Host Plant Compatibility Shapes the Proteogenome of Frankia coriariae

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.

Plants associate with a wide range of microorganisms, with both detrimental and beneficial outcomes. Central to plant survival is the ability to recognize invading microorganisms and either limit their intrusion, in the case of pathogens, or promote the association, in the case of symbionts. To aid in this recognition process, elaborate communication and counter-communication systems have been established that determine the degree of ingress of the microorganism into the host plant. In this Review, I describe the common signalling processes used by plants during mutualistic interactions with microorganisms as diverse as arbuscular mycorrhizal fungi and rhizobial bacteria.

Symbiotic nitrogen fixation by rhizobia in legume root nodules injects approximately 40 million tonnes of nitrogen into agricultural systems each year. In exchange for reduced nitrogen from the bacteria, the plant provides rhizobia with reduced carbon and all the essential nutrients required for bacterial metabolism. Symbiotic nitrogen fixation requires exquisite integration of plant and bacterial metabolism. Central to this integration are transporters of both the plant and the rhizobia, which transfer elements and compounds across various plant membranes and the two bacterial membranes. Here we review current knowledge of legume and rhizobial transport and metabolism as they relate to symbiotic nitrogen fixation. Although all legume-rhizobia symbioses have many metabolic features in common, there are also interesting differences between them, which show that evolution has solved metabolic problems in different ways to achieve effective symbiosis in different systems.