A new approach for determining SARS-CoV-2 epitopes using machine learning-based in silico methods

The emergence of machine learning-based in silico tools has enabled rapid and high-quality predictions in the biomedical field. In the COVID-19 pandemic, machine learning methods have been used in many topics such as predicting the death of patients, modeling the spread of infection, determining future effects, diagnosis with medical image analysis, and forecasting the vaccination rate. However, there is a gap in the literature regarding identifying epitopes that can be used in fast, useful, and effective vaccine design using machine learning methods and bioinformatics tools. Machine learning methods can give medical biotechnologists an advantage in designing a faster and more successful vaccine. The motivation of this study is to propose a successful hybrid machine learning method for SARS-CoV-2 epitope prediction and to identify nonallergen, nontoxic, antigen peptides that can be used in vaccine design from the predicted epitopes with bioinformatics tools. The identified epitopes will be effective not only in the design of the COVID-19 vaccine but also against viruses from the SARS family that may be encountered in the future. For this purpose, epitope prediction performances of random forest, support vector machine, logistic regression, bagging with decision tree, k-nearest neighbor and decision tree methods were examined. In the SARS-CoV and B-cell datasets used for education in the study, epitope estimation was performed again after the datasets were balanced with the synthetic minority oversampling technique (SMOTE) method since the epitope class samples were in the minority compared to the nonepitope class. The experimental results obtained were compared and the most successful predictions were obtained with the random forest (RF) method. The epitope prediction performance in balanced datasets was found to be higher than that in the original datasets (94.0% AUC and 94.4% PRC for the SMOTE-SARS-CoV dataset; 95.6% AUC and 95.3% PRC for the SMOTE-B-cell dataset). In this study, 252 peptides out of 20312 peptides were determined to be epitopes with the SMOTE-RF-SVM hybrid method proposed for SARS-CoV-2 epitope prediction. Determined epitopes were analyzed with AllerTOP 2.0, VaxiJen 2.0 and ToxinPred tools, and allergic, nonantigen, and toxic epitopes were eliminated. As a result, 11 possible nonallergic, high antigen and nontoxic epitope candidates were proposed that could be used in protein-based COVID-19 vaccine design (“VGGNYNY”, “VNFNFNGLTG”, “RQIAPGQTGKI”, “QIAPGQTGKIA”, “SYECDIPIGAGI”, “STFKCYGVSPTKL”, “GVVFLHVTYVPAQ”, “KNHTSPDVDLGDI”, “NHTSPDVDLGDIS”, “AGAAAYYVGYLQPR”, “KKSTNLVKNKCVNF”). It is predicted that the few epitopes determined by machine learning-based in silico methods will help biotechnologists design fast and accurate vaccines by reducing the number of trials in the laboratory environment. © 2022 Elsevier LtdTürkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK: 121E326This study was supported by Turkish Scientific and Technical Research Council, Turkey-TÜBİTAK (Project Number: 121E326).This study was supported by Turkish Scientific and Technical Research Council, Turkey -TÜBİTAK (Project Number: 121E326 )

Computational Design of a Novel VLP-Based Vaccine for Hepatitis B Virus

Hepatitis B virus (HBV) is a global virus responsible for a universal disease burden for millions of people. Various vaccination strategies have been developed using viral vector, nucleic acid, protein, peptide, and virus-like particles (VLPs) to stimulate favorable immune responses against HBV. Given the pivotal role of specific immune responses of hepatitis B surface antigen (HBsAg) and hepatitis B core antigen (HBcAg) in infection control, we designed a VLP-based vaccine by placing the antibody-binding fragments of HBsAg in the major immunodominant region (MIR) epitope of HBcAg to stimulate multilateral immunity. A computational approach was employed to predict and evaluate the conservation, antigenicity, allergenicity, and immunogenicity of the construct. Modeling and molecular dynamics (MD) demonstrated the folding stability of HBcAg as a carrier in inserting Myrcludex and �a� determinant of HBsAg. Regions 1�50 and 118�150 of HBsAg were considered to have the highest stability to be involved in the designed vaccine. Molecular docking revealed appropriate interactions between the B cell epitope of the designed vaccine and the antibodies. Totally, the final construct was promising for inducing humoral and cellular responses against HBV. © Copyright © 2020 Mobini, Chizari, Mafakher, Rismani and Rismani

Novel epitope based peptides for vaccine against SARS-CoV-2 virus: immunoinformatics with docking approach

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative viral strain for the contagious pandemic respiratory illness in humans which is a public health emergency of international concern. There is a desperate need for vaccines and antiviral strategies to combat the rapid spread of SARS-CoV-2 infection.Methods: The present study based on computational methods has identified novel conserved cytotoxic T-lymphocyte epitopes as well as linear and discontinuous B-cell epitopes on the SARS-CoV-2 spike (S) protein. The predicted MHC class I and class II binding peptides were further checked for their antigenic scores, allergenicity, toxicity, digesting enzymes and mutation.Results: A total of fourteen linear B-cell epitopes where GQSKRVDFC displayed the highest antigenicity-score and sixteen highly antigenic 100% conserved T-cell epitopes including the most potential vaccine candidates MHC class-I peptide KIADYNYKL and MHC class-II peptide VVFLHVTYV were identified. Furthermore, the potential peptide QGFSALEPL with high antigenicity score attached to larger number of human leukocyte antigen alleles. Docking analyses of the allele HLA-B*5201 predicted to be immunogenic to several of the selected epitopes revealed that the peptides engaged in strong binding with the HLA-B*5201 allele.Conclusions: Collectively, this research provides novel candidates for epitope-based peptide vaccine design against SARS-CoV-2 infection

Biology Science in Promoting Public Health

Health promotion is not a new concept. It has long been recognized that health is determined not only by factors within the health sector, but also by other factors. During the 19th century, when the theory of pathogenic illness was not yet established, the specific cause of most illnesses was thought to be "miasism", but poverty, misery, poor living conditions, lack of education, etc was recognized contributed to illness and death. Biology on its applications much contributed to the development of public health. This study try to generate candidate for typhoid vaccine. Until now, typhoid fever is still the biggest health problem worldwide, including in Indonesia. The purpose of this study was to screen and design B cell and T cell epitope from the outer membrane protein/OMP (OMP-A and OMP-C) S. Typhi. The results showed that the two best B cell epitopes, namely 28GEREAGKSGIGAGIGS43 (OMP-A) and 79YQIQGNQTEGGNDS92 (OMP-C) were predicted to be non-allergenic, non-toxin and have high antigenicity values, respectively 1.9244 and 2.2842. Meanwhile, the best T cell epitopes, 3KRVFVIAAI11 (OMP-A) and 199LTYAIGEGF207 (OMP-C) Meanwhile were predicted to be non-allergenic, non-toxin, and have quite high immunogenicity scores, 0.35 and 0.31 respectively. T cell epitope affinity analysis for MHC-I showed that the 3KRVFVIAAI11 epitope was interactive with the HLA- C*12:03, HLA-C*14:02, HLA-C*03:03 and 199LTYAIGEGF207 alleles interactively with the HLA-C *03 allele: 03, HLA-C*14:02, HLA-C*12:03, HLA- B*58:01, HLA-B*15:01. Globally, individuals who can express an interactive MHC-I allele for both T cell epitopes are predicted to be 30.03%