Neural Network-Based Ensemble Learning Model to Identify Antigenic Fragments of SARS-CoV-2

Abstract

The development of epitope-based vaccines (EBVs) necessitates the identification of antigenic fragments (AFs) of the target pathogen known as T-cell epitopes (TCEs). TCEs are recognized by immune system, specifically by T cells, B cells, and antibodies. Traditional wet lab methods for identifying TCEs are often costly, challenging, and time-consuming compared to computational approaches. In this study, we propose a neural network-based ensemble machine learning (ML) model trained on physicochemical properties of SARS-CoV-2 peptides sequences to predict TCE sequences. The performance of the model assessed using test dataset demonstrated an accuracy of >95%, surpassing the results of other ML classifiers that were employed for comparative analysis. Through fivefold cross-validation technique, a mean accuracy of approximately 95% was reported. Additionally, when compared to other existing TCE prediction methods using a blind dataset, the proposed model was found to be more accurate and effective. The predicted epitopes may have a strong probability to act as potential vaccine candidates. Nonetheless, it is imperative to subject these epitopes to further scientific examination both in vivo and in vitro, to confirm their suitability as vaccine candidates.