Cargando…
A Transfer Learning-Based Approach for Lysine Propionylation Prediction
Lysine propionylation is a newly discovered posttranslational modification (PTM) and plays a key role in the cellular process. Although proteomics techniques was capable of detecting propionylation, large-scale detection was still challenging. To bridge this gap, we presented a transfer learning-bas...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8096918/ https://www.ncbi.nlm.nih.gov/pubmed/33967828 http://dx.doi.org/10.3389/fphys.2021.658633 |
_version_ | 1783688242382503936 |
---|---|
author | Li, Ang Deng, Yingwei Tan, Yan Chen, Min |
author_facet | Li, Ang Deng, Yingwei Tan, Yan Chen, Min |
author_sort | Li, Ang |
collection | PubMed |
description | Lysine propionylation is a newly discovered posttranslational modification (PTM) and plays a key role in the cellular process. Although proteomics techniques was capable of detecting propionylation, large-scale detection was still challenging. To bridge this gap, we presented a transfer learning-based method for computationally predicting propionylation sites. The recurrent neural network-based deep learning model was trained firstly by the malonylation and then fine-tuned by the propionylation. The trained model served as feature extractor where protein sequences as input were translated into numerical vectors. The support vector machine was used as the final classifier. The proposed method reached a matthews correlation coefficient (MCC) of 0.6615 on the 10-fold crossvalidation and 0.3174 on the independent test, outperforming state-of-the-art methods. The enrichment analysis indicated that the propionylation was associated with these GO terms (GO:0016620, GO:0051287, GO:0003735, GO:0006096, and GO:0005737) and with metabolism. We developed a user-friendly online tool for predicting propoinylation sites which is available at http://47.113.117.61/. |
format | Online Article Text |
id | pubmed-8096918 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80969182021-05-06 A Transfer Learning-Based Approach for Lysine Propionylation Prediction Li, Ang Deng, Yingwei Tan, Yan Chen, Min Front Physiol Physiology Lysine propionylation is a newly discovered posttranslational modification (PTM) and plays a key role in the cellular process. Although proteomics techniques was capable of detecting propionylation, large-scale detection was still challenging. To bridge this gap, we presented a transfer learning-based method for computationally predicting propionylation sites. The recurrent neural network-based deep learning model was trained firstly by the malonylation and then fine-tuned by the propionylation. The trained model served as feature extractor where protein sequences as input were translated into numerical vectors. The support vector machine was used as the final classifier. The proposed method reached a matthews correlation coefficient (MCC) of 0.6615 on the 10-fold crossvalidation and 0.3174 on the independent test, outperforming state-of-the-art methods. The enrichment analysis indicated that the propionylation was associated with these GO terms (GO:0016620, GO:0051287, GO:0003735, GO:0006096, and GO:0005737) and with metabolism. We developed a user-friendly online tool for predicting propoinylation sites which is available at http://47.113.117.61/. Frontiers Media S.A. 2021-04-21 /pmc/articles/PMC8096918/ /pubmed/33967828 http://dx.doi.org/10.3389/fphys.2021.658633 Text en Copyright © 2021 Li, Deng, Tan and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Li, Ang Deng, Yingwei Tan, Yan Chen, Min A Transfer Learning-Based Approach for Lysine Propionylation Prediction |
title | A Transfer Learning-Based Approach for Lysine Propionylation Prediction |
title_full | A Transfer Learning-Based Approach for Lysine Propionylation Prediction |
title_fullStr | A Transfer Learning-Based Approach for Lysine Propionylation Prediction |
title_full_unstemmed | A Transfer Learning-Based Approach for Lysine Propionylation Prediction |
title_short | A Transfer Learning-Based Approach for Lysine Propionylation Prediction |
title_sort | transfer learning-based approach for lysine propionylation prediction |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8096918/ https://www.ncbi.nlm.nih.gov/pubmed/33967828 http://dx.doi.org/10.3389/fphys.2021.658633 |
work_keys_str_mv | AT liang atransferlearningbasedapproachforlysinepropionylationprediction AT dengyingwei atransferlearningbasedapproachforlysinepropionylationprediction AT tanyan atransferlearningbasedapproachforlysinepropionylationprediction AT chenmin atransferlearningbasedapproachforlysinepropionylationprediction AT liang transferlearningbasedapproachforlysinepropionylationprediction AT dengyingwei transferlearningbasedapproachforlysinepropionylationprediction AT tanyan transferlearningbasedapproachforlysinepropionylationprediction AT chenmin transferlearningbasedapproachforlysinepropionylationprediction |