Cargando…

Ion-pumping microbial rhodopsin protein classification by machine learning approach

BACKGROUND: Rhodopsin is a seven-transmembrane protein covalently linked with retinal chromophore that absorbs photons for energy conversion and intracellular signaling in eukaryotes, bacteria, and archaea. Haloarchaeal rhodopsins are Type-I microbial rhodopsin that elicits various light-driven func...

Descripción completa

Detalles Bibliográficos
Autores principales: Selvaraj, Muthu Krishnan, Thakur, Anamika, Kumar, Manoj, Pinnaka, Anil Kumar, Suri, Chander Raman, Siddhardha, Busi, Elumalai, Senthil Prasad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881276/
https://www.ncbi.nlm.nih.gov/pubmed/36707759
http://dx.doi.org/10.1186/s12859-023-05138-x
_version_ 1784879076368973824
author Selvaraj, Muthu Krishnan
Thakur, Anamika
Kumar, Manoj
Pinnaka, Anil Kumar
Suri, Chander Raman
Siddhardha, Busi
Elumalai, Senthil Prasad
author_facet Selvaraj, Muthu Krishnan
Thakur, Anamika
Kumar, Manoj
Pinnaka, Anil Kumar
Suri, Chander Raman
Siddhardha, Busi
Elumalai, Senthil Prasad
author_sort Selvaraj, Muthu Krishnan
collection PubMed
description BACKGROUND: Rhodopsin is a seven-transmembrane protein covalently linked with retinal chromophore that absorbs photons for energy conversion and intracellular signaling in eukaryotes, bacteria, and archaea. Haloarchaeal rhodopsins are Type-I microbial rhodopsin that elicits various light-driven functions like proton pumping, chloride pumping and Phototaxis behaviour. The industrial application of Ion-pumping Haloarchaeal rhodopsins is limited by the lack of full-length rhodopsin sequence-based classifications, which play an important role in Ion-pumping activity. The well-studied Haloarchaeal rhodopsin is a proton-pumping bacteriorhodopsin that shows promising applications in optogenetics, biosensitized solar cells, security ink, data storage, artificial retinal implant and biohydrogen generation. As a result, a low-cost computational approach is required to identify Ion-pumping Haloarchaeal rhodopsin sequences and its subtype. RESULTS: This study uses a support vector machine (SVM) technique to identify these ion-pumping Haloarchaeal rhodopsin proteins. The haloarchaeal ion pumping rhodopsins viz., bacteriorhodopsin, halorhodopsin, xanthorhodopsin, sensoryrhodopsin and marine prokaryotic Ion-pumping rhodopsins like actinorhodopsin, proteorhodopsin have been utilized to develop the methods that accurately identified the ion pumping haloarchaeal and other type I microbial rhodopsins. We achieved overall maximum accuracy of 97.78%, 97.84% and 97.60%, respectively, for amino acid composition, dipeptide composition and hybrid approach on tenfold cross validation using SVM. Predictive models for each class of rhodopsin performed equally well on an independent data set. In addition to this, similar results were achieved using another machine learning technique namely random forest. Simultaneously predictive models performed equally well during five-fold cross validation. Apart from this study, we also tested the own, blank, BLAST dataset and annotated whole-genome rhodopsin sequences of PWS haloarchaeal isolates in the developed methods. The developed web server (https://bioinfo.imtech.res.in/servers/rhodopred) can identify the Ion Pumping Haloarchaeal rhodopsin proteins and their subtypes. We expect this web tool would be useful for rhodopsin researchers. CONCLUSION: The overall performance of the developed method results show that it accurately identifies the Ionpumping Haloarchaeal rhodopsin and their subtypes using known and unknown microbial rhodopsin sequences. We expect that this study would be useful for optogenetics, molecular biologists and rhodopsin researchers. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05138-x.
format Online
Article
Text
id pubmed-9881276
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-98812762023-01-28 Ion-pumping microbial rhodopsin protein classification by machine learning approach Selvaraj, Muthu Krishnan Thakur, Anamika Kumar, Manoj Pinnaka, Anil Kumar Suri, Chander Raman Siddhardha, Busi Elumalai, Senthil Prasad BMC Bioinformatics Research BACKGROUND: Rhodopsin is a seven-transmembrane protein covalently linked with retinal chromophore that absorbs photons for energy conversion and intracellular signaling in eukaryotes, bacteria, and archaea. Haloarchaeal rhodopsins are Type-I microbial rhodopsin that elicits various light-driven functions like proton pumping, chloride pumping and Phototaxis behaviour. The industrial application of Ion-pumping Haloarchaeal rhodopsins is limited by the lack of full-length rhodopsin sequence-based classifications, which play an important role in Ion-pumping activity. The well-studied Haloarchaeal rhodopsin is a proton-pumping bacteriorhodopsin that shows promising applications in optogenetics, biosensitized solar cells, security ink, data storage, artificial retinal implant and biohydrogen generation. As a result, a low-cost computational approach is required to identify Ion-pumping Haloarchaeal rhodopsin sequences and its subtype. RESULTS: This study uses a support vector machine (SVM) technique to identify these ion-pumping Haloarchaeal rhodopsin proteins. The haloarchaeal ion pumping rhodopsins viz., bacteriorhodopsin, halorhodopsin, xanthorhodopsin, sensoryrhodopsin and marine prokaryotic Ion-pumping rhodopsins like actinorhodopsin, proteorhodopsin have been utilized to develop the methods that accurately identified the ion pumping haloarchaeal and other type I microbial rhodopsins. We achieved overall maximum accuracy of 97.78%, 97.84% and 97.60%, respectively, for amino acid composition, dipeptide composition and hybrid approach on tenfold cross validation using SVM. Predictive models for each class of rhodopsin performed equally well on an independent data set. In addition to this, similar results were achieved using another machine learning technique namely random forest. Simultaneously predictive models performed equally well during five-fold cross validation. Apart from this study, we also tested the own, blank, BLAST dataset and annotated whole-genome rhodopsin sequences of PWS haloarchaeal isolates in the developed methods. The developed web server (https://bioinfo.imtech.res.in/servers/rhodopred) can identify the Ion Pumping Haloarchaeal rhodopsin proteins and their subtypes. We expect this web tool would be useful for rhodopsin researchers. CONCLUSION: The overall performance of the developed method results show that it accurately identifies the Ionpumping Haloarchaeal rhodopsin and their subtypes using known and unknown microbial rhodopsin sequences. We expect that this study would be useful for optogenetics, molecular biologists and rhodopsin researchers. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05138-x. BioMed Central 2023-01-27 /pmc/articles/PMC9881276/ /pubmed/36707759 http://dx.doi.org/10.1186/s12859-023-05138-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Selvaraj, Muthu Krishnan
Thakur, Anamika
Kumar, Manoj
Pinnaka, Anil Kumar
Suri, Chander Raman
Siddhardha, Busi
Elumalai, Senthil Prasad
Ion-pumping microbial rhodopsin protein classification by machine learning approach
title Ion-pumping microbial rhodopsin protein classification by machine learning approach
title_full Ion-pumping microbial rhodopsin protein classification by machine learning approach
title_fullStr Ion-pumping microbial rhodopsin protein classification by machine learning approach
title_full_unstemmed Ion-pumping microbial rhodopsin protein classification by machine learning approach
title_short Ion-pumping microbial rhodopsin protein classification by machine learning approach
title_sort ion-pumping microbial rhodopsin protein classification by machine learning approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881276/
https://www.ncbi.nlm.nih.gov/pubmed/36707759
http://dx.doi.org/10.1186/s12859-023-05138-x
work_keys_str_mv AT selvarajmuthukrishnan ionpumpingmicrobialrhodopsinproteinclassificationbymachinelearningapproach
AT thakuranamika ionpumpingmicrobialrhodopsinproteinclassificationbymachinelearningapproach
AT kumarmanoj ionpumpingmicrobialrhodopsinproteinclassificationbymachinelearningapproach
AT pinnakaanilkumar ionpumpingmicrobialrhodopsinproteinclassificationbymachinelearningapproach
AT surichanderraman ionpumpingmicrobialrhodopsinproteinclassificationbymachinelearningapproach
AT siddhardhabusi ionpumpingmicrobialrhodopsinproteinclassificationbymachinelearningapproach
AT elumalaisenthilprasad ionpumpingmicrobialrhodopsinproteinclassificationbymachinelearningapproach