Prediction of Epitopes in the Proteome of Helicobacter pylori

Abstract

Helicobacter pylori (H. pylori) is classified by the World Health Organization (WHO) as a group I carcinogen and is one of the most efficient human pathogens with over half of the world's population colonized by this gram-negative spiral bacterium. H. pylori can cause a chronic infection in the stomach during early childhood that persists throughout life due to diverse mechanisms of immune response evasion. H. pylori has several factors strongly associated with increased risk of disease such as toxins, adhesins, and chemoattractants, some of which are highly polymorphic, phase variable, and have different functions. Conventional treatments involve the use of antibiotics. However, treatment frequently fails due to the resistance H. pylori has progressively developed to antibiotics. This creates the need for different treatments made possible by identifying new therapeutic targets in the pathogen’s genome.The purpose of this study was an in silico prediction of T- and B- epitopes in H. pylori proteins. Twenty-two external membrane proteins from H. pylori Strain 26695 (accession number NC_000915) were identified using the web tool Vaxign (http://www.violinet.org/vaxign/). A total of one-hundred epitopes (60 class I epitopes and 40 class II epitopes) that could be used to develop novel non-antibiotics drugs for an H. pylori infection were predicted.

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

“…RV has been successfully applied to vaccine discovery for pathogens such as Group B meningococcus and led to the license Bexsero vaccine ( 13 ). Among current RV prediction tools ( 14 , 15 ), Vaxign is the first web-based RV program ( 16 ) and has been used to predict vaccine candidates against different bacterial and viral pathogens ( 17 – 19 ). Recently we have also developed a machine learning approach called Vaxign-ML to enhance prediction accuracy ( 20 ).…”

Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning

“…The Vaxign web service has been running since 2010 and is accessed by thousands of users per year. Over the past decade, Vaxign has been applied by other research groups to predict vaccine candidates against different pathogens such as Helicobacter pylori ( 13 ), Mycobacterium tuberculosis ( 14 ), and African swine fever virus ( 15 ). To push the performance of ML-based RV prediction further, we created the ML-based Vaxign, or Vaxign-ML, in 2020.…”