Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance

Abstract Identification of post-translational modifications (PTM) is significant in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Though there are several computational tools to identify individual PTMs, only...

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Autores principales:	Sabit Ahmed, Afrida Rahman, Md. Al Mehedi Hasan, Shamim Ahmad, S. M. Shovan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/88a966cf5d1144c39a8b9b5dd5e744b1
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spelling	oai:doaj.org-article:88a966cf5d1144c39a8b9b5dd5e744b12021-12-02T18:14:22ZComputational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance10.1038/s41598-021-98458-y2045-2322https://doaj.org/article/88a966cf5d1144c39a8b9b5dd5e744b12021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98458-yhttps://doaj.org/toc/2045-2322Abstract Identification of post-translational modifications (PTM) is significant in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Though there are several computational tools to identify individual PTMs, only three predictors have been established to predict multiple PTMs at the same lysine residue. Furthermore, detailed analysis and assessment on dataset balancing and the significance of different feature encoding techniques for a suitable multi-PTM prediction model are still lacking. This study introduces a computational method named ’iMul-kSite’ for predicting acetylation, crotonylation, methylation, succinylation, and glutarylation, from an unrecognized peptide sample with one, multiple, or no modifications. After successfully eliminating the redundant data samples from the majority class by analyzing the hardness of the sequence-coupling information, feature representation has been optimized by adopting the combination of ANOVA F-Test and incremental feature selection approach. The proposed predictor predicts multi-label PTM sites with 92.83% accuracy using the top 100 features. It has also achieved a 93.36% aiming rate and 96.23% coverage rate, which are much better than the existing state-of-the-art predictors on the validation test. This performance indicates that ’iMul-kSite’ can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, ’iMul-kSite’ has been deployed as a user-friendly web-server at http://103.99.176.239/iMul-kSite .Sabit AhmedAfrida RahmanMd. Al Mehedi HasanShamim AhmadS. M. ShovanNature 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 Sabit Ahmed Afrida Rahman Md. Al Mehedi Hasan Shamim Ahmad S. M. Shovan Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance
description	Abstract Identification of post-translational modifications (PTM) is significant in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Though there are several computational tools to identify individual PTMs, only three predictors have been established to predict multiple PTMs at the same lysine residue. Furthermore, detailed analysis and assessment on dataset balancing and the significance of different feature encoding techniques for a suitable multi-PTM prediction model are still lacking. This study introduces a computational method named ’iMul-kSite’ for predicting acetylation, crotonylation, methylation, succinylation, and glutarylation, from an unrecognized peptide sample with one, multiple, or no modifications. After successfully eliminating the redundant data samples from the majority class by analyzing the hardness of the sequence-coupling information, feature representation has been optimized by adopting the combination of ANOVA F-Test and incremental feature selection approach. The proposed predictor predicts multi-label PTM sites with 92.83% accuracy using the top 100 features. It has also achieved a 93.36% aiming rate and 96.23% coverage rate, which are much better than the existing state-of-the-art predictors on the validation test. This performance indicates that ’iMul-kSite’ can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, ’iMul-kSite’ has been deployed as a user-friendly web-server at http://103.99.176.239/iMul-kSite .
format	article
author	Sabit Ahmed Afrida Rahman Md. Al Mehedi Hasan Shamim Ahmad S. M. Shovan
author_facet	Sabit Ahmed Afrida Rahman Md. Al Mehedi Hasan Shamim Ahmad S. M. Shovan
author_sort	Sabit Ahmed
title	Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance
title_short	Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance
title_full	Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance
title_fullStr	Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance
title_full_unstemmed	Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance
title_sort	computational identification of multiple lysine ptm sites by analyzing the instance hardness and feature importance
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/88a966cf5d1144c39a8b9b5dd5e744b1
work_keys_str_mv	AT sabitahmed computationalidentificationofmultiplelysineptmsitesbyanalyzingtheinstancehardnessandfeatureimportance AT afridarahman computationalidentificationofmultiplelysineptmsitesbyanalyzingtheinstancehardnessandfeatureimportance AT mdalmehedihasan computationalidentificationofmultiplelysineptmsitesbyanalyzingtheinstancehardnessandfeatureimportance AT shamimahmad computationalidentificationofmultiplelysineptmsitesbyanalyzingtheinstancehardnessandfeatureimportance AT smshovan computationalidentificationofmultiplelysineptmsitesbyanalyzingtheinstancehardnessandfeatureimportance
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Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance

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