Ca ²⁺/Cation Antiporters (CaCA): Identification, Characterization and Expression Profiling in Bread Wheat ( Triticum aestivum L.)

SMART (Simple Modular Architecture Research Tool) is a web resource (http://smart.embl.de/) providing simple identification and extensive annotation of protein domains and the exploration of protein domain architectures. In the current version, SMART contains manually curated models for more than 1200 protein domains, with ∼200 new models since our last update article. The underlying protein databases were synchronized with UniProt, Ensembl and STRING, bringing the total number of annotated domains and other protein features above 100 million. SMART's ‘Genomic’ mode, which annotates proteins from completely sequenced genomes was greatly expanded and now includes 2031 species, compared to 1133 in the previous release. SMART analysis results pages have been completely redesigned and include links to several new information sources. A new, vector-based display engine has been developed for protein schematics in SMART, which can also be exported as high-resolution bitmap images for easy inclusion into other documents. Taxonomic tree displays in SMART have been significantly improved, and can be easily navigated using the integrated search engine. Show more

Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization. Show more

The appearance of photosynthetic eukaryotes (algae and plants) dramatically altered the Earth's ecosystem, making possible all vertebrate life on land, including humans. Dating algal origin is, however, frustrated by a meager fossil record. We generated a plastid multi-gene phylogeny with Bayesian inference and then used maximum likelihood molecular clock methods to estimate algal divergence times. The plastid tree was used as a surrogate for algal host evolution because of recent phylogenetic evidence supporting the vertical ancestry of the plastid in the red, green, and glaucophyte algae. Nodes in the plastid tree were constrained with six reliable fossil dates and a maximum age of 3,500 MYA based on the earliest known eubacterial fossil. Our analyses support an ancient (late Paleoproterozoic) origin of photosynthetic eukaryotes with the primary endosymbiosis that gave rise to the first alga having occurred after the split of the Plantae (i.e., red, green, and glaucophyte algae plus land plants) from the opisthokonts sometime before 1,558 MYA. The split of the red and green algae is calculated to have occurred about 1,500 MYA, and the putative single red algal secondary endosymbiosis that gave rise to the plastid in the cryptophyte, haptophyte, and stramenopile algae (chromists) occurred about 1,300 MYA. These dates, which are consistent with fossil evidence for putative marine algae (i.e., acritarchs) from the early Mesoproterozoic (1,500 MYA) and with a major eukaryotic diversification in the very late Mesoproterozoic and Neoproterozoic, provide a molecular timeline for understanding algal evolution. Show more