VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology

Muhammad Rizwan,Anam Naz,Amjad Ali,Tamsila Parveen,Kanwal Naz,Jamil Ahmad,Muhammad Ahsan,Ayesha Obaid

doi:10.1186/s12859-017-1540-0

Muhammad Rizwan, Anam Naz + Show 6 more

Open Access

https://doi.org/10.1186/s12859-017-1540-0

Copy DOI

Abstract

BackgroundWith advances in reverse vaccinology approaches, a progressive improvement has been observed in the prediction of putative vaccine candidates. Reverse vaccinology has changed the way of discovery and provides a mean to propose target identification in reduced time and labour. In this regard, high throughput genomic sequencing technologies and supporting bioinformatics tools have greatly facilitated the prompt analysis of pathogens, where various predicted candidates have been found effective against certain infections and diseases. A pipeline, VacSol, is designed here based on a similar approach to predict putative vaccine candidates both rapidly and efficiently.ResultsVacSol, a new pipeline introduced here, is a highly scalable, multi-mode, and configurable software designed to automate the high throughput in silico vaccine candidate prediction process for the identification of putative vaccine candidates against the proteome of bacterial pathogens. Vaccine candidates are screened using integrated, well-known and robust algorithms/tools for proteome analysis, and the results from the VacSol software are presented in five different formats by taking proteome sequence as input in FASTA file format. The utility of VacSol is tested and compared with published data and using the Helicobacter pylori 26695 reference strain as a benchmark.ConclusionVacSol rapidly and efficiently screens the whole bacterial pathogen proteome to identify a few predicted putative vaccine candidate proteins. This pipeline has the potential to save computational costs and time by efficiently reducing false positive candidate hits. VacSol results do not depend on any universal set of rules and may vary based on the provided input. It is freely available to download from: https://sourceforge.net/projects/vacsol/.

Highlights

With advances in reverse vaccinology approaches, a progressive improvement has been observed in the prediction of putative vaccine candidates
Implementation of VacSol for test data The first working step was performed by identifying the non-host homologs, required to elute host homologous proteins to restrict the chance of autoimmunity [20, 21]
For BLAST non-human homologs, criteria included a Bit Score >100, E-Value 35% [24]. These 1452 proteins were subjected for further protein prioritization processing by VacSol to predict subcellular localization. 65 proteins were found to be in the secretome and exoproteome, of which 23 proteins lie in the extracellular region, and 42 were screened as outer-membrane proteins

Summary

Introduction

With advances in reverse vaccinology approaches, a progressive improvement has been observed in the prediction of putative vaccine candidates. This methodology has the potential to produce successful vaccines and has long been in practice, but considered timeconsuming and inadequate for most pathogens This caveat is evident when microbes are inactive, protective, or even in the case where antigen expression is decreased; rendering the conventional approach a significant challenge for putative vaccine candidate discovery [2, 3]. With the introduction of high-throughput sequencing techniques over the last decade and the advent of bioinformatics approaches, Rino Rappouli revolutionized Pasteur’s vaccinology procedure by introducing a novel “reverse vaccinology” method [4,5,6] This advanced in-silico technique for vaccine prediction couples genomic information and analysis with bioinformatics tools. Reverse vaccinology is recognized as safer and more reliable as compared to conventional vaccinology methods [10, 11]

Results

Discussion

Conclusion