Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach

Abstract The light-dependent ion-transport function of microbial rhodopsin has been widely used in optogenetics for optical control of neural activity. In order to increase the variety of rhodopsin proteins having a wide range of absorption wavelengths, the light absorption properties of various wil...

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Autores principales:	Masayuki Karasuyama, Keiichi Inoue, Ryoko Nakamura, Hideki Kandori, Ichiro Takeuchi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Microbial Rhodopsins Color Tuning Absorption Wavelength Data-driven Machine Learning Approach Protein Rhodopsin Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6d5f6523e95c47b9a3267d31656cf7dd
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spelling	oai:doaj.org-article:6d5f6523e95c47b9a3267d31656cf7dd2021-12-02T11:40:46ZUnderstanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach10.1038/s41598-018-33984-w2045-2322https://doaj.org/article/6d5f6523e95c47b9a3267d31656cf7dd2018-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-33984-whttps://doaj.org/toc/2045-2322Abstract The light-dependent ion-transport function of microbial rhodopsin has been widely used in optogenetics for optical control of neural activity. In order to increase the variety of rhodopsin proteins having a wide range of absorption wavelengths, the light absorption properties of various wild-type rhodopsins and their artificially mutated variants were investigated in the literature. Here, we demonstrate that a machine-learning-based (ML-based) data-driven approach is useful for understanding and predicting the light-absorption properties of microbial rhodopsin proteins. We constructed a database of 796 proteins consisting of microbial rhodopsin wildtypes and their variants. We then proposed an ML method that produces a statistical model describing the relationship between amino-acid sequences and absorption wavelengths and demonstrated that the fitted statistical model is useful for understanding colour tuning rules and predicting absorption wavelengths. By applying the ML method to the database, two residues that were not considered in previous studies are newly identified to be important to colour shift.Masayuki KarasuyamaKeiichi InoueRyoko NakamuraHideki KandoriIchiro TakeuchiNature PortfolioarticleMicrobial RhodopsinsColor TuningAbsorption WavelengthData-driven Machine Learning ApproachProtein RhodopsinMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-11 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Microbial Rhodopsins Color Tuning Absorption Wavelength Data-driven Machine Learning Approach Protein Rhodopsin Medicine R Science Q
spellingShingle	Microbial Rhodopsins Color Tuning Absorption Wavelength Data-driven Machine Learning Approach Protein Rhodopsin Medicine R Science Q Masayuki Karasuyama Keiichi Inoue Ryoko Nakamura Hideki Kandori Ichiro Takeuchi Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach
description	Abstract The light-dependent ion-transport function of microbial rhodopsin has been widely used in optogenetics for optical control of neural activity. In order to increase the variety of rhodopsin proteins having a wide range of absorption wavelengths, the light absorption properties of various wild-type rhodopsins and their artificially mutated variants were investigated in the literature. Here, we demonstrate that a machine-learning-based (ML-based) data-driven approach is useful for understanding and predicting the light-absorption properties of microbial rhodopsin proteins. We constructed a database of 796 proteins consisting of microbial rhodopsin wildtypes and their variants. We then proposed an ML method that produces a statistical model describing the relationship between amino-acid sequences and absorption wavelengths and demonstrated that the fitted statistical model is useful for understanding colour tuning rules and predicting absorption wavelengths. By applying the ML method to the database, two residues that were not considered in previous studies are newly identified to be important to colour shift.
format	article
author	Masayuki Karasuyama Keiichi Inoue Ryoko Nakamura Hideki Kandori Ichiro Takeuchi
author_facet	Masayuki Karasuyama Keiichi Inoue Ryoko Nakamura Hideki Kandori Ichiro Takeuchi
author_sort	Masayuki Karasuyama
title	Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach
title_short	Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach
title_full	Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach
title_fullStr	Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach
title_full_unstemmed	Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach
title_sort	understanding colour tuning rules and predicting absorption wavelengths of microbial rhodopsins by data-driven machine-learning approach
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/6d5f6523e95c47b9a3267d31656cf7dd
work_keys_str_mv	AT masayukikarasuyama understandingcolourtuningrulesandpredictingabsorptionwavelengthsofmicrobialrhodopsinsbydatadrivenmachinelearningapproach AT keiichiinoue understandingcolourtuningrulesandpredictingabsorptionwavelengthsofmicrobialrhodopsinsbydatadrivenmachinelearningapproach AT ryokonakamura understandingcolourtuningrulesandpredictingabsorptionwavelengthsofmicrobialrhodopsinsbydatadrivenmachinelearningapproach AT hidekikandori understandingcolourtuningrulesandpredictingabsorptionwavelengthsofmicrobialrhodopsinsbydatadrivenmachinelearningapproach AT ichirotakeuchi understandingcolourtuningrulesandpredictingabsorptionwavelengthsofmicrobialrhodopsinsbydatadrivenmachinelearningapproach
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Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach

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