title: Inter-kingdom prediction certainty evaluation of protein subcellular localization tools: microbial pathogenesis approach for deciphering host microbe interaction.
abstract: Microbial pathogenesis involves several aspects of host-pathogen interactions, including microbial proteins targeting host subcellular compartments and subsequent effects on host physiology. Such studies are supported by experimental data, but recent detection of bacterial proteins localization through computational eukaryotic subcellular protein targeting prediction tools has also come into practice. We evaluated inter-kingdom prediction certainty of these tools. The bacterial proteins experimentally known to target host subcellular compartments were predicted with eukaryotic subcellular targeting prediction tools, and prediction certainty was assessed. The results indicate that these tools alone are not sufficient for inter-kingdom protein targeting prediction. The correct prediction of pathogen's protein subcellular targeting depends on several factors, including presence of localization signal, transmembrane domain and molecular weight, etc., in addition to approach for subcellular targeting prediction. The detection of protein targeting in endomembrane system is comparatively difficult, as the proteins in this location are channelized to different compartments. In addition, the high specificity of training data set also creates low inter-kingdom prediction accuracy. Current data can help to suggest strategy for correct prediction of bacterial protein's subcellular localization in host cell.
authors.forename: Abdul Arif
authors.initials: AA
authors.lastname: Khan
authors.forename: Zakir
authors.initials: Z
authors.lastname: Khan
authors.forename: Mohd Abul
authors.initials: MA
authors.lastname: Kalam
authors.forename: Azmat Ali
authors.initials: AA
authors.lastname: Khan
mesh_terms: D000818:Animals; D001419:Bacteria / Q000378:metabolism; D001426:Bacterial Proteins / Q000032:analysis* / Q000378:metabolism; D002462:Cell Membrane / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003593:Cytoplasm / Q000378:metabolism; D030562:Databases, Protein; D054884:Host-Pathogen Interactions; D006801:Humans; D008954:Models, Biological; D020543:Proteome / Q000032:analysis* / Q000378:metabolism; D012984:Software; D013347:Subcellular Fractions
chemical_list: D001426:Bacterial Proteins; D020543:Proteome
keywords: in silico; microbial pathogenesis; mitochondrial proteins; nuclear proteins; protein targeting