The use of proteomics to inform subsequent biological validation studies requires substantial stringency in the analytical approach to ensure that the most important leads are followed. Our laboratory explores virulence determinants including an N-linked glycosylation (pgl) system and nutrient transporters in the gastrointestinal pathogen, Campylobacter jejuni; as well as those involved in Pseudomonas aeruginosa infection of the lungs in cystic fibrosis patients. Target identification is based on the response of the proteome to environmental conditions that mimic the host, including gut bile salts and lung mucous, low iron, growth temperatures and various inhibitors. Our proteomics workflow includes parallel label-based liquid chromatography / tandem mass spectrometry (LC-MS/MS) and system-wide validation using data independent analysis (DIA). Here, we discuss the correlation between large-scale datasets and how they facilitate subsequent studies, as well as highlight poorly or non-correlating data. In each case, we show how validated changes in the proteome reflect ‘functional reality’ that can be determined by integrated ‘omics including transcriptomics, metabolomics, lipidomics and lipid A analysis combined with molecular genetics and cellular and in vivo virulence assays. Furthermore, we show how a proteomics-informed ‘reverse vaccinology’ approach has led to the identification of 6 vaccine antigens, two of which provide >99% reduction in bacterial load in the lungs of P. aeruginosa-infected mice.