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PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms
Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influenc...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9556876/ https://www.ncbi.nlm.nih.gov/pubmed/36246645 http://dx.doi.org/10.3389/fgene.2022.969915 |
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author | Sengupta, Kaustav Saha, Sovan Halder, Anup Kumar Chatterjee, Piyali Nasipuri, Mita Basu, Subhadip Plewczynski, Dariusz |
author_facet | Sengupta, Kaustav Saha, Sovan Halder, Anup Kumar Chatterjee, Piyali Nasipuri, Mita Basu, Subhadip Plewczynski, Dariusz |
author_sort | Sengupta, Kaustav |
collection | PubMed |
description | Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influence than the one derived from a single source. Considering this fact, a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain, and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term. Based on this ranking, GO terms are propagated to the target proteins. While Protein sequence enriches the sequence-based information, Protein Domain and Protein-Protein Interaction Networks embed structural/functional and topological based information, respectively, during the phase of GO ranking. Performance analysis of PFP-GO is also based on Precision, Recall, and F-Score. The same was found to perform reasonably better when compared to the other existing state-of-art. PFP-GO has achieved an overall Precision, Recall, and F-Score of 0.67, 0.58, and 0.62, respectively. Furthermore, we check some of the top-ranked GO terms predicted by PFP-GO through multilayer network propagation that affect the 3D structure of the genome. The complete source code of PFP-GO is freely available at https://sites.google.com/view/pfp-go/. |
format | Online Article Text |
id | pubmed-9556876 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95568762022-10-14 PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms Sengupta, Kaustav Saha, Sovan Halder, Anup Kumar Chatterjee, Piyali Nasipuri, Mita Basu, Subhadip Plewczynski, Dariusz Front Genet Genetics Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influence than the one derived from a single source. Considering this fact, a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain, and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term. Based on this ranking, GO terms are propagated to the target proteins. While Protein sequence enriches the sequence-based information, Protein Domain and Protein-Protein Interaction Networks embed structural/functional and topological based information, respectively, during the phase of GO ranking. Performance analysis of PFP-GO is also based on Precision, Recall, and F-Score. The same was found to perform reasonably better when compared to the other existing state-of-art. PFP-GO has achieved an overall Precision, Recall, and F-Score of 0.67, 0.58, and 0.62, respectively. Furthermore, we check some of the top-ranked GO terms predicted by PFP-GO through multilayer network propagation that affect the 3D structure of the genome. The complete source code of PFP-GO is freely available at https://sites.google.com/view/pfp-go/. Frontiers Media S.A. 2022-09-29 /pmc/articles/PMC9556876/ /pubmed/36246645 http://dx.doi.org/10.3389/fgene.2022.969915 Text en Copyright © 2022 Sengupta, Saha, Halder, Chatterjee, Nasipuri, Basu and Plewczynski. 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 | Genetics Sengupta, Kaustav Saha, Sovan Halder, Anup Kumar Chatterjee, Piyali Nasipuri, Mita Basu, Subhadip Plewczynski, Dariusz PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms |
title | PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms |
title_full | PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms |
title_fullStr | PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms |
title_full_unstemmed | PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms |
title_short | PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms |
title_sort | pfp-go: integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked go terms |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9556876/ https://www.ncbi.nlm.nih.gov/pubmed/36246645 http://dx.doi.org/10.3389/fgene.2022.969915 |
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