REVVAC

Identification of vaccine candidates using reverse vaccinology

 Coordinatore UNIVERSITY OF SOUTHAMPTON 

 Organization address address: Highfield
city: SOUTHAMPTON
postcode: SO17 1BJ

contact info
Titolo: Ms.
Nome: Yan
Cognome: Qiao
Email: send email
Telefono: +44 2380 593907

 Nazionalità Coordinatore United Kingdom [UK]
 Totale costo 100˙000 €
 EC contributo 100˙000 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-2013-CIG
 Funding Scheme MC-CIG
 Anno di inizio 2014
 Periodo (anno-mese-giorno) 2014-03-01   -   2018-02-28

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    UNIVERSITY OF SOUTHAMPTON

 Organization address address: Highfield
city: SOUTHAMPTON
postcode: SO17 1BJ

contact info
Titolo: Ms.
Nome: Yan
Cognome: Qiao
Email: send email
Telefono: +44 2380 593907

UK (SOUTHAMPTON) coordinator 100˙000.00

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

directly    genome    vaccines    sequence    reverse    predicted    protective    bacteria    subunit    formulated    bioinformatics    protein    pathogenic    vaccinology    candidates    vaccine    tested    hypothesis    revvac    uses    data    bacterial   

 Obiettivo del progetto (Objective)

'Reverse vaccinology (RevVac) is an emerging field that uses bioinformatics to identify vaccine candidates directly from the genome sequence of pathogenic bacteria. The long-term goal of the proposed research is to identify new vaccine candidates that may be formulated into subunit vaccines that protect against bacterial pathogens. The objective of this particular application is to dramatically improve upon methods of RevVac developed in my laboratory. The central hypothesis is that my RevVac procedure will be enhanced by building larger training data sets, incorporating new protein annotation, assessing multiple machine learning methods, investigating additional validation techniques, and by facilitating open access to predicted vaccine candidates to the vaccine community through the Bacterial Protective Antigen Database (BPAD).

Guided by published preliminary data in the journal Vaccine, my hypothesis will be tested by pursuing the following objectives: (1) To improve the prediction of vaccine candidates, (2) To understand what makes a bacterial protein a good vaccine candidate, and (3) To create a vaccine resource for bacteriologists. The rationale for the proposed research is that once there is high confidence in predicted vaccine candidates, then only a small number will need to be tested in animal models in order to identify those with protective effects that can be formulated into subunit vaccines.

This proposal is responsive to the objectives of the work programme since it will establish my novel research talents and knowledge with respect to reverse vaccinology and gene expression biomarker analysis in the EU. In addition, I will bring a plethora of longstanding collaborations solidified while working in the USA. Finally, the University of Southampton is fully committed to my long-term EU integration vaccinology (RevVac) is an emerging field that uses bioinformatics to identify vaccine candidates directly from the genome sequence of pathogenic bacteria.'

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