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Published on: March 25, 2014
Ensemble Machine Learning Model to Predict SARS-CoV-2 T-Cell Epitopes as Potential Vaccine Targets
Syed Nisar Hussain Bukhari1, Amit Jain1, Ehtishamul Haq2
1University Institute of Computing, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Mohali 140413, India.
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.
Insights
This study introduces a machine learning model to predict T-cell epitopes for SARS-CoV-2, accelerating the development of safer and more effective COVID-19 peptide vaccines.
Area of Science:
- Computational vaccinology
- Machine learning in immunology
- Virology and infectious disease
Background:
- The COVID-19 pandemic, caused by SARS-CoV-2, highlights the urgent need for effective vaccines.
- Epitope-based peptide vaccines offer potential for enhanced safety and immunogenicity but identifying T-cell epitopes is challenging.
- Current experimental methods for T-cell epitope identification are time-consuming and costly.
Purpose of the Study:
- To develop an ensemble machine learning model for predicting SARS-CoV-2 T-cell epitopes.
- To utilize physicochemical properties of amino acids for accurate epitope prediction.
- To provide a computational tool to expedite the screening of potential peptide vaccine candidates.
Main Methods:
- An ensemble machine learning model was developed using physicochemical properties of amino acids.
- The model was trained on experimentally validated SARS-CoV-2 T-cell epitopes from the IEDB repository.
- Performance was evaluated using accuracy, AUC, Gini, specificity, sensitivity, F-score, and precision, alongside 5-fold cross-validation.
Main Results:
- The developed model achieved high performance metrics, including 98.20% accuracy, 0.991 AUC, and 0.982 sensitivity on a test set.
- The model demonstrated an average accuracy of 97.98% in repeated 5-fold cross-validation.
- Comparison with existing methods like NetMHC and CTLpred indicated superior performance of the proposed model.
Conclusions:
- The proposed ensemble machine learning model accurately predicts SARS-CoV-2 T-cell epitopes.
- Predicted epitopes show high potential as candidates for peptide-based COVID-19 vaccines.
- This computational approach can significantly reduce time and resources in vaccine development by efficiently screening epitope candidates.
