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Simple proteins and protein complexes contain only amino acids. In contrast, many other proteins, called conjugated proteins, covalently bond with non-protein moieties.
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human

Lopamudra Dey1, Sanjay Chakraborty1, Anirban Mukhopadhyay1

  • 1Department of Computer Science & Engineering, Heritage Institute of Technology, Kolkata, India; Department of Information Technology, Techno Main, Saltlake, Kolkata, India; Department of. Computer Science & Engineering, University of Kalyani, Kalyani, India.

Biomedical Journal
|October 10, 2020
PubMed
Summary

Machine learning models predict SARS-CoV-2 protein-protein interactions (PPIs) to identify potential drug targets. An ensemble model accurately identified 1326 human targets, aiding anti-COVID-19 drug discovery.

Keywords:
COVID-19Classifier ensembleMachine learningProtein–protein interactionSARS-CoV-2Supervised classification

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in virology

Background:

  • COVID-19, caused by SARS-CoV-2, is a global pandemic with significant mortality.
  • Protein-protein interactions (PPIs) are crucial for SARS-CoV-2 infection mechanisms.
  • Identifying virus-human protein interactions is key to understanding infection and developing treatments.

Purpose of the Study:

  • To develop and validate machine learning models for predicting SARS-CoV-2 human protein interactions.
  • To identify novel human protein targets for therapeutic intervention against COVID-19.
  • To explore repurposable drugs for targeting predicted interactions.

Main Methods:

  • Utilized sequence-based features of human proteins (amino acid composition, pseudo amino acid composition, conjoint triad).
  • Developed various classification models, including an ensemble voting classifier (SVM-Radial, SVM-Polynomial, Random Forest).
  • Validated predictions using biological experiments, gene ontology, and KEGG pathway enrichment analysis.

Main Results:

  • The ensemble voting classifier demonstrated superior accuracy, precision, specificity, recall, and F1 score.
  • Successfully predicted 1326 potential human target proteins for SARS-CoV-2.
  • Identified several repurposable drugs targeting the predicted protein-protein interactions.

Conclusions:

  • The study provides a robust method for identifying critical SARS-CoV-2 host targets.
  • Predicted targets and drug candidates can accelerate the development of effective anti-COVID-19 therapies.
  • This research facilitates further investigation into host-pathogen interactions for pandemic preparedness.