Identification of epitope-based peptide vaccine candidates against enterotoxigenic Escherichia coli: a comparative genomics and immunoinformatics approach

Abstract

Enterotoxigenic Escherichia coli (ETEC) associated diarrhea remains a global killer with an estimated annual incidence rate of 840 million infections and 3 800 000 deaths worldwide. There are no vaccines available for ETEC and the traditional vaccine development approach is arduous and time consuming. Thus, alternative in silico approaches for epitope prediction have engrossed the interest of researchers to reduce resources and time of vaccine development. Computational approaches are playing a crucial role in fighting against rapidly growing infectious organisms. In this study we employed an integrated comparative genomics and immunoinformatics approach for proteome scale identification of peptide vaccine candidates. The proteins shared between both ETEC E24377A and H10407 strains, but lacking in commensal E. coli SE11, were subjected to immunoinformatics analysis. For a protein pool shared between different pathogenic ETEC strains, we investigated varied physicochemical and immunogenic properties to prioritize potential vaccine candidates. Epitopes were further tested using docking studies to bind in the MHC-I binding cleft. Predicted epitopes provided more than a 95% population coverage in diarrhea endemic regions presented by major MHC-I supertypes, and bind efficiently to a MHC molecule. We conclude by accentuating that the epitopes predicted in this study are believed to accelerate the development of successful vaccines to control or prevent ETEC infections, albeit the results require experimental validation using model organisms. This study underscores that in silico approaches, together with omics data, hold great potential to be utilized for rapid and reliable genome-wide screening for identification of vaccine candidates against devastating infectious diseases.

Antigenic Peptide Prediction From E6 and E7 Oncoproteins of HPV Types 16 and 18 for Therapeutic Vaccine Design Using Immunoinformatics and MD Simulation Analysis

“…Immunoinformatics has been shown to be useful in predicting antigenic peptide B-cell and T-cell epitopes for peptide vaccine development [for recent reviews see for example (58, 59)]. Studying the peptide-MHC interactions by molecular docking studies [see for example references (60–65)] has been used to aid in evaluating the binding affinity of the predicted peptide fragments since a sufficient binding affinity to an antigen presenting MHC-I protein is the most critical requirement for a peptide to elicite a proper CTL response (37, 66). In general, the docking method and results need to be validated against the available experimental peptide-MHC complex structures.…”

Engineering a novel subunit vaccine against SARS-CoV-2 by exploring immunoinformatics approach

“… Andrusier et al, 2007 , Bacchetta et al, 2005 , Biovia et al, 2000 , Carbone et al, 2003 , Cooper and Nemerow, 1984 , Dhanda et al, 2013 , Garcia et al, 1999 , Gu et al, 2017 , Guruprasad et al, 1990 , Ikai, 1980 , Inovio Pharmaceuticals, 2020 , Johnston et al, 2016 , Kaiser Permanente Washington Health Research Institute . Kaiser PermanenteWashington Health Research Institute; Seattle , Kumar et al, 2015 , Kyte and Doolittle, 1982 , Large-scale validation of methods for cytotoxic et al, 2007 , Lee and Nguyen, 2015 , Mehla and Ramana, 2016 , Murina et al, 2019 , Perlman, 2020 , Purcell et al, 2007 , Shieber, 2020 , Sievers et al, 2011 , Tameris et al, 2013 , Wen et al, 2013 , Yadav et al, 2014 , Zhang, 2018 .…”

In silico analysis and in vivo assessment of a novel epitope-based vaccine candidate against uropathogenic Escherichia coli

“…Recently, subunit vaccines, mostly consisting of MHC-I and MHC-II T-cell epitopes, and B-cell epitopes, have been considered 18 because of their safety compared to killed and attenuated vaccines, their stability under different conditions, cost-effective production, high specificity, and capacity to deliver high doses of antigens 19 , 20 .…”