title: Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis.
abstract: A complete map of the Arabidopsis (Arabidopsis thaliana) proteome is clearly a major goal for the plant research community in terms of determining the function and regulation of each encoded protein. Developing genome-wide prediction tools such as for localizing gene products at the subcellular level will substantially advance Arabidopsis gene annotation. To this end, we performed a comprehensive study in Arabidopsis and created an integrative support vector machine-based localization predictor called AtSubP (for Arabidopsis subcellular localization predictor) that is based on the combinatorial presence of diverse protein features, such as its amino acid composition, sequence-order effects, terminal information, Position-Specific Scoring Matrix, and similarity search-based Position-Specific Iterated-Basic Local Alignment Search Tool information. When used to predict seven subcellular compartments through a 5-fold cross-validation test, our hybrid-based best classifier achieved an overall sensitivity of 91% with high-confidence precision and Matthews correlation coefficient values of 90.9% and 0.89, respectively. Benchmarking AtSubP on two independent data sets, one from Swiss-Prot and another containing green fluorescent protein- and mass spectrometry-determined proteins, showed a significant improvement in the prediction accuracy of species-specific AtSubP over some widely used "general" tools such as TargetP, LOCtree, PA-SUB, MultiLoc, WoLF PSORT, Plant-PLoc, and our newly created All-Plant method. Cross-comparison of AtSubP on six nontrained eukaryotic organisms (rice [Oryza sativa], soybean [Glycine max], human [Homo sapiens], yeast [Saccharomyces cerevisiae], fruit fly [Drosophila melanogaster], and worm [Caenorhabditis elegans]) revealed inferior predictions. AtSubP significantly outperformed all the prediction tools being currently used for Arabidopsis proteome annotation and, therefore, may serve as a better complement for the plant research community. A supplemental Web site that hosts all the training/testing data sets and whole proteome predictions is available at http://bioinfo3.noble.org/AtSubP/.
authors.affiliation: Plant Biology Division, Samuel Roberts Noble Foundation, Ardmore, Oklahoma 73401, USA.
authors.forename: Rakesh
authors.initials: R
authors.lastname: Kaundal
authors.forename: Reena
authors.initials: R
authors.lastname: Saini
authors.forename: Patrick X
authors.initials: PX
authors.lastname: Zhao
mesh_terms: D000596:Amino Acids / Q000378:metabolism; D017360:Arabidopsis / Q000378:metabolism*; D029681:Arabidopsis Proteins / Q000737:chemistry / Q000378:metabolism*; D001185:Artificial Intelligence; D030562:Databases, Protein; D058977:Molecular Sequence Annotation; D021382:Protein Sorting Signals; D020543:Proteome / Q000378:metabolism; D012372:ROC Curve; D017386:Sequence Homology, Amino Acid; D013045:Species Specificity; D013347:Subcellular Fractions / Q000378:metabolism
chemical_list: D000596:Amino Acids; D029681:Arabidopsis Proteins; D021382:Protein Sorting Signals; D020543:Proteome