Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components

Abstract Presynaptic and postsynaptic neurotoxins are two groups of neurotoxins. Identification of presynaptic and postsynaptic neurotoxins is an important work for numerous newly found toxins. It is both costly and time consuming to determine these two neurotoxins by experimental methods. As a comp...

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Autores principales:	Haiyan Huo, Tao Li, Shiyuan Wang, Yingli Lv, Yongchun Zuo, Lei Yang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/304afac711ff41e18b670323a322fb11
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spelling	oai:doaj.org-article:304afac711ff41e18b670323a322fb112021-12-02T15:05:30ZPrediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components10.1038/s41598-017-06195-y2045-2322https://doaj.org/article/304afac711ff41e18b670323a322fb112017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-06195-yhttps://doaj.org/toc/2045-2322Abstract Presynaptic and postsynaptic neurotoxins are two groups of neurotoxins. Identification of presynaptic and postsynaptic neurotoxins is an important work for numerous newly found toxins. It is both costly and time consuming to determine these two neurotoxins by experimental methods. As a complement, using computational methods for predicting presynaptic and postsynaptic neurotoxins could provide some useful information in a timely manner. In this study, we described four algorithms for predicting presynaptic and postsynaptic neurotoxins from sequence driven features by using Increment of Diversity (ID), Multinomial Naive Bayes Classifier (MNBC), Random Forest (RF), and K-nearest Neighbours Classifier (IBK). Each protein sequence was encoded by pseudo amino acid (PseAA) compositions and three biological motif features, including MEME, Prosite and InterPro motif features. The Maximum Relevance Minimum Redundancy (MRMR) feature selection method was used to rank the PseAA compositions and the 50 top ranked features were selected to improve the prediction accuracy. The PseAA compositions and three kinds of biological motif features were combined and 12 different parameters that defined as P1-P12 were selected as the input parameters of ID, MNBC, RF, and IBK. The prediction results obtained in this study were significantly better than those of previously developed methods.Haiyan HuoTao LiShiyuan WangYingli LvYongchun ZuoLei YangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Haiyan Huo Tao Li Shiyuan Wang Yingli Lv Yongchun Zuo Lei Yang Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components
description	Abstract Presynaptic and postsynaptic neurotoxins are two groups of neurotoxins. Identification of presynaptic and postsynaptic neurotoxins is an important work for numerous newly found toxins. It is both costly and time consuming to determine these two neurotoxins by experimental methods. As a complement, using computational methods for predicting presynaptic and postsynaptic neurotoxins could provide some useful information in a timely manner. In this study, we described four algorithms for predicting presynaptic and postsynaptic neurotoxins from sequence driven features by using Increment of Diversity (ID), Multinomial Naive Bayes Classifier (MNBC), Random Forest (RF), and K-nearest Neighbours Classifier (IBK). Each protein sequence was encoded by pseudo amino acid (PseAA) compositions and three biological motif features, including MEME, Prosite and InterPro motif features. The Maximum Relevance Minimum Redundancy (MRMR) feature selection method was used to rank the PseAA compositions and the 50 top ranked features were selected to improve the prediction accuracy. The PseAA compositions and three kinds of biological motif features were combined and 12 different parameters that defined as P1-P12 were selected as the input parameters of ID, MNBC, RF, and IBK. The prediction results obtained in this study were significantly better than those of previously developed methods.
format	article
author	Haiyan Huo Tao Li Shiyuan Wang Yingli Lv Yongchun Zuo Lei Yang
author_facet	Haiyan Huo Tao Li Shiyuan Wang Yingli Lv Yongchun Zuo Lei Yang
author_sort	Haiyan Huo
title	Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components
title_short	Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components
title_full	Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components
title_fullStr	Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components
title_full_unstemmed	Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components
title_sort	prediction of presynaptic and postsynaptic neurotoxins by combining various chou’s pseudo components
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/304afac711ff41e18b670323a322fb11
work_keys_str_mv	AT haiyanhuo predictionofpresynapticandpostsynapticneurotoxinsbycombiningvariouschouspseudocomponents AT taoli predictionofpresynapticandpostsynapticneurotoxinsbycombiningvariouschouspseudocomponents AT shiyuanwang predictionofpresynapticandpostsynapticneurotoxinsbycombiningvariouschouspseudocomponents AT yinglilv predictionofpresynapticandpostsynapticneurotoxinsbycombiningvariouschouspseudocomponents AT yongchunzuo predictionofpresynapticandpostsynapticneurotoxinsbycombiningvariouschouspseudocomponents AT leiyang predictionofpresynapticandpostsynapticneurotoxinsbycombiningvariouschouspseudocomponents
_version_	1718388819505446912

Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components

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