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Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space
GPCR proteins belong to diverse families of proteins that are defined at multiple hierarchical levels. Inspecting relationships between GPCR proteins on the hierarchical structure is important, since characteristics of the protein can be inferred from proteins in similar hierarchical information. Ho...
Main Authors: | , , , |
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Format: | Online Article Text |
Language: | English |
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Nature Publishing Group UK
2021
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Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8100104/ https://www.ncbi.nlm.nih.gov/pubmed/33953216 http://dx.doi.org/10.1038/s41598-021-88623-8 |
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author | Lee, Taeheon Lee, Sangseon Kang, Minji Kim, Sun |
author_facet | Lee, Taeheon Lee, Sangseon Kang, Minji Kim, Sun |
author_sort | Lee, Taeheon |
collection | PubMed |
description | GPCR proteins belong to diverse families of proteins that are defined at multiple hierarchical levels. Inspecting relationships between GPCR proteins on the hierarchical structure is important, since characteristics of the protein can be inferred from proteins in similar hierarchical information. However, modeling of GPCR families has been performed separately for each of the family, subfamily, and sub-subfamily level. Relationships between GPCR proteins are ignored in these approaches as they process the information in the proteins with several disconnected models. In this study, we propose DeepHier, a deep learning model to simultaneously learn representations of GPCR family hierarchy from the protein sequences with a unified single model. Novel loss term based on metric learning is introduced to incorporate hierarchical relations between proteins. We tested our approach using a public GPCR sequence dataset. Metric distances in the deep feature space corresponded to the hierarchical family relation between GPCR proteins. Furthermore, we demonstrated that further downstream tasks, like phylogenetic reconstruction and motif discovery, are feasible in the constructed embedding space. These results show that hierarchical relations between sequences were successfully captured in both of technical and biological aspects. |
format | Online Article Text |
id | pubmed-8100104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81001042021-05-07 Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space Lee, Taeheon Lee, Sangseon Kang, Minji Kim, Sun Sci Rep Article GPCR proteins belong to diverse families of proteins that are defined at multiple hierarchical levels. Inspecting relationships between GPCR proteins on the hierarchical structure is important, since characteristics of the protein can be inferred from proteins in similar hierarchical information. However, modeling of GPCR families has been performed separately for each of the family, subfamily, and sub-subfamily level. Relationships between GPCR proteins are ignored in these approaches as they process the information in the proteins with several disconnected models. In this study, we propose DeepHier, a deep learning model to simultaneously learn representations of GPCR family hierarchy from the protein sequences with a unified single model. Novel loss term based on metric learning is introduced to incorporate hierarchical relations between proteins. We tested our approach using a public GPCR sequence dataset. Metric distances in the deep feature space corresponded to the hierarchical family relation between GPCR proteins. Furthermore, we demonstrated that further downstream tasks, like phylogenetic reconstruction and motif discovery, are feasible in the constructed embedding space. These results show that hierarchical relations between sequences were successfully captured in both of technical and biological aspects. Nature Publishing Group UK 2021-05-05 /pmc/articles/PMC8100104/ /pubmed/33953216 http://dx.doi.org/10.1038/s41598-021-88623-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Lee, Taeheon Lee, Sangseon Kang, Minji Kim, Sun Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space |
title | Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space |
title_full | Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space |
title_fullStr | Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space |
title_full_unstemmed | Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space |
title_short | Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space |
title_sort | deep hierarchical embedding for simultaneous modeling of gpcr proteins in a unified metric space |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8100104/ https://www.ncbi.nlm.nih.gov/pubmed/33953216 http://dx.doi.org/10.1038/s41598-021-88623-8 |
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