IFNBoost: An interpretable computational model for identifying IFNγ inducing peptides

Motivation: Interferon-gamma (IFNγ) is a pivotal cytokine that coordinates various aspects of the immune response, notably enhancing T-cell activation, clearing intracellular pathogens, and providing long-term immune protection. Identification of IFNγ-inducing peptides is essential for the advancement of peptide-based vaccines and immunotherapies; however, the experimental determination of these peptides is hampered by the large number of potential peptide candidates present in pathogen proteins. Results: In this study, we present IFNBoost, a machine learning model developed to accurately predict IFNγ-inducing peptides by leveraging existing immunological datasets, including both peptide sequences and associated metadata. IFNBoost demonstrates impressive performance metrics, achieving an accuracy of 0.819, an F1 score of 0.798, and a Matthew's correlation coefficient (MCC) of 0.634. Evaluation against independent datasets demonstrates that IFNBoost surpasses all current models for predicting IFNγ-inducing peptides, highlighting generalizability of the model. Our comprehensive analysis indicates that, in addition to peptide sequences, metadata features such as the source organism and host significantly enhance predictive accuracy. The predictions produced by IFNBoost have the potential to guide rational vaccine design, thereby improving vaccine efficacy via precise identification of peptides that elicit the desired cytokine responses. Availability and implementation: To improve the accessibility and utility of our model, we have developed a web application available at https://ifnboost.streamlit.app/.