Analysis of known bacterial protein vaccine antigens reveals biased physical properties and amino acid composition

Abstract

Many vaccines have been developed from live attenuated forms of bacterial pathogens or from killed bacterial cells. However, an increased awareness of the potential for transient side-effects following vaccination has prompted an increased emphasis on the use of sub-unit vaccines, rather than those based on whole bacterial cells. The identification of vaccine sub-units is often a lengthy process and bioinformatics approaches have recently been used to identify candidate protein vaccine antigens. Such methods ultimately offer the promise of a more rapid advance towards preclinical studies with vaccines. We have compared the properties of known bacterial vaccine antigens against randomly selected proteins and identified differences in the make-up of these two groups. A computer algorithm that exploits these differences allows the identification of potential vaccine antigen candidates from pathogenic bacteria on the basis of their amino acid composition, a property inherently associated with sub-cellular location.

Are bacterial vaccine antigens T-cell epitope depleted?

“…The composition of each subset in our control database did not differ significantly from that of similar subsets generated by another study [30] based on all gram-negative bacterial sequence entries in the SwissProt [31] database. Thus, the vaccine antigen dataset [15] differs from control proteins in possessing both fewer FILMVWY amino acids and fewer nonamers predicted to bind MHC class I and class II alleles, although, because most epitopes are amphipathic (both hydrophobic and hydrophilic), the reduction in hydrophobic residues cannot be the sole reason for the lack of observed epitopes within this dataset.…”

“…Therefore, the vaccine antigen dataset differs from the control dataset of randomly selected bacterial proteins in possessing a lower density of nonamers that bind to MHC class II alleles [15]. Furthermore, the vaccine antigen dataset is not an atypical population of bacterial proteins per se; rather, their T-cell epitope content is typical of the secreted and outer membrane proteins, groups of proteins to which most of the this dataset belong to.…”

“…Vaccine antigens are depleted in predicted T-cell epitopes We compared a dataset of 70 proteins experimentally known to confer immune protection against bacterial pathogens in at least one animal model of human disease [15] (the vaccine antigen dataset) against a control dataset of randomly selected sequences derived from the predicted proteomes of pathogenic bacteria. All of the sequences in these datasets were annotated with experimentally determined subcellular locations ( Table 2).…”

Machine learning approaches for prediction of linear B‐cell epitopes on proteins

“…The parameters were grouped into amino acid propensity scales, neighborhood propensities, and sequence complexities. The 55 single amino acid propensity scales available at ProtScale (http://us.expasy.org/cgi-bin/protscale.pl; data status as of November 2003), and the secondary structure scale proposed by (Mayers et al, 2003) were used to calculate average values per peptide for each propensity scale. These scales reflect mainly physicochemical attributes such as hydrophobicity and bulkiness, but also others like mutability.…”

ImmunoGrid: towards agent-based simulations of the human immune system at a natural scale

“…Although the development of human simulators is our ultimate goal, the kind of validation experiment that was undertaken would not have been possible with human subjects, as it is currently impracticable (as well as perhaps unethical) to validate a simulator by testing vaccine schedules on large populations of cancer patients. Furthermore, animal (particularly rodent) models are regularly used in vaccine design, and immune protection in appropriate animal models has been used as a proxy for a human immune response in preparing a dataset of vaccine antigens for testing computational models (Mayers et al 2003).…”