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Antibiotic resistant Staphylococcus aureus is a major public health concern effecting millions of people annually. Medical science has documented completely untreatable S. aureus infections. These strains are appearing in the community with increasing frequency. New diagnostic and therapeutic options are needed to combat this deadly infection. Interestingly, around 50% of the proteins in S. aureus are annotated as hypothetical. Methods to select hypothetical proteins related to antibiotic resistance have been inadequate. This study uses differential gene expression to identify hypothetical proteins related to antibiotic resistant phenotype strain variations. We apply computational tools to predict physiochemical properties, cellular location, sequence-based homologs, domains, 3D modeling, active site features, and binding partners. Nine of 23 hypothetical proteins were &lt;100 residues, unlikely to be functional proteins based on size. Of the 14 differentially expressed hypothetical proteins examined, confident predictions on function could not be made. Most identified domains had unknown functions. Six hypothetical protein models had &gt;50% confidence over &gt;20% residues. These findings indicate the method of hypothetical protein identification is sufficient; however, current scientific knowledge is inadequate to properly annotate these proteins. This process should be repeated regularly until entire genomes are clearly and accurately annotated.

Improved Annotations of 23 Differentially Expressed Hypothetical Proteins in Methicillin Resistant S. aureus