Precise control of water stress in the field reveals different response thresholds for forage yield and digestibility of maize hybrids.

Introduction: With dwindling global freshwater supplies and increasing water stress, agriculture is coming under increasing pressure to reduce water use. Plant breeding requires high analytical capabilities. For this reason, near-infrared spectroscopy (NIRS) has been used to develop prediction equations for whole-plant samples, particularly for predicting dry matter digestibility, which has a major impact on the energy value of forage maize hybrids and is required for inclusion in the official French catalogue. Although the historical NIRS equations have long been used routinely in seed company breeding programmes, they do not predict all variables with the same accuracy. In addition, little is known about how accurate their predictions are under different water stress-environments. Methods: Here, we examined the effects of water stress and stress intensity on agronomic, biochemical, and NIRS predictive values in a set of 13 modern S0-S1 forage maize hybrids under four different environmental conditions resulting from the combination of a northern and southern location and two monitored water stress levels in the south. Results: First, we compared the reliability of NIRS predictions for basic forage quality traits obtained using the historical NIRS predictive equations and the new equations we recently developed. We found that NIRS predicted values were affected to varying degrees by environmental conditions. We also showed that forage yield gradually decreased as a function of water stress, whereas both dry matter and cell wall digestibilities increased regardless of the intensity of water stress, with variability among the tested varieties decreasing under the most stressed conditions. Discussion: By combining forage yield and dry matter digestibility, we were able to quantify digestible yield and identify varieties with different strategies for coping with water stress, raising the exciting possibility that important potential selection targets still exist. Finally, from a farmer's perspective, we were able to show that late silage harvest has no effect on dry matter digestibility and that moderate water stress does not necessarily result in a loss of digestible yield.

PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

MOTIVATION: PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. RESULTS: We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. AVAILABILITY: http://www.psort.org/psortb (download open source software or use the web interface). CONTACT: psort-mail@sfu.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Photochemistry of peroxoborates: borate inhibition of the photodecomposition of hydrogen peroxide.

The UV absorbance and photochemical decomposition kinetics of hydrogen peroxide in borate/boric acid buffers were investigated as a function of pH, total peroxide concentration, and total boron concentration. At higher pH borate/boric acid inhibits the photodecomposition of hydrogen peroxide (molar absorptivity and quantum yield of H(2)O(2) and HO(2) (-), (19.0+/-0.3) M(-1) cm(-1) and 1, and (237+/-7) M(-1) cm(-1) and 0.8+/-0.1, respectively). The results are consistent with the equilibrium formation of the anions monoperoxoborate, K(BOOH)=[H(+)][HOOB(OH)(3) (-)]/([B(OH)(3)][H(2)O(2)]), 2.0 x 10(-8), R. Pizer, C. Tihal, Inorg. Chem. 1987, 26, 3639-3642, and monoperoxodiborate, K(BOOB)=[BOOB(2-)]/([B(OH)(4) (-)][HOOB(OH)(3) (-)]), 1.0+/-0.3 or 4.3+/-0.9, depending upon the conditions, with molar absorptivity, (19+/-1) M(-1) cm(-1) and (86+/-15) M(-1) cm(-1), respectively, and respective quantum yields, 1.1+/-0.1 and 0.04+/-0.04. The low quantum yield of monoperoxodiborate is discussed in terms of the slower diffusion apart of incipient (.)OB(OH)(3) (-) radicals than may be possible for (.)OH radicals, or a possible oxygen-bridged cyclic structure of the monoperoxodiborate.

Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria.

BACKGROUND: Identification of a bacterial protein's subcellular localization (SCL) is important for genome annotation, function prediction and drug or vaccine target identification. Subcellular fractionation techniques combined with recent proteomics technology permits the identification of large numbers of proteins from distinct bacterial compartments. However, the fractionation of a complex structure like the cell into several subcellular compartments is not a trivial task. Contamination from other compartments may occur, and some proteins may reside in multiple localizations. New computational methods have been reported over the past few years that now permit much more accurate, genome-wide analysis of the SCL of protein sequences deduced from genomes. There is a need to compare such computational methods with laboratory proteomics approaches to identify the most effective current approach for genome-wide localization characterization and annotation. RESULTS: In this study, ten subcellular proteome analyses of bacterial compartments were reviewed. PSORTb version 2.0 was used to computationally predict the localization of proteins reported in these publications, and these computational predictions were then compared to the localizations determined by the proteomics study. By using a combined approach, we were able to identify a number of contaminants and proteins with dual localizations, and were able to more accurately identify membrane subproteomes. Our results allowed us to estimate the precision level of laboratory subproteome studies and we show here that, on average, recent high-precision computational methods such as PSORTb now have a lower error rate than laboratory methods. CONCLUSION: We have performed the first focused comparison of genome-wide proteomic and computational methods for subcellular localization identification, and show that computational methods have now attained a level of precision that is exceeding that of high-throughput laboratory approaches. We note that analysis of all cellular fractions collectively is required to effectively provide localization information from laboratory studies, and we propose an overall approach to genome-wide subcellular localization characterization that capitalizes on the complementary nature of current laboratory and computational methods.

PSORTdb: a protein subcellular localization database for bacteria.

Information about bacterial subcellular localization (SCL) is important for protein function prediction and identification of suitable drug/vaccine/diagnostic targets. PSORTdb (http://db.psort.org/) is a web-accessible database of SCL for bacteria that contains both information determined through laboratory experimentation and computational predictions. The dataset of experimentally verified information (approximately 2000 proteins) was manually curated by us and represents the largest dataset of its kind. Earlier versions have been used for training SCL predictors, and its incorporation now into this new PSORTdb resource, with its associated additional annotation information and dataset version control, should aid researchers in future development of improved SCL predictors. The second component of this database contains computational analyses of proteins deduced from the most recent NCBI dataset of completely sequenced genomes. Analyses are currently calculated using PSORTb, the most precise automated SCL predictor for bacterial proteins. Both datasets can be accessed through the web using a very flexible text search engine, a data browser, or using BLAST, and the entire database or search results may be downloaded in various formats. Features such as GO ontologies and multiple accession numbers are incorporated to facilitate integration with other bioinformatics resources. PSORTdb is freely available under GNU General Public License.

Quantitative structure-permeation relationships for solute transport across silicone membranes.

PURPOSE: The purpose of this work was to assess the molecular properties that influence solute permeation across siliconemembranes and to compare the results with transport across human skin. METHODS: The permeability coefficients (log Kp) of a series of model solutes across silicone membranes were determined from the analysis of simple transport experiments using a pseudosteady-state mathematical model of the diffusion process. Subsequently, structure permeation relationships were constructed and examined, focusing in particular on the difference between solute octanol/water and 1,2 dichloroethane/water partition coefficients (deltalog P(oct-dce)), which re ported upon H-bond donor activity, and the computationally derived molecular hydrogen-bonding potential. RESULTS: The hydrogen-bond donor acidity and the lipophilicity of the compounds examined greatly influenced their permeation across sil cone membranes. Furthermore, for a limited dataset, a significant correlation was identified between solute permeation across silicone membranes and that through human epidermis. CONCLUSION: The key molecular properties that control solute perme ation across silicone membranes have been identified. For the set of substituted phenols and other unrelated compounds examined here a similar structure-permeation relationship has been derived for their transport through human epidermis, suggesting application of the results to the prediction of flux across biological barriers.