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Related Experiment Video

Updated: Jan 18, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
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MultiTox: A sequence-based stacked ensemble model for multiclass protein toxin classification.

Harshika Sharma1, Mayank Singh Thakur2, Avinash Barala2

  • 1Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, India; Infosys Centre for Artificial Intelligence, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, India; Center of Excellence in Healthcare, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, India.

International Journal of Biological Macromolecules
|September 9, 2025
PubMed
Summary
This summary is machine-generated.

MultiTox classifies toxin proteins by their mode of action using an ensemble stacking framework. This approach enhances understanding of protein structure, function, and bioactivity for neurotoxins, cytotoxins, hemotoxins, and enterotoxins.

Keywords:
Biological interpretationESM-2 embeddingsEnsemble stackingMachine learningMulticlass toxicityProtein toxin classificationSequence analysis

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Area of Science:

  • Proteomics and Bioinformatics
  • Structural Biology
  • Toxicology

Background:

  • Understanding toxin protein diversity is crucial for deciphering macromolecular behavior and bioactivity.
  • Traditional toxicity prediction methods offer limited insight into distinct toxin modes of action.

Purpose of the Study:

  • To develop an advanced computational framework, MultiTox, for classifying toxin proteins based on their molecular mechanisms.
  • To differentiate between neurotoxins, cytotoxins, hemotoxins, and enterotoxins using sequence data.

Main Methods:

  • Curated a dataset of 24,756 proteins, extracting high-dimensional ESM-2 embeddings.
  • Employed a two-tier ensemble stacking framework integrating LGBM, MLP, ET, KNN, QDA, and XGBoost classifiers.
  • Utilized SHAP for model interpretation and InterProScan for functional annotation.

Main Results:

  • Achieved high overall accuracy (91.07%), F1-score (90.73%), and MCC (91.61%).
  • Demonstrated strong class-wise accuracies, particularly for hemotoxins (98.80%) and enterotoxins (97.02%).
  • Identified class-specific features related to structural motifs, hydrophobicity, solvent accessibility, folding, localization, and host interactions.

Conclusions:

  • MultiTox offers a scalable and interpretable method for toxin protein classification, linking sequence data to functional insights.
  • The framework provides valuable tools for researchers studying protein structure-function relationships and developing targeted interventions.
  • A public web server is available for real-time predictions, facilitating broader scientific application.