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

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 expe...

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Autores principales: Liu, Jia-Jun, Yu, Chin-Sheng, Wu, Hsiao-Wei, Chang, Yu-Jen, Lin, Chih-Peng, Lu, Chih-Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8245468/
https://www.ncbi.nlm.nih.gov/pubmed/34193921
http://dx.doi.org/10.1038/s41598-021-92793-w
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author Liu, Jia-Jun
Yu, Chin-Sheng
Wu, Hsiao-Wei
Chang, Yu-Jen
Lin, Chih-Peng
Lu, Chih-Hao
author_facet Liu, Jia-Jun
Yu, Chin-Sheng
Wu, Hsiao-Wei
Chang, Yu-Jen
Lin, Chih-Peng
Lu, Chih-Hao
author_sort Liu, Jia-Jun
collection PubMed
description 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.
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spelling pubmed-82454682021-07-06 The structure-based cancer-related single amino acid variation prediction Liu, Jia-Jun Yu, Chin-Sheng Wu, Hsiao-Wei Chang, Yu-Jen Lin, Chih-Peng Lu, Chih-Hao Sci Rep Article 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. Nature Publishing Group UK 2021-06-30 /pmc/articles/PMC8245468/ /pubmed/34193921 http://dx.doi.org/10.1038/s41598-021-92793-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liu, Jia-Jun
Yu, Chin-Sheng
Wu, Hsiao-Wei
Chang, Yu-Jen
Lin, Chih-Peng
Lu, Chih-Hao
The structure-based cancer-related single amino acid variation prediction
title 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_short The structure-based cancer-related single amino acid variation prediction
title_sort structure-based cancer-related single amino acid variation prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8245468/
https://www.ncbi.nlm.nih.gov/pubmed/34193921
http://dx.doi.org/10.1038/s41598-021-92793-w
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