ReVac: a reverse vaccinology computational pipeline for prioritization of prokaryotic protein vaccine candidates.

ABSTRACT:

Background

Reverse vaccinology accelerates the discovery of potential vaccine candidates (PVCs) prior to experimental validation. Current programs typically use one bacterial proteome to identify PVCs through a filtering architecture using feature prediction programs or a machine learning approach. Filtering approaches may eliminate potential antigens based on limitations in the accuracy of prediction tools used. Machine learning approaches are heavily dependent on the selection of training datasets with experimentally validated antigens (positive control) and non-protective-antigens (negative control). The use of one or few bacterial proteomes does not assess PVC conservation among strains, an important feature of vaccine antigens.

Results

We present ReVac, which implements

SUBMITTER: D'Mello A 

PROVIDER: S-EPMC6916091 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature