A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites

Abstract Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas re...

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Autores principales:	Niraj Thapa, Meenal Chaudhari, Anthony A. Iannetta, Clarence White, Kaushik Roy, Robert H. Newman, Leslie M. Hicks, Dukka B. KC
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/763a7ff34700418ebf40a11c4465bd6e
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spelling	oai:doaj.org-article:763a7ff34700418ebf40a11c4465bd6e2021-12-02T17:23:49ZA deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites10.1038/s41598-021-91840-w2045-2322https://doaj.org/article/763a7ff34700418ebf40a11c4465bd6e2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91840-whttps://doaj.org/toc/2045-2322Abstract Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii, a model algal phototroph. An ensemble model combining convolutional neural networks and long short-term memory (LSTM) achieves the best performance in predicting phosphorylation sites in C. reinhardtii. Deemed Chlamy-EnPhosSite, the measured best AUC and MCC are 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing higher than those measures for other predictors. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC–MS/MS) in a blinded study and approximately 89.69% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 76.83% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga.Niraj ThapaMeenal ChaudhariAnthony A. IannettaClarence WhiteKaushik RoyRobert H. NewmanLeslie M. HicksDukka B. KCNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Niraj Thapa Meenal Chaudhari Anthony A. Iannetta Clarence White Kaushik Roy Robert H. Newman Leslie M. Hicks Dukka B. KC A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites
description	Abstract Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii, a model algal phototroph. An ensemble model combining convolutional neural networks and long short-term memory (LSTM) achieves the best performance in predicting phosphorylation sites in C. reinhardtii. Deemed Chlamy-EnPhosSite, the measured best AUC and MCC are 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing higher than those measures for other predictors. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC–MS/MS) in a blinded study and approximately 89.69% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 76.83% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga.
format	article
author	Niraj Thapa Meenal Chaudhari Anthony A. Iannetta Clarence White Kaushik Roy Robert H. Newman Leslie M. Hicks Dukka B. KC
author_facet	Niraj Thapa Meenal Chaudhari Anthony A. Iannetta Clarence White Kaushik Roy Robert H. Newman Leslie M. Hicks Dukka B. KC
author_sort	Niraj Thapa
title	A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites
title_short	A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites
title_full	A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites
title_fullStr	A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites
title_full_unstemmed	A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites
title_sort	deep learning based approach for prediction of chlamydomonas reinhardtii phosphorylation sites
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/763a7ff34700418ebf40a11c4465bd6e
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A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites

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