Table 2.

Reverse vaccinology/functional/structural genomics approaches: features and limitations

Approach	Features	Limitations
Reverse vaccinology
Classical	Fast	It cannot be used to develop vaccines on the basis of nonprotein-coding antigens, like lipopolysaccharides
	Comprehensive: it can virtually identify all potential antigens in a pathogen's genome, irrespective of their abundance, phase of expression and immunogenicity	It needs animal models, because there is a potential lack of method to measure in vitro efficacy
	It could be used against all pathogens, including those that cannot be grown in vitro	It lacks of information on gene expression
Pan-genomic	Very exhaustive	It requires the sequences of multiple isolates
	It performs interspecies and intraspecies comparisons	It needs a crucial selection of very representative strains of a given microorganism
	It could be useful to develop universal vaccines
Functional genomics
Transcriptomics	Very comprehensive	There is not a direct correlation between mRNA and protein expression level
	It provides indications on semiquantitative data of genes expressed during infection	It does not give information on protein localization and gene expression regulation at the transcriptional level
	It can identify pathogenicity factors	It requires a high number of bacteria
Proteomics	It provides qualitative and quantitative data on protein expression	It could identify only a fraction of all proteins
	It can identify membrane-associated proteins	It requires a large number of bacteria cells
		It is time-consuming and expensive
Structural genomics
	It can provide insights into protein structure, create comparative models of the most similar proteins and assign a previously unknown molecular function to a protein, providing the opportunity to recognize homologies undetectable by sequence comparison	It is practically limited to comparative modeling for evolutionarily related proteins, with consequent problems for accurate protein model in case of low sequence similarity (less than 30%)
	It can provide a complete understanding of molecular interactions	It needs the implementing of de novo protein structure prediction for unique folds determination in the case of sequences that are divergent from those already in the Protein Data Bank
	It can help the rational design of target epitopes to be used as vaccine candidates and increase the understanding of immune recognition mechanisms	Structural genomics efforts often study individual protein domains rather than whole protein or complexes