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

Abstract

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.

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction

“…Due to their ability to facilitate the research and development of vaccines for a variety of illnesses that are rapidly developing, epitopes are essential for scientific and clinical investigations 23 . A potential vaccine against SARS-CoV-2 16 , 22 , 24 – 26 , malaria 27 , 28 , Ebola virus 29 , dengue virus field 30 , hepatitis B virus field 31 , Staphylococcus aureus 32 , 33 , Acinetobacter baumannii 34 – 36 , and Helicobacter pylori 37 has been developed using a variety of immuno-informatics techniques. Recently, numerous immunoinformatic tools such as 2 , 15 , 24 , 38 , 39 have been utilized in the design of TB epitope-based vaccines.…”

PreSubLncR: Predicting Subcellular Localization of Long Non-Coding RNA Based on Multi-Scale Attention Convolutional Network and Bidirectional Long Short-Term Memory Network

“…Traditional convolutional neural networks (CNNs) are usually used to process image data, and their basic structure is two-dimensional [40,41]. However, text can be regarded as one-dimensional sequence data, so the idea of a one-dimensional CNN can be borrowed and applied to text data to extract text features, which is the basic concept of TextCNN.…”

Enhancing explainable SARS-CoV-2 vaccine development leveraging bee colony optimised Bi-LSTM, Bi-GRU models and bioinformatic analysis

“…DL efficiently reduces time, expense, and burden on COVID-19 diagnosis 32 – 34 . For instance, Yang et al 35 , Ameen et al 36 , Abbasi et al 37 suggested computational and deep learning approaches to create multi-epitope-based vaccines against SARS-CoV-2. They found several potential peptide vaccines that may be further tested utilising experimental studies, in addition to showing that their technique creates vaccine candidates with useful immunogenicity and minimal toxicity.…”