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

• Published in: Scientific reports 2024-03, Vol.14 (1), p.6737-6737, Article 6737
• Main Authors: Ozsahin, Dilber Uzun, Ameen, Zubaida Said, Hassan, Abdurrahman Shuaibu, Mubarak, Auwalu Saleh
• Format: Article
• Language: English
• Subjects: 631/114; 631/154; 631/250; Allergies; Amino acid sequence; Animals; Bees; Biochemical characteristics; Biochemistry; Cloning; Computational Biology - methods; Coronaviruses; COVID-19; COVID-19 - prevention & control; COVID-19 Vaccines; Deep learning; Epitopes; Epitopes, B-Lymphocyte; Epitopes, T-Lymphocyte; Humanities and Social Sciences; Humans; Immune response; Long short-term memory; Molecular Docking Simulation; multidisciplinary; Neural networks; Outbreaks; Protein composition; Protein purification; Proteomes; RNA viruses; SARS-CoV-2; Science; Science (multidisciplinary); Severe acute respiratory syndrome coronavirus 2; Toxicity; Vaccine development; Vaccine efficacy; Vaccines; Viral Vaccines
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-55762-7

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus that caused the outbreak of the coronavirus disease 2019 (COVID-19). The COVID-19 outbreak has led to millions of deaths and economic losses globally. Vaccination is the most practical solution, but finding epitopes (antigenic peptide regions) in the SARS-CoV-2 proteome is challenging, costly, and time-consuming. Here, we proposed a deep learning method based on standalone Recurrent Neural networks to predict epitopes from SARS-CoV-2 proteins easily. We optimised the standalone Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) with a bioinspired optimisation algorithm, namely, Bee Colony Optimization (BCO). The study shows that LSTM-based models, particularly BCO-Bi-LSTM, outperform all other models and achieve an accuracy of 0.92 and AUC of 0.944. To overcome the challenge of understanding the model predictions, explainable AI using the Shapely Additive Explanations (SHAP) method was employed to explain how Blackbox models make decisions. Finally, the predicted epitopes led to the development of a multi-epitope vaccine. The multi-epitope vaccine effectiveness evaluation is based on vaccine toxicity, allergic response risk, and antigenic and biochemical characteristics using bioinformatic tools. The developed multi-epitope vaccine is non-toxic and highly antigenic. Codon adaptation, cloning, gel electrophoresis assess genomic sequence, protein composition, expression and purification while docking and IMMSIM servers simulate interactions and immunological response, respectively. These investigations provide a conceptual framework for developing a SARS-CoV-2 vaccine.