Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins
Protein malonylation, a reversible post-translational modification of lysine residues, is associated with various biological functions, such as cellular regulation and pathogenesis. In proteomics, to improve our understanding of the mechanisms of malonylation at the molecular level, the identificati...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324624/ https://www.ncbi.nlm.nih.gov/pubmed/32601280 http://dx.doi.org/10.1038/s41598-020-67384-w |
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author | Chung, Chia-Ru Chang, Ya-Ping Hsu, Yu-Lin Chen, Siyu Wu, Li-Ching Horng, Jorng-Tzong Lee, Tzong-Yi |
author_facet | Chung, Chia-Ru Chang, Ya-Ping Hsu, Yu-Lin Chen, Siyu Wu, Li-Ching Horng, Jorng-Tzong Lee, Tzong-Yi |
author_sort | Chung, Chia-Ru |
collection | PubMed |
description | Protein malonylation, a reversible post-translational modification of lysine residues, is associated with various biological functions, such as cellular regulation and pathogenesis. In proteomics, to improve our understanding of the mechanisms of malonylation at the molecular level, the identification of malonylation sites via an efficient methodology is essential. However, experimental identification of malonylated substrates via mass spectrometry is time-consuming, labor-intensive, and expensive. Although numerous methods have been developed to predict malonylation sites in mammalian proteins, the computational resource for identifying plant malonylation sites is very limited. In this study, a hybrid model incorporating multiple convolutional neural networks (CNNs) with physicochemical properties, evolutionary information, and sequenced-based features was developed for identifying protein malonylation sites in mammals. For plant malonylation, multiple CNNs and random forests were integrated into a secondary modeling phase using a support vector machine. The independent testing has demonstrated that the mammalian and plant malonylation models can yield the area under the receiver operating characteristic curves (AUC) at 0.943 and 0.772, respectively. The proposed scheme has been implemented as a web-based tool, Kmalo (https://fdblab.csie.ncu.edu.tw/kmalo/home.html), which can help facilitate the functional investigation of protein malonylation on mammals and plants. |
format | Online Article Text |
id | pubmed-7324624 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73246242020-07-01 Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins Chung, Chia-Ru Chang, Ya-Ping Hsu, Yu-Lin Chen, Siyu Wu, Li-Ching Horng, Jorng-Tzong Lee, Tzong-Yi Sci Rep Article Protein malonylation, a reversible post-translational modification of lysine residues, is associated with various biological functions, such as cellular regulation and pathogenesis. In proteomics, to improve our understanding of the mechanisms of malonylation at the molecular level, the identification of malonylation sites via an efficient methodology is essential. However, experimental identification of malonylated substrates via mass spectrometry is time-consuming, labor-intensive, and expensive. Although numerous methods have been developed to predict malonylation sites in mammalian proteins, the computational resource for identifying plant malonylation sites is very limited. In this study, a hybrid model incorporating multiple convolutional neural networks (CNNs) with physicochemical properties, evolutionary information, and sequenced-based features was developed for identifying protein malonylation sites in mammals. For plant malonylation, multiple CNNs and random forests were integrated into a secondary modeling phase using a support vector machine. The independent testing has demonstrated that the mammalian and plant malonylation models can yield the area under the receiver operating characteristic curves (AUC) at 0.943 and 0.772, respectively. The proposed scheme has been implemented as a web-based tool, Kmalo (https://fdblab.csie.ncu.edu.tw/kmalo/home.html), which can help facilitate the functional investigation of protein malonylation on mammals and plants. Nature Publishing Group UK 2020-06-29 /pmc/articles/PMC7324624/ /pubmed/32601280 http://dx.doi.org/10.1038/s41598-020-67384-w Text en © The Author(s) 2020 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 license, and indicate if changes were made. The images or other third party material in this article are included in the articleΓÇÖs Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the articleΓÇÖs Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Chung, Chia-Ru Chang, Ya-Ping Hsu, Yu-Lin Chen, Siyu Wu, Li-Ching Horng, Jorng-Tzong Lee, Tzong-Yi Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins |
title | Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins |
title_full | Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins |
title_fullStr | Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins |
title_full_unstemmed | Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins |
title_short | Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins |
title_sort | incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324624/ https://www.ncbi.nlm.nih.gov/pubmed/32601280 http://dx.doi.org/10.1038/s41598-020-67384-w |
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