroyuki Tsunoda Reiji Teramoto
author_facet	Koichiro Saka Taro Kakuzaki Shoichi Metsugi Daiki Kashiwagi Kenji Yoshida Manabu Wada Hiroyuki Tsunoda Reiji Teramoto
author_sort	Koichiro Saka
title	Antibody design using LSTM based deep generative model from phage display library for affinity maturation
title_short	Antibody design using LSTM based deep generative model from phage display library for affinity maturation
title_full	Antibody design using LSTM based deep generative model from phage display library for affinity maturation
title_fullStr	Antibody design using LSTM based deep generative model from phage display library for affinity maturation
title_full_unstemmed	Antibody design using LSTM based deep generative model from phage display library for affinity maturation
title_sort	antibody design using lstm based deep generative model from phage display library for affinity maturation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b637d0a8f2934168ad948f2310d11803
work_keys_str_mv	AT koichirosaka antibodydesignusinglstmbaseddeepgenerativemodelfromphagedisplaylibraryforaffinitymaturation AT tarokakuzaki antibodydesignusinglstmbaseddeepgenerativemodelfromphagedisplaylibraryforaffinitymaturation AT shoichimetsugi antibodydesignusinglstmbaseddeepgenerativemodelfromphagedisplaylibraryforaffinitymaturation AT daikikashiwagi antibodydesignusinglstmbaseddeepgenerativemodelfromphagedisplaylibraryforaffinitymaturation AT kenjiyoshida antibodydesignusinglstmbaseddeepgenerativemodelfromphagedisplaylibraryforaffinitymaturation AT manabuwada antibodydesignusinglstmbaseddeepgenerativemodelfromphagedisplaylibraryforaffinitymaturation AT hiroyukitsunoda antibodydesignusinglstmbaseddeepgenerativemodelfromphagedisplaylibraryforaffinitymaturation AT reijiteramoto antibodydesignusinglstmbaseddeepgenerativemodelfromphagedisplaylibraryforaffinitymaturation
_version_	1718393352300265472

Antibody design using LSTM based deep generative model from phage display library for affinity maturation

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