Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction

A neurotoxin is essentially a protein that mainly acts on the nervous system; it has a selective toxic effect on the central nervous system and neuromuscular nodes, can cause muscle paralysis and respiratory paralysis, and has strong lethality. According to their principle of action, neurotoxins are...

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Autores principales:	Wen Zhu, Yuxin Guo, Quan Zou
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	neurotoxin monomonokgap logistic regression protein classification neu_lr Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/a98db116164242c6a609d74e8fea54c8
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spelling	oai:doaj.org-article:a98db116164242c6a609d74e8fea54c82021-11-09T05:54:57ZPrediction of presynaptic and postsynaptic neurotoxins based on feature extraction10.3934/mbe.20212971551-0018https://doaj.org/article/a98db116164242c6a609d74e8fea54c82021-06-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021297?viewType=HTMLhttps://doaj.org/toc/1551-0018A neurotoxin is essentially a protein that mainly acts on the nervous system; it has a selective toxic effect on the central nervous system and neuromuscular nodes, can cause muscle paralysis and respiratory paralysis, and has strong lethality. According to their principle of action, neurotoxins are divided into presynaptic neurotoxins and postsynaptic neurotoxins. Correctly identifying presynaptic and postsynaptic nerve toxins provides important clues for future drug development and the discovery of drug targets. Therefore, a predictive model, Neu_LR, was constructed in this paper. The monoMonokGap method was used to extract the frequency characteristics of presynaptic and postsynaptic neurotoxin sequences and carry out feature selection, then, based on the important features obtained after dimensionality reduction, the prediction model Neu_LR was constructed using a logistic regression algorithm, and ten-fold cross-validation and independent test set validation were used. The final accuracy rates were 99.6078 and 94.1176%, respectively, which proved that the Neu_LR model had good predictive performance and robustness, and could meet the prediction requirements of presynaptic and postsynaptic neurotoxins. The data and source code of the model can be freely download from https://github.com/gyx123681/.Wen ZhuYuxin GuoQuan ZouAIMS Pressarticleneurotoxinmonomonokgaplogistic regressionprotein classificationneu_lrBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 5943-5958 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	neurotoxin monomonokgap logistic regression protein classification neu_lr Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	neurotoxin monomonokgap logistic regression protein classification neu_lr Biotechnology TP248.13-248.65 Mathematics QA1-939 Wen Zhu Yuxin Guo Quan Zou Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction
description	A neurotoxin is essentially a protein that mainly acts on the nervous system; it has a selective toxic effect on the central nervous system and neuromuscular nodes, can cause muscle paralysis and respiratory paralysis, and has strong lethality. According to their principle of action, neurotoxins are divided into presynaptic neurotoxins and postsynaptic neurotoxins. Correctly identifying presynaptic and postsynaptic nerve toxins provides important clues for future drug development and the discovery of drug targets. Therefore, a predictive model, Neu_LR, was constructed in this paper. The monoMonokGap method was used to extract the frequency characteristics of presynaptic and postsynaptic neurotoxin sequences and carry out feature selection, then, based on the important features obtained after dimensionality reduction, the prediction model Neu_LR was constructed using a logistic regression algorithm, and ten-fold cross-validation and independent test set validation were used. The final accuracy rates were 99.6078 and 94.1176%, respectively, which proved that the Neu_LR model had good predictive performance and robustness, and could meet the prediction requirements of presynaptic and postsynaptic neurotoxins. The data and source code of the model can be freely download from https://github.com/gyx123681/.
format	article
author	Wen Zhu Yuxin Guo Quan Zou
author_facet	Wen Zhu Yuxin Guo Quan Zou
author_sort	Wen Zhu
title	Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction
title_short	Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction
title_full	Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction
title_fullStr	Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction
title_full_unstemmed	Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction
title_sort	prediction of presynaptic and postsynaptic neurotoxins based on feature extraction
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/a98db116164242c6a609d74e8fea54c8
work_keys_str_mv	AT wenzhu predictionofpresynapticandpostsynapticneurotoxinsbasedonfeatureextraction AT yuxinguo predictionofpresynapticandpostsynapticneurotoxinsbasedonfeatureextraction AT quanzou predictionofpresynapticandpostsynapticneurotoxinsbasedonfeatureextraction
_version_	1718441323102470144

Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction

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