AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes.

The structure and activity of enzymes are influenced by pH value of their surroundings. Although many enzymes work well in the pH range from 6 to 8, some specific enzymes have good efficiencies only in acidic (pH<5) or alkaline (pH>9) solution. Studies have demonstrated that the activities of...

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Autores principales:	Hao Lin, Wei Chen, Hui Ding
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6175992bc6a745caa3adcd1e18e7a2ee
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spelling	oai:doaj.org-article:6175992bc6a745caa3adcd1e18e7a2ee2021-11-18T08:51:50ZAcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes.1932-620310.1371/journal.pone.0075726https://doaj.org/article/6175992bc6a745caa3adcd1e18e7a2ee2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24130738/?tool=EBIhttps://doaj.org/toc/1932-6203The structure and activity of enzymes are influenced by pH value of their surroundings. Although many enzymes work well in the pH range from 6 to 8, some specific enzymes have good efficiencies only in acidic (pH<5) or alkaline (pH>9) solution. Studies have demonstrated that the activities of enzymes correlate with their primary sequences. It is crucial to judge enzyme adaptation to acidic or alkaline environment from its amino acid sequence in molecular mechanism clarification and the design of high efficient enzymes. In this study, we developed a sequence-based method to discriminate acidic enzymes from alkaline enzymes. The analysis of variance was used to choose the optimized discriminating features derived from g-gap dipeptide compositions. And support vector machine was utilized to establish the prediction model. In the rigorous jackknife cross-validation, the overall accuracy of 96.7% was achieved. The method can correctly predict 96.3% acidic and 97.1% alkaline enzymes. Through the comparison between the proposed method and previous methods, it is demonstrated that the proposed method is more accurate. On the basis of this proposed method, we have built an online web-server called AcalPred which can be freely accessed from the website (http://lin.uestc.edu.cn/server/AcalPred). We believe that the AcalPred will become a powerful tool to study enzyme adaptation to acidic or alkaline environment.Hao LinWei ChenHui DingPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 10, p e75726 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hao Lin Wei Chen Hui Ding AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes.
description	The structure and activity of enzymes are influenced by pH value of their surroundings. Although many enzymes work well in the pH range from 6 to 8, some specific enzymes have good efficiencies only in acidic (pH<5) or alkaline (pH>9) solution. Studies have demonstrated that the activities of enzymes correlate with their primary sequences. It is crucial to judge enzyme adaptation to acidic or alkaline environment from its amino acid sequence in molecular mechanism clarification and the design of high efficient enzymes. In this study, we developed a sequence-based method to discriminate acidic enzymes from alkaline enzymes. The analysis of variance was used to choose the optimized discriminating features derived from g-gap dipeptide compositions. And support vector machine was utilized to establish the prediction model. In the rigorous jackknife cross-validation, the overall accuracy of 96.7% was achieved. The method can correctly predict 96.3% acidic and 97.1% alkaline enzymes. Through the comparison between the proposed method and previous methods, it is demonstrated that the proposed method is more accurate. On the basis of this proposed method, we have built an online web-server called AcalPred which can be freely accessed from the website (http://lin.uestc.edu.cn/server/AcalPred). We believe that the AcalPred will become a powerful tool to study enzyme adaptation to acidic or alkaline environment.
format	article
author	Hao Lin Wei Chen Hui Ding
author_facet	Hao Lin Wei Chen Hui Ding
author_sort	Hao Lin
title	AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes.
title_short	AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes.
title_full	AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes.
title_fullStr	AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes.
title_full_unstemmed	AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes.
title_sort	acalpred: a sequence-based tool for discriminating between acidic and alkaline enzymes.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/6175992bc6a745caa3adcd1e18e7a2ee
work_keys_str_mv	AT haolin acalpredasequencebasedtoolfordiscriminatingbetweenacidicandalkalineenzymes AT weichen acalpredasequencebasedtoolfordiscriminatingbetweenacidicandalkalineenzymes AT huiding acalpredasequencebasedtoolfordiscriminatingbetweenacidicandalkalineenzymes
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AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes.

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