The structure-based cancer-related single amino acid variation prediction

Abstract Single amino acid variation (SAV) is an amino acid substitution of the protein sequence that can potentially influence the entire protein structure or function, as well as its binding affinity. Protein destabilization is related to diseases, including several cancers, although using traditi...

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Autores principales:	Jia-Jun Liu, Chin-Sheng Yu, Hsiao-Wei Wu, Yu-Jen Chang, Chih-Peng Lin, Chih-Hao Lu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/df5fbbfb0aea4292884ab89b7c901d15
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spelling	oai:doaj.org-article:df5fbbfb0aea4292884ab89b7c901d152021-12-02T16:10:27ZThe structure-based cancer-related single amino acid variation prediction10.1038/s41598-021-92793-w2045-2322https://doaj.org/article/df5fbbfb0aea4292884ab89b7c901d152021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92793-whttps://doaj.org/toc/2045-2322Abstract Single amino acid variation (SAV) is an amino acid substitution of the protein sequence that can potentially influence the entire protein structure or function, as well as its binding affinity. Protein destabilization is related to diseases, including several cancers, although using traditional experiments to clarify the relationship between SAVs and cancer uses much time and resources. Some SAV prediction methods use computational approaches, with most predicting SAV-induced changes in protein stability. In this investigation, all SAV characteristics generated from protein sequences, structures and the microenvironment were converted into feature vectors and fed into an integrated predicting system using a support vector machine and genetic algorithm. Critical features were used to estimate the relationship between their properties and cancers caused by SAVs. We describe how we developed a prediction system based on protein sequences and structure that is capable of distinguishing if the SAV is related to cancer or not. The five-fold cross-validation performance of our system is 89.73% for the accuracy, 0.74 for the Matthews correlation coefficient, and 0.81 for the F1 score. We have built an online prediction server, CanSavPre ( http://bioinfo.cmu.edu.tw/CanSavPre/ ), which is expected to become a useful, practical tool for cancer research and precision medicine.Jia-Jun LiuChin-Sheng YuHsiao-Wei WuYu-Jen ChangChih-Peng LinChih-Hao LuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jia-Jun Liu Chin-Sheng Yu Hsiao-Wei Wu Yu-Jen Chang Chih-Peng Lin Chih-Hao Lu The structure-based cancer-related single amino acid variation prediction
description	Abstract Single amino acid variation (SAV) is an amino acid substitution of the protein sequence that can potentially influence the entire protein structure or function, as well as its binding affinity. Protein destabilization is related to diseases, including several cancers, although using traditional experiments to clarify the relationship between SAVs and cancer uses much time and resources. Some SAV prediction methods use computational approaches, with most predicting SAV-induced changes in protein stability. In this investigation, all SAV characteristics generated from protein sequences, structures and the microenvironment were converted into feature vectors and fed into an integrated predicting system using a support vector machine and genetic algorithm. Critical features were used to estimate the relationship between their properties and cancers caused by SAVs. We describe how we developed a prediction system based on protein sequences and structure that is capable of distinguishing if the SAV is related to cancer or not. The five-fold cross-validation performance of our system is 89.73% for the accuracy, 0.74 for the Matthews correlation coefficient, and 0.81 for the F1 score. We have built an online prediction server, CanSavPre ( http://bioinfo.cmu.edu.tw/CanSavPre/ ), which is expected to become a useful, practical tool for cancer research and precision medicine.
format	article
author	Jia-Jun Liu Chin-Sheng Yu Hsiao-Wei Wu Yu-Jen Chang Chih-Peng Lin Chih-Hao Lu
author_facet	Jia-Jun Liu Chin-Sheng Yu Hsiao-Wei Wu Yu-Jen Chang Chih-Peng Lin Chih-Hao Lu
author_sort	Jia-Jun Liu
title	The structure-based cancer-related single amino acid variation prediction
title_short	The structure-based cancer-related single amino acid variation prediction
title_full	The structure-based cancer-related single amino acid variation prediction
title_fullStr	The structure-based cancer-related single amino acid variation prediction
title_full_unstemmed	The structure-based cancer-related single amino acid variation prediction
title_sort	structure-based cancer-related single amino acid variation prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/df5fbbfb0aea4292884ab89b7c901d15
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The structure-based cancer-related single amino acid variation prediction

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