Prediction of protein subcellular localization for prokaryotes, using experimentally-based criteria

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There is a growing interest in both agriculture and medicine in computationally predicting the subcellular localization of bacterial proteins, to identify new potentially accessible (surface-exposed) drug targets and vaccine candidates from genomic sequence data. There is also a growing interest in the identification of proteins of particular localizations in archaeal prokaryotes, as such proteins have potential significant industrial applications (i.e. thermotolerant enzymes) and environmental utility (detection of ecologically important archaea). Previously, I led the development of PSORTb, which is currently the world's most precise predictor of bacterial protein subcellular localization. This program, coupled with a database my group developed of proteins of known and computationally-predicted subcellular localization, is now being used to accelerate genome-wide identification of proteins of particular medical, agricultural or environmental interest. However, there is currently no such computational predictor at this level of precision for the analysis of archaea and so I propose developing PSORTa for this purpose. In addition, while bacterial PSORTb precision/specificity is high, I hypothesize that the recall/sensitivity of this method could be improved though an expansion of the database of proteins of known localization associated with this program, and through the addition of a new computational analyses outlined in this proposal. I also propose that improved identification of sequences exposed on particular cell surface proteins is possible, using data generated from laboratory analysis of the structure of a reference cell-surface protein. The resulting expanded family of subcellular localization predictors, for both archaea (PSORTa) and bacteria (PSORTb), could be used to perform global analyses of protein localization across all prokaryotes, leading to new insights regarding the evolution of protein localization and cell networks across localizations. This improved PSORT tool could further accelerate the identification of prokaryotic proteins that are of interest for their medical, agricultural, environmental and industrial applications.