Vaccine development in the post-genomic era often begins with the in silico screening of genome information,
with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown.
Despite the obvious advantages of this approach - such as speed and cost efficiency - its success remains dependent on
the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic
for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties.
The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment.
The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. |
To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction,
which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties.
Bacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity.
Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation
and external validation using test sets. The models performed well in both validations showing prediction accuracy from 70% to 89%.
The models were implemented in a server, which we call VaxiJen.
VaxiJen is the first server for alignment-independent prediction of protective antigens.
It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment.
Protein sequences can be submitted as single proteins or uploaded as a multiple sequence file in fasta format.
A single target organism can be selected.
Additionally, ACC coefficients can be output.
This option makes the server useful for general ACC calculations of proteins.
The results page lists the selected target, the protein sequence, its prediction probability, and a statement of protective antigen or non-antigen,
according to a predefined cutoff. The server can be used on its own or in combination with alignment-based prediction methods.
Irini A Doytchinova and Darren R Flower. VaxiJen: a
server for prediction of protective antigens, tumour antigens and subunit
vaccines. BMC Bioinformatics. 2007 8:4.
Irini A Doytchinova and Darren R Flower. Identifying candidate subunit vaccines using an alignment-independent method
based on principal amino acid properties. Vaccine. 2007 25:856-866.
Irini A Doytchinova and Darren R Flower. Bioinformatic Approach for Identifying Parasite and Fungal Candidate Subunit Vaccines. Open Vaccines Journal, 2008 1:22-26.