Ensemble Machine Learning Model to Predict SARS-CoV-2 T-Cell Epitopes as Potential Vaccine Targets

Abstract

An ongoing outbreak of coronavirus disease 2019 (COVID-19), caused by a single-stranded RNA virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide pandemic that continues to date. Vaccination has proven to be the most effective technique, by far, for the treatment of COVID-19 and to combat the outbreak. Among all vaccine types, epitope-based peptide vaccines have received less attention and hold a large untapped potential for boosting vaccine safety and immunogenicity. Peptides used in such vaccine technology are chemically synthesized based on the amino acid sequences of antigenic proteins (T-cell epitopes) of the target pathogen. Using wet-lab experiments to identify antigenic proteins is very difficult, expensive, and time-consuming. We hereby propose an ensemble machine learning (ML) model for the prediction of T-cell epitopes (also known as immune relevant determinants or antigenic determinants) against SARS-CoV-2, utilizing physicochemical properties of amino acids. To train the model, we retrieved the experimentally determined SARS-CoV-2 T-cell epitopes from Immune Epitope Database and Analysis Resource (IEDB) repository. The model so developed achieved accuracy, AUC (Area under the ROC curve), Gini, specificity, sensitivity, F-score, and precision of 98.20%, 0.991, 0.994, 0.971, 0.982, 0.990, and 0.981, respectively, using a test set consisting of SARS-CoV-2 peptides (T-cell epitopes and non-epitopes) obtained from IEDB. The average accuracy of 97.98% was recorded in repeated 5-fold cross validation. Its comparison with 05 robust machine learning classifiers and existing T-cell epitope prediction techniques, such as NetMHC and CTLpred, suggest the proposed work as a better model. The predicted epitopes from the current model could possess a high probability to act as potential peptide vaccine candidates subjected to in vitro and in vivo scientific assessments. The model developed would help scientific community working in vaccine development save time to screen the active T-cell epitope candidates of SARS-CoV-2 against the inactive ones.

Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates

“…The epitopes predicted using the current model could serve as prospective peptide vaccine candidates for developing an epitope-based peptide vaccination against ZIKV. The model would save time for the scientific community working in vaccine development to screen active epitope candidates against inactive ones 45 . However it is pertinent to mention that the proposed model can only predict linear epitopes not the conformational ones.…”

Immunoinformatics Tools: A boon in vaccine Development Against Covid-19

“…NetCTLpan1.1 was shown to be more accurate than NetMHCpan 4.1, DeepLigand, PickPocket1.1, and MHCflurry2.0.and is seen applied in mishra et al [27], Quiros-Fernandez [28] and Bukhari et al [29].…”

Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets

“…The identification of immunodominant epitopes for PBV design through wet lab experiments is a difficult, expensive, and time-consuming process. Nevertheless, machine learning (ML) methods can predict these epitopes with considerable precision, thereby expediting vaccine development and reducing costs when contrasted with wet-lab techniques ( Bukhari et al, 2021 ). It is essential to highlight the pivotal role of T-cells in adaptive immunity, contributing crucial helper functions to various arms of the immune system and playing a vital role in controlling, clearing, and providing protection against a wide range of viral infections, as emphasized by Moss (2022) .…”