Determining human-coronavirus protein-protein interaction using machine intelligence

Arijit Chakraborty; Sajal Mitra; Mainak Bhattacharjee; Debashis De; Anindya J Pal

doi:10.1016/j.medntd.2023.100228

Determining human-coronavirus protein-protein interaction using machine intelligence

Med Nov Technol Devices. 2023 Jun:18:100228. doi: 10.1016/j.medntd.2023.100228. Epub 2023 Apr 6.

Authors

Arijit Chakraborty¹, Sajal Mitra², Mainak Bhattacharjee³, Debashis De⁴, Anindya J Pal⁵

Affiliations

¹ Bachelor of Computer Application Department, The Heritage Academy, Kolkata, India.
² Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India.
³ Department of Biotechnology, Heritage Institute of Technology, Kolkata, India.
⁴ Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India.
⁵ University of Burdwan, Burdwan, India.

Abstract

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.

Keywords: Coronavirus; Ensemble learning; Machine intelligence; Protein-protein interaction.