Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties

Abstract Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity to normal cells. As the number of experim...

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Autores principales:	Kai-Yao Huang, Yi-Jhan Tseng, Hui-Ju Kao, Chia-Hung Chen, Hsiao-Hsiang Yang, Shun-Long Weng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d3245427733a41edafdd6a24fa23af92
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spelling	oai:doaj.org-article:d3245427733a41edafdd6a24fa23af922021-12-02T16:10:35ZIdentification of subtypes of anticancer peptides based on sequential features and physicochemical properties10.1038/s41598-021-93124-92045-2322https://doaj.org/article/d3245427733a41edafdd6a24fa23af922021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93124-9https://doaj.org/toc/2045-2322Abstract Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity to normal cells. As the number of experimentally verified bioactive peptides has increased significantly, various of in silico approaches are imperative for investigating the characteristics of ACPs. However, the lack of methods for investigating the differences in physicochemical properties of ACPs. In this study, we compared the N- and C-terminal amino acid composition for each peptide, there are three major subtypes of ACPs that are defined based on the distribution of positively charged residues. For the first time, we were motivated to develop a two-step machine learning model for identification of the subtypes of ACPs, which classify the input data into the corresponding group before applying the classifier. Further, to improve the predictive power, the hybrid feature sets were considered for prediction. Evaluation by five-fold cross-validation showed that the two-step model trained with sequence-based features and physicochemical properties was most effective in discriminating between ACPs and non-ACPs. The two-step model trained with the hybrid features performed well, with a sensitivity of 86.75%, a specificity of 85.75%, an accuracy of 86.08%, and a Matthews Correlation Coefficient value of 0.703. Furthermore, the model also consistently provides the effective performance in independent testing set, with sensitivity of 77.6%, specificity of 94.74%, accuracy of 88.99% and the MCC value reached 0.75. Finally, the two-step model has been implemented as a web-based tool, namely iDACP, which is now freely available at http://mer.hc.mmh.org.tw/iDACP/ .Kai-Yao HuangYi-Jhan TsengHui-Ju KaoChia-Hung ChenHsiao-Hsiang YangShun-Long WengNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kai-Yao Huang Yi-Jhan Tseng Hui-Ju Kao Chia-Hung Chen Hsiao-Hsiang Yang Shun-Long Weng Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties
description	Abstract Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity to normal cells. As the number of experimentally verified bioactive peptides has increased significantly, various of in silico approaches are imperative for investigating the characteristics of ACPs. However, the lack of methods for investigating the differences in physicochemical properties of ACPs. In this study, we compared the N- and C-terminal amino acid composition for each peptide, there are three major subtypes of ACPs that are defined based on the distribution of positively charged residues. For the first time, we were motivated to develop a two-step machine learning model for identification of the subtypes of ACPs, which classify the input data into the corresponding group before applying the classifier. Further, to improve the predictive power, the hybrid feature sets were considered for prediction. Evaluation by five-fold cross-validation showed that the two-step model trained with sequence-based features and physicochemical properties was most effective in discriminating between ACPs and non-ACPs. The two-step model trained with the hybrid features performed well, with a sensitivity of 86.75%, a specificity of 85.75%, an accuracy of 86.08%, and a Matthews Correlation Coefficient value of 0.703. Furthermore, the model also consistently provides the effective performance in independent testing set, with sensitivity of 77.6%, specificity of 94.74%, accuracy of 88.99% and the MCC value reached 0.75. Finally, the two-step model has been implemented as a web-based tool, namely iDACP, which is now freely available at http://mer.hc.mmh.org.tw/iDACP/ .
format	article
author	Kai-Yao Huang Yi-Jhan Tseng Hui-Ju Kao Chia-Hung Chen Hsiao-Hsiang Yang Shun-Long Weng
author_facet	Kai-Yao Huang Yi-Jhan Tseng Hui-Ju Kao Chia-Hung Chen Hsiao-Hsiang Yang Shun-Long Weng
author_sort	Kai-Yao Huang
title	Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties
title_short	Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties
title_full	Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties
title_fullStr	Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties
title_full_unstemmed	Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties
title_sort	identification of subtypes of anticancer peptides based on sequential features and physicochemical properties
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d3245427733a41edafdd6a24fa23af92
work_keys_str_mv	AT kaiyaohuang identificationofsubtypesofanticancerpeptidesbasedonsequentialfeaturesandphysicochemicalproperties AT yijhantseng identificationofsubtypesofanticancerpeptidesbasedonsequentialfeaturesandphysicochemicalproperties AT huijukao identificationofsubtypesofanticancerpeptidesbasedonsequentialfeaturesandphysicochemicalproperties AT chiahungchen identificationofsubtypesofanticancerpeptidesbasedonsequentialfeaturesandphysicochemicalproperties AT hsiaohsiangyang identificationofsubtypesofanticancerpeptidesbasedonsequentialfeaturesandphysicochemicalproperties AT shunlongweng identificationofsubtypesofanticancerpeptidesbasedonsequentialfeaturesandphysicochemicalproperties
_version_	1718384439335059456

Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties

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