Determining human-coronavirus protein-protein interaction using machine intelligence

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus −19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for v...

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Main Authors: Arijit Chakraborty, Sajal Mitra, Mainak Bhattacharjee, Debashis De, Anindya J. Pal
Format: Article
Language:English
Published: Elsevier 2023-06-01
Series:Medicine in Novel Technology and Devices
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2590093523000231
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author Arijit Chakraborty
Sajal Mitra
Mainak Bhattacharjee
Debashis De
Anindya J. Pal
author_facet Arijit Chakraborty
Sajal Mitra
Mainak Bhattacharjee
Debashis De
Anindya J. Pal
author_sort Arijit Chakraborty
collection DOAJ
description The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus −19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for viral pathogenesis. However, many of these PPIs are poorly understood and unexplored, necessitating a more in-depth investigation to find latent yet critical interactions. This article elucidates the host-viral PPI through Machine Learning (ML) lenses and validates the biological significance of the same using web-based tools. ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins, namely Amino Acid Composition, Pseudo Amino Acid Composition, Conjoint Triad, Dipeptide Composition, and Normalized Auto Correlation. A majority voting rule-based ensemble method composed of the Random Forest Model (RFM), AdaBoost, and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work. The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor ≥70%, validated by utilizing Gene Ontology (GO) and KEGG pathway enrichment analysis. Consequently, this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.
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spelling doaj.art-10a56705dff94f5694f1e8f278c8792e2023-07-01T04:35:35ZengElsevierMedicine in Novel Technology and Devices2590-09352023-06-0118100228Determining human-coronavirus protein-protein interaction using machine intelligenceArijit Chakraborty0Sajal Mitra1Mainak Bhattacharjee2Debashis De3Anindya J. Pal4Bachelor of Computer Application Department, The Heritage Academy, Kolkata, India; Corresponding author.The Heritage Academy, Chowbaga Road, Anandapur, Madurdaha, Kolkata, West Bengal, 700107, India.Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, IndiaDepartment of Biotechnology, Heritage Institute of Technology, Kolkata, IndiaDepartment of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, IndiaUniversity of Burdwan, Burdwan, IndiaThe Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus −19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for viral pathogenesis. However, many of these PPIs are poorly understood and unexplored, necessitating a more in-depth investigation to find latent yet critical interactions. This article elucidates the host-viral PPI through Machine Learning (ML) lenses and validates the biological significance of the same using web-based tools. ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins, namely Amino Acid Composition, Pseudo Amino Acid Composition, Conjoint Triad, Dipeptide Composition, and Normalized Auto Correlation. A majority voting rule-based ensemble method composed of the Random Forest Model (RFM), AdaBoost, and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work. The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor ≥70%, validated by utilizing Gene Ontology (GO) and KEGG pathway enrichment analysis. Consequently, this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.http://www.sciencedirect.com/science/article/pii/S2590093523000231CoronavirusEnsemble learningMachine intelligenceProtein-protein interaction
spellingShingle Arijit Chakraborty
Sajal Mitra
Mainak Bhattacharjee
Debashis De
Anindya J. Pal
Determining human-coronavirus protein-protein interaction using machine intelligence
Medicine in Novel Technology and Devices
Coronavirus
Ensemble learning
Machine intelligence
Protein-protein interaction
title Determining human-coronavirus protein-protein interaction using machine intelligence
title_full Determining human-coronavirus protein-protein interaction using machine intelligence
title_fullStr Determining human-coronavirus protein-protein interaction using machine intelligence
title_full_unstemmed Determining human-coronavirus protein-protein interaction using machine intelligence
title_short Determining human-coronavirus protein-protein interaction using machine intelligence
title_sort determining human coronavirus protein protein interaction using machine intelligence
topic Coronavirus
Ensemble learning
Machine intelligence
Protein-protein interaction
url http://www.sciencedirect.com/science/article/pii/S2590093523000231
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