A comparison of extreme European daily precipitation simulated by a global and a regional climate model for present and future climates

The intensity and distribution of daily precipitation is predicted to change under scenarios of increased greenhouse gases (GHGs). In this paper, we analyse the ability of HadCM2, a general circulation model (GCM), and a high-resolution regional climate model (RCM), both developed at the Met Office's Hadley Centre, to simulate extreme daily precipitation by reference to observations. A detailed analysis of daily precipitation is made at two UK grid boxes, where probabilities of reaching daily thresholds in the GCM and RCM are compared with observations. We find that the RCM generally overpredicts probabilities of extreme daily precipitation but that, when the GCM and RCM simulated values are scaled to have the same mean as the observations, the RCM captures the upper-tail distribution more realistically. To compare regional changes in daily precipitation in the GHG-forced period 2080-2100 in the GCM and the RCM, we develop two methods. The first considers the fractional changes in probability of local daily precipitation reaching or exceeding a fixed 15 mm threshold in the anomaly climate compared with the control. The second method uses the upper one-percentile of the control at each point as the threshold. Agreement between the models is better in both seasons with the latter method, which we suggest may be more useful when considering larger scale spatial changes. On average, the probability of precipitation exceeding the 1% threshold increases by a factor of 2.5 (GCM and RCM) in winter and by I .7 (GCM) or 1.3 (RCM) in summer.

Comparison of methods for analysis of proteolysis by plasmin in milk

Sensitive methods that are currently used to monitor proteolysis by plasmin in milk are limited due to 7 their high cost and lack of standardisation for quality assurance in the various dairy laboratories. In 8 this study, four methods, trinitrobenzene sulphonic acid (TNBS), reverse phase high pressure liquid 9 chromatography (RP-HPLC), gel electrophoresis and fluorescamine, were selected to assess their 10 suitability for the detection of proteolysis in milk by plasmin. Commercial UHT milk was incubated 11 with plasmin at 37 °C for one week. Clarification was achieved by isoelectric precipitation (pH 4·6 12 soluble extracts)or 6% (final concentration) trichloroacetic acid (TCA). The pH 4·6 and 6% TCA 13 soluble extracts of milk showed high correlations (R2 > 0·93) by the TNBS, fluorescamine and 14 RP-HPLC methods, confirming increased proteolysis during storage. For gel electrophoresis,15 extensive proteolysis was confirmed by the disappearance of α- and β-casein bands on the seventh 16 day, which was more evident in the highest plasmin concentration. This was accompanied by the 17 appearance of α- and β-casein proteolysis products with higher intensities than on previous days, 18 implying that more products had been formed as a result of casein breakdown. The fluorescamine 19 method had a lower detection limit compared with the other methods, whereas gel electrophoresis 20 was the best qualitative method for monitoring β-casein proteolysis products. Although HPLC was the 21 most sensitive, the TNBS method is recommended for use in routine laboratory analysis on the basis 22 of its accuracy, reliability and simplicity.

Molecular modelling studies of binding of DACD derivatives into G-Quadruplex DNA: comparison of force field and quantum polarized ligand docking methods

The DNA G-qadruplexes are one of the targets being actively explored for anti-cancer therapy by inhibiting them through small molecules. This computational study was conducted to predict the binding strengths and orientations of a set of novel dimethyl-amino-ethyl-acridine (DACA) analogues that are designed and synthesized in our laboratory, but did not diffract in Synchrotron light.Thecrystal structure of DNA G-Quadruplex(TGGGGT)4(PDB: 1O0K) was used as target for their binding properties in our studies.We used both the force field (FF) and QM/MM derived atomic charge schemes simultaneously for comparing the predictions of drug binding modes and their energetics. This study evaluates the comparative performance of fixed point charge based Glide XP docking and the quantum polarized ligand docking schemes. These results will provide insights on the effects of including or ignoring the drug-receptor interfacial polarization events in molecular docking simulations, which in turn, will aid the rational selection of computational methods at different levels of theory in future drug design programs. Plenty of molecular modelling tools and methods currently exist for modelling drug-receptor or protein-protein, or DNA-protein interactionssat different levels of complexities.Yet, the capasity of such tools to describevarious physico-chemical propertiesmore accuratelyis the next step ahead in currentresearch.Especially, the usage of most accurate methods in quantum mechanics(QM) is severely restricted by theirtedious nature. Though the usage of massively parallel super computing environments resulted in a tremendous improvement in molecular mechanics (MM) calculations like molecular dynamics,they are still capable of dealing with only a couple of tens to hundreds of atoms for QM methods. One such efficient strategy that utilizes thepowers of both MM and QM are the QM/MM hybrid methods. Lately, attempts have been directed towards the goal of deploying several different QM methods for betterment of force field based simulations, but with practical restrictions in place. One of such methods utilizes the inclusion of charge polarization events at the drug-receptor interface, that is not explicitly present in the MM FF.

Rapid protein domain assignment from amino acid sequence using predicted secondary structure

The elucidation of the domain content of a given protein sequence in the absence of determined structure or significant sequence homology to known domains is an important problem in structural biology. Here we address how successfully the delineation of continuous domains can be accomplished in the absence of sequence homology using simple baseline methods, an existing prediction algorithm (Domain Guess by Size), and a newly developed method (DomSSEA). The study was undertaken with a view to measuring the usefulness of these prediction methods in terms of their application to fully automatic domain assignment. Thus, the sensitivity of each domain assignment method was measured by calculating the number of correctly assigned top scoring predictions. We have implemented a new continuous domain identification method using the alignment of predicted secondary structures of target sequences against observed secondary structures of chains with known domain boundaries as assigned by Class Architecture Topology Homology (CATH). Taking top predictions only, the success rate of the method in correctly assigning domain number to the representative chain set is 73.3%. The top prediction for domain number and location of domain boundaries was correct for 24% of the multidomain set (±20 residues). These results have been put into context in relation to the results obtained from the other prediction methods assessed

What are the baselines for protein fold recognition?

What constitutes a baseline level of success for protein fold recognition methods? As fold recognition benchmarks are often presented without any thought to the results that might be expected from a purely random set of predictions, an analysis of fold recognition baselines is long overdue. Given varying amounts of basic information about a protein—ranging from the length of the sequence to a knowledge of its secondary structure—to what extent can the fold be determined by intelligent guesswork? Can simple methods that make use of secondary structure information assign folds more accurately than purely random methods and could these methods be used to construct viable hierarchical classifications?

The PSIPRED protein structure prediction server

The PSIPRED protein structure prediction server allows users to submit a protein sequence, perform a prediction of their choice and receive the results of the prediction both textually via e-mail and graphically via the web. The user may select one of three prediction methods to apply to their sequence: PSIPRED, a highly accurate secondary structure prediction method; MEMSAT 2, a new version of a widely used transmembrane topology prediction method; or GenTHREADER, a sequence profile based fold recognition method.

Staphylococcal extracellular adherence protein induces platelet activation by stimulation of thiol isomerases

OBJECTIVE: Staphylococcus aureus can induce platelet aggregation. The rapidity and degree of this correlates with the severity of disseminated intravascular coagulation, and depends on platelet peptidoglycans. Surface-located thiol isomerases play an important role in platelet activation. The staphylococcal extracellular adherence protein (Eap) functions as an adhesin for host plasma proteins. Therefore we tested the effect of Eap on platelets. METHODS AND RESULTS: We found a strong stimulation of the platelet-surface thiol isomerases protein disulfide isomerase, endoplasmic reticulum stress proteins 57 and 72 by Eap. Eap induced thiol isomerase-dependent glycoprotein IIb/IIIa activation, granule secretion, and platelet aggregation. Treatment of platelets with thiol blockers, bacitracin, and anti-protein disulfide isomerase antibody inhibited Eap-induced platelet activation. The effect of Eap on platelets and protein disulfide isomerase activity was completely blocked by glycosaminoglycans. Inhibition by the hydrophobic probe bis(1-anilinonaphthalene 8-sulfonate) suggested the involvement of hydrophobic sites in protein disulfide isomerase and platelet activation by Eap. CONCLUSIONS: In the present study, we found an additional and yet unknown mechanism of platelet activation by a bacterial adhesin, involving stimulation of thiol isomerases. The thiol isomerase stimulatory and prothrombotic features of a microbial secreted protein are probably not restricted to S aureus and Eap. Because many microorganisms are coated with amyloidogenic proteins, it is likely that the observed mechanism is a more general one.

Improvement of 3D protein models using multiple templates guided by single-template model quality assessment

Motivation: Modelling the 3D structures of proteins can often be enhanced if more than one fold template is used during the modelling process. However, in many cases, this may also result in poorer model quality for a given target or alignment method. There is a need for modelling protocols that can both consistently and significantly improve 3D models and provide an indication of when models might not benefit from the use of multiple target-template alignments. Here, we investigate the use of both global and local model quality prediction scores produced by ModFOLDclust2, to improve the selection of target-template alignments for the construction of multiple-template models. Additionally, we evaluate clustering the resulting population of multi- and single-template models for the improvement of our IntFOLD-TS tertiary structure prediction method. Results: We find that using accurate local model quality scores to guide alignment selection is the most consistent way to significantly improve models for each of the sequence to structure alignment methods tested. In addition, using accurate global model quality for re-ranking alignments, prior to selection, further improves the majority of multi-template modelling methods tested. Furthermore, subsequent clustering of the resulting population of multiple-template models significantly improves the quality of selected models compared with the previous version of our tertiary structure prediction method, IntFOLD-TS.

Quantitative precipitation estimation over ocean using Bayesian approach from microwave observations during the typhoon season

We have developed a new Bayesian approach to retrieve oceanic rain rate from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), with an emphasis on typhoon cases in the West Pacific. Retrieved rain rates are validated with measurements of rain gauges located on Japanese islands. To demonstrate improvement, retrievals are also compared with those from the TRMM/Precipitation Radar (PR), the Goddard Profiling Algorithm (GPROF), and a multi-channel linear regression statistical method (MLRS). We have found that qualitatively, all methods retrieved similar horizontal distributions in terms of locations of eyes and rain bands of typhoons. Quantitatively, our new Bayesian retrievals have the best linearity and the smallest root mean square (RMS) error against rain gauge data for 16 typhoon overpasses in 2004. The correlation coefficient and RMS of our retrievals are 0.95 and ~2 mm hr-1, respectively. In particular, at heavy rain rates, our Bayesian retrievals outperform those retrieved from GPROF and MLRS. Overall, the new Bayesian approach accurately retrieves surface rain rate for typhoon cases. Accurate rain rate estimates from this method can be assimilated in models to improve forecast and prevent potential damages in Taiwan during typhoon seasons.

Periplasmic adaptor protein AcrA has a distinct role in the antibiotic resistance and virulence of Salmonella enterica serovar Typhimurium

Objectives: AcrA can function as the periplasmic adaptor protein (PAP) in several RND tripartite efflux pumps, of which AcrAB-TolC is considered the most important. This system confers innate multiple antibiotic resistance. Disruption of acrB or tolC impairs the ability of Salmonella Typhimurium to colonize and persist in the host. The aim of this study was to investigate the role of AcrA alone in multidrug resistance and pathogenicity. Methods: The acrA gene was inactivated in Salmonella Typhimurium SL1344 by insertion of the aph gene and this mutant complemented with pWKS30acrA. The antimicrobial susceptibility of the mutant to six antibiotics as well as various dyes and detergents was determined. In addition, efflux activity was quantified. The ability of the mutant to adhere to, and invade, tissue culture cells in vitro was measured. Results: Following disruption of acrA, RT-PCR and western blotting confirmed that acrB/AcrB was still expressed when acrA was disrupted. The acrA mutant was hypersusceptible to antibiotics, dyes and detergents. In some cases, lower MICs were seen than for the acrB or tolC mutants. Efflux of the fluorescent dye Hoechst H33342 was less than in wild-type following disruption of acrA. acrA was also required for adherence to, and invasion of, tissue culture cells. Conclusions: Inactivation of acrA conferred a phenotype distinct to that of acrB::aph and tolC::aph. These data indicate a role for AcrA distinct to that of other protein partners in both efflux of substrates and virulence.

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

Predicting protein structures and structural annotation of proteomes

Protein structure prediction methods aim to predict the structures of proteins from their amino acid sequences, utilizing various computational algorithms. Structural genome annotation is the process of attaching biological information to every protein encoded within a genome via the production of three-dimensional protein models.

Use of miniaturized protein arrays for Escherichia coli O serotyping

Serological typing of Escherichia coli O antigens is a well-established method used for differentiation and identification of O serotypes commonly associated with disease. In this feasibility study, we have developed a novel somatic antibody-based miniaturized microarray chip, using 17 antisera, which can be used to detect bound whole-cell E. coli antigen with its corresponding immobilized antibody, to assess the feasibility of this approach. The chip was tested using the related 17 control strains, and the O types found by the microarray chip showed 100% correlation with the O types found by conventional typing. A blind trial was performed in which 100 E. coli isolates that had been O serotyped previously by the conventional assay were tested by the array approach. Overall, the O serotypes of 88% of isolates were correctly identified by the microarray method. For several isolates, ambiguity of O-type designation by microarray arose due to increased sensitivity of this method, allowing signal intensities of cross-reactions to be quantified. Investigation of discrepancies between conventional and microarray O serotyping indicated that some isolates upon storage had become untypeable and, therefore, gave poor signal intensity when tested by the microarray or retested by conventional means. For all 20 serotype O26 and O157 isolates, the apparent discrepancy in O serotyping was analyzed further by a third independent test, which confirmed the microarray results. Therefore, the use of miniaturized protein arrays increases the speed and efficiency of O serotyping in a cost-effective manner, and these preliminary findings suggest the microarray approach may have a higher accuracy than those of traditional O-serotyping methods.

Towards data warehousing and mining of protein unfolding simulation data

OBJECTIVES: The prediction of protein structure and the precise understanding of protein folding and unfolding processes remains one of the greatest challenges in structural biology and bioinformatics. Computer simulations based on molecular dynamics (MD) are at the forefront of the effort to gain a deeper understanding of these complex processes. Currently, these MD simulations are usually on the order of tens of nanoseconds, generate a large amount of conformational data and are computationally expensive. More and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. METHODS: To adequately organize, manage, and analyze the data generated by unfolding simulation studies, we designed a data warehouse system that is embedded in a grid environment to facilitate the seamless sharing of available computer resources and thus enable many groups to share complex molecular dynamics simulations on a more regular basis. RESULTS: To gain insight into the conformational fluctuations and stability of the monomeric forms of the amyloidogenic protein transthyretin (TTR), molecular dynamics unfolding simulations of the monomer of human TTR have been conducted. Trajectory data and meta-data of the wild-type (WT) protein and the highly amyloidogenic variant L55P-TTR represent the test case for the data warehouse. CONCLUSIONS: Web and grid services, especially pre-defined data mining services that can run on or 'near' the data repository of the data warehouse, are likely to play a pivotal role in the analysis of molecular dynamics unfolding data.

Conditioning model output statistics of regional climate model precipitation on circulation patterns

Dynamical downscaling of Global Climate Models (GCMs) through regional climate models (RCMs) potentially improves the usability of the output for hydrological impact studies. However, a further downscaling or interpolation of precipitation from RCMs is often needed to match the precipitation characteristics at the local scale. This study analysed three Model Output Statistics (MOS) techniques to adjust RCM precipitation; (1) a simple direct method (DM), (2) quantile-quantile mapping (QM) and (3) a distribution-based scaling (DBS) approach. The modelled precipitation was daily means from 16 RCMs driven by ERA40 reanalysis data over the 1961–2000 provided by the ENSEMBLES (ENSEMBLE-based Predictions of Climate Changes and their Impacts) project over a small catchment located in the Midlands, UK. All methods were conditioned on the entire time series, separate months and using an objective classification of Lamb's weather types. The performance of the MOS techniques were assessed regarding temporal and spatial characteristics of the precipitation fields, as well as modelled runoff using the HBV rainfall-runoff model. The results indicate that the DBS conditioned on classification patterns performed better than the other methods, however an ensemble approach in terms of both climate models and downscaling methods is recommended to account for uncertainties in the MOS methods.