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LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor
Viral-host protein protein interaction (PPI) analysis is essential to decode the molecular mechanism of viral pathogen and host immunity processes which eventually help to control viral diseases and optimize therapeutics. The state-of-the-art viral-host PPI predictor leverages unsupervised embedding...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9255777/ https://www.ncbi.nlm.nih.gov/pubmed/35789333 http://dx.doi.org/10.1371/journal.pone.0270275 |
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author | Asim, Muhammad Nabeel Ibrahim, Muhammad Ali Malik, Muhammad Imran Dengel, Andreas Ahmed, Sheraz |
author_facet | Asim, Muhammad Nabeel Ibrahim, Muhammad Ali Malik, Muhammad Imran Dengel, Andreas Ahmed, Sheraz |
author_sort | Asim, Muhammad Nabeel |
collection | PubMed |
description | Viral-host protein protein interaction (PPI) analysis is essential to decode the molecular mechanism of viral pathogen and host immunity processes which eventually help to control viral diseases and optimize therapeutics. The state-of-the-art viral-host PPI predictor leverages unsupervised embedding learning technique (doc2vec) to generate statistical representations of viral-host protein sequences and a Random Forest classifier for interaction prediction. However, doc2vec approach generates the statistical representations of viral-host protein sequences by merely modelling the local context of residues which only partially captures residue semantics. The paper in hand proposes a novel technique for generating better statistical representations of viral and host protein sequences based on the infusion of comprehensive local and global contextual information of the residues. While local residue context aware encoding captures semantic relatedness and short range dependencies of residues. Global residue context aware encoding captures comprehensive long-range residues dependencies, positional invariance of residues, and unique residue combination distribution important for interaction prediction. Using concatenated rich statistical representations of viral and host protein sequences, a robust machine learning framework “LGCA-VHPPI” is developed which makes use of a deep forest model to effectively model complex non-linearity of viral-host PPI sequences. An in-depth performance comparison of the proposed LGCA-VHPPI framework with existing diverse sequence encoding schemes based viral-host PPI predictors reveals that LGCA-VHPPI outperforms state-of-the-art predictor by 6%, 2%, and 2% in terms of matthews correlation coefficient over 3 different benchmark viral-host PPI prediction datasets. |
format | Online Article Text |
id | pubmed-9255777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-92557772022-07-06 LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor Asim, Muhammad Nabeel Ibrahim, Muhammad Ali Malik, Muhammad Imran Dengel, Andreas Ahmed, Sheraz PLoS One Research Article Viral-host protein protein interaction (PPI) analysis is essential to decode the molecular mechanism of viral pathogen and host immunity processes which eventually help to control viral diseases and optimize therapeutics. The state-of-the-art viral-host PPI predictor leverages unsupervised embedding learning technique (doc2vec) to generate statistical representations of viral-host protein sequences and a Random Forest classifier for interaction prediction. However, doc2vec approach generates the statistical representations of viral-host protein sequences by merely modelling the local context of residues which only partially captures residue semantics. The paper in hand proposes a novel technique for generating better statistical representations of viral and host protein sequences based on the infusion of comprehensive local and global contextual information of the residues. While local residue context aware encoding captures semantic relatedness and short range dependencies of residues. Global residue context aware encoding captures comprehensive long-range residues dependencies, positional invariance of residues, and unique residue combination distribution important for interaction prediction. Using concatenated rich statistical representations of viral and host protein sequences, a robust machine learning framework “LGCA-VHPPI” is developed which makes use of a deep forest model to effectively model complex non-linearity of viral-host PPI sequences. An in-depth performance comparison of the proposed LGCA-VHPPI framework with existing diverse sequence encoding schemes based viral-host PPI predictors reveals that LGCA-VHPPI outperforms state-of-the-art predictor by 6%, 2%, and 2% in terms of matthews correlation coefficient over 3 different benchmark viral-host PPI prediction datasets. Public Library of Science 2022-07-05 /pmc/articles/PMC9255777/ /pubmed/35789333 http://dx.doi.org/10.1371/journal.pone.0270275 Text en © 2022 Asim et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Asim, Muhammad Nabeel Ibrahim, Muhammad Ali Malik, Muhammad Imran Dengel, Andreas Ahmed, Sheraz LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor |
title | LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor |
title_full | LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor |
title_fullStr | LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor |
title_full_unstemmed | LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor |
title_short | LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor |
title_sort | lgca-vhppi: a local-global residue context aware viral-host protein-protein interaction predictor |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9255777/ https://www.ncbi.nlm.nih.gov/pubmed/35789333 http://dx.doi.org/10.1371/journal.pone.0270275 |
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