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IL2Pepscan: A machine learning framework for predicting IL-2 inducing peptides and their identification across global
Pooja Arora1, Rachit Abhigyan2, Neha Periwal3,4
1Department of Zoology, Hansraj College, University of Delhi, Delhi, India. pooja@hrc.du.ac.in.
Scientific Reports
|January 30, 2026
Summary
We developed a computational model to predict Interleukin-2 (IL-2)-inducing peptides, crucial for immunotherapy and vaccine development. Our validated approach identified promising viral candidates, aiding future research.
Area of Science:
- Immunology
- Computational Biology
- Bioinformatics
Background:
- Interleukin-2 (IL-2) is a critical cytokine for T cell activation and immune response regulation.
- Identifying IL-2-inducing peptides is essential for advancing immunotherapy and vaccine design.
Purpose of the Study:
- To develop and validate a computational method for predicting IL-2-inducing peptides.
- To screen a large viral proteome for novel IL-2-inducing peptide candidates.
Main Methods:
- Utilized peptide datasets from the Immune Epitope Database (IEDB).
- Extracted features using pfeature, ifeature, and ProtBERT.
- Developed an Extra Tree-based model using Dipeptide deviation from Expected mean (DDE) features.
- Predicted IL-2-inducing peptides across the global viral RefSeq proteome.
Main Results:
- Achieved 79.88% accuracy, 81.24% sensitivity, and an MCC of 0.6 in external validation.
- Identified numerous potential IL-2-inducing viral peptides from over 155 million screened peptides.
- Validated predictions against existing literature, confirming experimentally verified IL-2-inducing viral proteins.
Conclusions:
- The developed computational model accurately predicts IL-2-inducing peptides.
- The study identified novel viral-encoded IL-2-inducing peptide candidates.
- A user-friendly web server (http://www.soodlab.com/il2pepscan/) is available for peptide analysis.
Keywords:
Bidirectional long short-term memory (Bi-LSTM)Convolutional neural network (CNN)Deep learningExtra trees (ET)IL-2 inducersLarge language modelMachine learningPeptidesProtBERTMore Related Videos
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