Vaccine candidate prediction software tools | Immune system data analysis
Reverse vaccinology is an emerging vaccine development approach that starts with the prediction of vaccine targets by bioinformatics analysis of microbial genome sequences. Predicted proteins are selected based on desirable attributes. Normal wet laboratory experiments are conducted in a later stage to test all or selected vaccine targets.
The first server for alignment-independent prediction of protective antigens of bacterial, viral and tumour origin. VaxiJen contains models derived by auto- and cross-covariance pre-processing of amino acids properties. The predictive ability of our models was tested by internal leave-one-out cross-validation on training sets and by external validation on test sets. The models showed remarkable stability, as tested by combinations of the positive set and five different negative sets. Thus, VaxiJen is a reliable and consistent tool for the prediction of protective antigens. It can be used singly or in combination with other bioinformatics tools used for reverse vaccinology.
Predicts vaccine targets against different pathogens. Vaxign is based on the principle of reverse vaccinology and composed by two programs: (1) Vaxign Query, which provides precomputed results to explore about 350 genomes, and (2) Dynamic Vaxign Analysis which allows users to submit their own parameters. The software includes a pipeline based on various vaccine design criteria using microbial genomic and protein sequences as input data.
Predicts worthy vaccine candidates from large volumes of superfluous, disseminated and noisy data. Vacceed is the collective name for a framework of linked bioinformatics programs, Perl scripts, R functions and Linux shell scripts. The software has been designed to facilitate an automated, high-throughput in silico approach to vaccine candidate discovery for eukaryotic pathogens. Core to Vacceed are user-definable configuration files.
Identifies candidates for vaccines in case of fungal infections. FungalRV uses candidate occurrence in immuno-compromised or otherwise debilitated host to make its predictions. It is based on highly accurate support vector machine (SVM) models. The platform provides a database that offers immuno-informatics data on 307 predicted adhesins and adhesin like proteins obtained by the tool run on entire proteomes of eight human pathogenic fungal species.
Aims to identify credible protein vaccine candidates (PVCs) from a proteome or protein(s) sequences for sub-unit vaccine development. Jenner-predict allows PVCs’ prediction and analysis of their vaccine potentials. Moreover, the predicted PVCs’ are measured by examining experimentally known IEDB epitopes, absence of autoimmunity and conservation in different strains in order to measure their vaccine potential.
Provides a web application for generating antigenic maps. ATIVS can perform two types of analysis: a serological data analysis for all influenza subtypes and HA1 sequence data for influenza A/H3N2 viruses; and, a sequence data analysis to obtain predicted antigenic distances calculating until 500 sequences. It aims to help in observing antigenic evolution of influenza viruses and facilitate the selection of vaccine strains.
Selects mutated peptides for personalized therapeutic cancer vaccination. Vaxrank is a package determines which peptides should be used in a vaccine from tumor-specific somatic mutations, tumor RNA sequencing data, and a patient’s Human Leukocyte Antigen (HLA) type. Additionally, it considers surrounding non-mutated residues in a peptide to prioritize vaccine peptide candidates and to improve the odds of successful synthesis.