VaxiJen Help  
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 protein datasets were used to derive models for prediction of whole protein antigenicity. The set consisted of 317 known antigens and 317 non-antigens. Three machine learning models were derived using xgboost algorithm, random forest algorithm and random subspace with knn(n=1) algorithm, respectively. The derived models were tested by internal 10-fold cross-validation and external validation using test sets. The prediction probability was calculated by voting and accounting the number of servers with positive vote. 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. The results page lists the selected target, the protein sequence, its prediction probability, and a statement of protective antigen or non-antigen. The server can be used on its own or in combination with alignment-based prediction methods.

Publications:

Zaharieva N, Dimitrov I, Flower DR, Doytchinova I. VaxiJen Dataset of Bacterial Immunogens: An Update. Curr. Comp.-Aided Drug Des. 15, 398-400, 2019.
   
 
[ Drug Design and Bioinformatics Lab] [ Contact Us]
 
 
  © Drug design and bioinformatics group, Medical University of Sofia 2020