title: Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties.
abstract: Subunit vaccine discovery is an accepted clinical priority. The empirical approach is time- and labor-consuming and can often end in failure. Rational information-driven approaches can overcome these limitations in a fast and efficient manner. However, informatics solutions require reliable algorithms for antigen identification. All known algorithms use sequence similarity to identify antigens. However, antigenicity may be encoded subtly in a sequence and may not be directly identifiable by sequence alignment. We propose a new alignment-independent method for antigen recognition based on the principal chemical properties of protein amino acid sequences. The method is tested by cross-validation on a training set of bacterial antigens and external validation on a test set of known antigens. The prediction accuracy is 83% for the cross-validation and 80% for the external test set. Our approach is accurate and robust, and provides a potent tool for the in silico discovery of medically relevant subunit vaccines.
authors.affiliation: Faculty of Pharmacy, Medical University of Sofia, Dunav st. 2, 1000 Sofia, Bulgaria. doytchinova@gmail.com
authors.forename: Irini A
authors.initials: IA
authors.lastname: Doytchinova
authors.forename: Darren R
authors.initials: DR
authors.lastname: Flower
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000942:Antigens, Bacterial / Q000276:immunology*; D001428:Bacterial Vaccines / Q000276:immunology*; D015233:Models, Statistical; D012680:Sensitivity and Specificity; D022223:Vaccines, Subunit / Q000276:immunology*
chemical_list: D000942:Antigens, Bacterial; D001428:Bacterial Vaccines; D022223:Vaccines, Subunit