Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1783716119130931200 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8245468 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT liujiajun thestructurebasedcancerrelatedsingleaminoacidvariationprediction AT yuchinsheng thestructurebasedcancerrelatedsingleaminoacidvariationprediction AT wuhsiaowei thestructurebasedcancerrelatedsingleaminoacidvariationprediction AT changyujen thestructurebasedcancerrelatedsingleaminoacidvariationprediction AT linchihpeng thestructurebasedcancerrelatedsingleaminoacidvariationprediction AT luchihhao thestructurebasedcancerrelatedsingleaminoacidvariationprediction AT liujiajun structurebasedcancerrelatedsingleaminoacidvariationprediction AT yuchinsheng structurebasedcancerrelatedsingleaminoacidvariationprediction AT wuhsiaowei structurebasedcancerrelatedsingleaminoacidvariationprediction AT changyujen structurebasedcancerrelatedsingleaminoacidvariationprediction AT linchihpeng structurebasedcancerrelatedsingleaminoacidvariationprediction AT luchihhao structurebasedcancerrelatedsingleaminoacidvariationprediction |