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MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization

MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number...

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Detalles Bibliográficos
Autores principales: Wang, Duolin, Liu, Dongpeng, Yuchi, Jiakang, He, Fei, Jiang, Yuexu, Cai, Siteng, Li, Jingyi, Xu, Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319475/
https://www.ncbi.nlm.nih.gov/pubmed/32324217
http://dx.doi.org/10.1093/nar/gkaa275
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author Wang, Duolin
Liu, Dongpeng
Yuchi, Jiakang
He, Fei
Jiang, Yuexu
Cai, Siteng
Li, Jingyi
Xu, Dong
author_facet Wang, Duolin
Liu, Dongpeng
Yuchi, Jiakang
He, Fei
Jiang, Yuexu
Cai, Siteng
Li, Jingyi
Xu, Dong
author_sort Wang, Duolin
collection PubMed
description MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download. This database will be updated every three months. The MusiteDeep server is available at https://www.musite.net. The stand-alone tools for locally using MusiteDeep are available at https://github.com/duolinwang/MusiteDeep_web.
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spelling pubmed-73194752020-07-01 MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization Wang, Duolin Liu, Dongpeng Yuchi, Jiakang He, Fei Jiang, Yuexu Cai, Siteng Li, Jingyi Xu, Dong Nucleic Acids Res Web Server Issue MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download. This database will be updated every three months. The MusiteDeep server is available at https://www.musite.net. The stand-alone tools for locally using MusiteDeep are available at https://github.com/duolinwang/MusiteDeep_web. Oxford University Press 2020-07-02 2020-04-23 /pmc/articles/PMC7319475/ /pubmed/32324217 http://dx.doi.org/10.1093/nar/gkaa275 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Web Server Issue
Wang, Duolin
Liu, Dongpeng
Yuchi, Jiakang
He, Fei
Jiang, Yuexu
Cai, Siteng
Li, Jingyi
Xu, Dong
MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization
title MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization
title_full MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization
title_fullStr MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization
title_full_unstemmed MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization
title_short MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization
title_sort musitedeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319475/
https://www.ncbi.nlm.nih.gov/pubmed/32324217
http://dx.doi.org/10.1093/nar/gkaa275
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