Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for Vaccine Development

Ghoshal, Bhargab; Ghoshal, Biraja; Swift, Stephen; Tucker, Allan

doi:10.48550/arxiv.2103.11214

Cited by 1 publication

(1 citation statement)

References 10 publications

(13 reference statements)

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“…In another study [42], the attention method and LSTM were paired for this task by combining protein with chemical and structural features, during which they achieved an accuracy of 79% and an AUC of 0.822. In another study, a Bayesian neural network was used for this task and was able to achieve an accuracy of 85% [63]. It can be seen from Table 9 that the random forest model developed in [43] was able to make good predictions, although there is a need for improvement in the model's performance.…”

Section: Discussionmentioning

confidence: 99%

Accelerating SARS-CoV-2 Vaccine Development: Leveraging Novel Hybrid Deep Learning Models and Bioinformatics Analysis for Epitope Selection and Classification

Ameen¹,

Mostafa

Ozsahin

et al. 2023

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It is essential to use highly antigenic epitope areas, since the development of peptide vaccines heavily relies on the precise design of epitope regions that can elicit a strong immune response. Choosing epitope regions experimentally for the production of the SARS-CoV-2 vaccine can be time-consuming, costly, and labor-intensive. Scientists have created in silico prediction techniques based on machine learning to find these regions, to cut down the number of candidate epitopes that might be tested in experiments, and, as a result, to lessen the time-consuming process of their mapping. However, the tools and approaches involved continue to have low accuracy. In this work, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and long short-term memory (LSTM) for the classification of peptides into epitopes or non-epitopes. Numerous transfer learning strategies were utilized, and the fine-tuned method gave the best result, with an AUC of 0.979, an f1 score of 0.902, and 95.1% accuracy, which was far better than the performance of the model trained from scratch. The experimental results obtained show that this model has superior performance when compared to other methods trained on IEDB datasets. Using bioinformatics tools such as ToxinPred, VaxiJen, and AllerTop2.0, the toxicities, antigenicities, and allergenicities, respectively, of the predicted epitopes were determined. In silico cloning and codon optimization were used to successfully express the vaccine in E. coli. This work will help scientists choose the best epitope for the development of the COVID-19 vaccine, reducing cost and labor and thereby accelerating vaccine production.

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Section: Discussionmentioning

confidence: 99%