Using species‐environmental amplitudes to predict pH values from vegetation

Question: Species optima or indicator values are frequently used to predict environmental variables from species composition. The present study focuses on the question whether predictions can be improved by using species environmental amplitudes instead of single values representing species optima. Location: Semi‐natural, deciduous hardwood forests of northwestern Germany. Methods: Based on a data set of 558 relevés, species responses (presence/absence) to pH were modelled with Huisman‐Olff‐Fresco (HOF) regression models. Species amplitudes were derived from response curves using three different methods. To predict the pH from vegetation, a maximum amplitude overlap method was applied. For comparison, predictions resulting from several established methods, i. e. maximum likelihood/present and absent species, maximum likelihood/present species only, mean weighted averages and mean Ellenberg indicator values were calculated. The predictive success (squared Pearson's r and root mean square error of prediction) was evaluated using an independent data set of 151 relevés. Results: Predictions based upon amplitudes defined by maximum Cohen's x probability threshold yield the best results of all amplitude definitions (R²= 0.75, RMSEP = 0.52). Provided there is an even distribution of the environmental variable, amplitudes defined by predicted probability exceeding prevalence are also suitable (R²= 0.76, RMSEP = 0.55). The prediction success is comparable to maximum likelihood (present species only) and – after rescaling – to mean weighted averages. Predicted values show a good linearity to observed pH values as opposed to a curvilinear relationship of mean Ellenberg indicator values. Transformation or rescaling of the predicted values is not required. Conclusions: Species amplitudes given by a minimum and maximum boundary for each species can be used to efficiently predict environmental variables from species composition. The predictive success is superior to mean Ellenberg indicator values and comparable to mean indicator values based on species weighted averages.     

Prediction of Eyespot Severity on Winter Wheat or Winter Barley Inoculated with W-type or R-type Isolates of Pseudocercosporella herpotrichoides

In crops of winter wheat (1986—88) or winter barley (1987—88) inoculated with W-type or R-type isolates of Pseudocercosporella herpotrichoides and sown on different dates (1986) or at different seed rates (1987,1988) eyespot epidemics developed in different ways. Methods of measuring eyespot incidence/severity during crop growth were compared for their ability to predict eyespot severity at grain filling. Regressions were calculated for eyespot severity score at GS 71 on earlier measurements, either at GS 30/31 (11 methods) or from GS 22 to GS 65 (3 methods). Based on measurements at GS 30/31, all the methods predicted eyespot severity at GS 71 well in plots of winter barley inoculated with W-type isolates (r, 0.83—0.97) but the accuracy of predictions in plots inoculated with R-type isolates was very variable (r, 0.09—0.71). Predictions for 1987 and 1988 were less accurate in wheat than in W-type plots of barley, but did not differ between W-type and R-type plots (r, 0.70—0.89). When the wheat data for 1986 were also included predictions were less accurate, especially in R-type plots (r, 0—0.59). Generally, it was easier to predict eyespot severity at GS 71 in W-type than in R-type plots, especially in barley and in wheat before GS 37/39. Predictions of eyespot severity at GS 71 based on measurements before GS 25 were inaccurate for both wheat and barley. After GS 25 the accuracy of the prediction was generally good in W-type plots and did not improve greatly except in wheat after GS 59. However, there was a steady improvement in the accuracy of the prediction in R-type plots of barley from GS 24 to GS 53. Assessments of eyespot incidence on stems predicted eyespot severity at GS 71 more accurately than assessments on leaf sheaths on wheat after GS 37/39, but were not as good on barley until GS 53.     

Assessing the skill of yes/no predictions

Summary Should healthy, middle‐aged women receive precautionary mammograms? Should trauma surgeons use the popular TRISS score to predict the likelihood of patient survival? These are examples of questions confronting us when we decide whether to use a yes/no prediction. In order to trust a prediction we must show that it is more valuable than would be our best guess of the future in the absence of the prediction. Calculating value means identifying our loss should the prediction err and examining the past performance of the prediction with respect to that loss. A statistical test to do this is developed. Predictions that pass this test are said to have skill. Only skillful predictions should be used. Graphical and numerical methods to identify skill will be demonstrated. The usefulness of mammograms is explored.     

Introducing “best single template” models as reference baseline for the Continuous Automated Model Evaluation (CAMEO)

Critical blind assessment of structure prediction techniques is crucial for the scientific community to establish the state of the art, identify bottlenecks, and guide future developments. In Critical Assessment of Techniques in Structure Prediction (CASP), human experts assess the performance of participating methods in relation to the difficulty of the prediction task in a biennial experiment on approximately 100 targets. Yet, the development of automated computational modeling methods requires more frequent evaluation cycles and larger sets of data. The “Continuous Automated Model EvaluatiOn (CAMEO)” platform complements CASP by conducting fully automated blind prediction evaluations based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the Protein Data Bank (PDB). Each week, CAMEO publishes benchmarking results for predictions corresponding to a set of about 20 targets collected during a 4-day prediction window. CAMEO benchmarking data are generated consistently for all methods at the same point in time, enabling developers to cross-validate their method's performance, and referring to their results in publications. Many successful participants of CASP have used CAMEO—either by directly benchmarking their methods within the system or by comparing their own performance to CAMEO reference data. CAMEO offers a variety of scores reflecting different aspects of structure modeling, for example, binding site accuracy, homo-oligomer interface quality, or accuracy of local model confidence estimates. By introducing the "bestSingleTemplate" method based on structure superpositions as a reference for the accuracy of 3D modeling predictions, CAMEO facilitates objective comparison of techniques and fosters the development of advanced methods.     

HIERARCHICAL SELECTION THEORY AND SEX RATIOS. II. ON APPLYING THE THEORY,AND A TEST WITH FIG WASPS

Predictions from the theory of sex ratios in subdivided populations are tested by studying fig wasps (Agaonidae). Observations strongly support the qualitative prediction that fig wasp sex ratios (males/total) decrease with increasing amounts of both inbreeding and competition among male relatives for access to mates (local mate competition). However, the observed sex ratio is consistently lower than predicted by previous quantitative models. Many assumptions underlying these models are unrealistic. Each unrealistic assumption is discussed as it applies to fig wasps, and where appropriate, new quantitative predictions are derived based on more realistic assumptions. New predictions are compared to the data in an a posteriori fashion and are found to be much closer to the observations than previous models from the literature, but further work will be required before a close match between theory and observation can be claimed.     

Predicting the future of species diversity: macroecological theory, climate change, and direct tests of alternative forecasting methods

Accurate predictions of future shifts in species diversity in response to global change are critical if useful conservation strategies are to be developed. The most widely used prediction method is to model individual species niches from point observations and project these models forward using future climate scenarios. The resulting changes in individual ranges are then summed to predict diversity changes; multiple models can be combined to produce ensemble forecasts. Predictions based on environment-richness regressions are rarer. However, richness regression models, based on macroecological diversity theory, have a long track record of making reliable spatial predictions of diversity patterns. If these empirical theories capture true functional relationships between environment and diversity, then they should make consistent predictions through time as well as space and could complement individual species-based predictions. Here, we use climate change throughout the 20th century to directly test the ability of these different approaches to predict shifts of Canadian butterfly diversity. We found that all approaches performed reasonably well, but the most accurate predictions were made using the single best richness-environment regression model, after accounting for the effects of spatial autocorrelation. Spatially trained regression models based on macroecological theory accurately predict diversity shifts for large species assemblages. Global changes provide pseudo-experimental tests of those macroecological theories that can then generate robust predictions of future conditions.     

Systematic interpolation method predicts protein chromatographic elution with salt gradients,pH gradients and combined salt/pH gradients

A methodology is presented to predict protein elution behavior from an ion exchange column using both individual or combined pH and salt gradients based on high‐throughput batch isotherm data. The buffer compositions are first optimized to generate linear pH gradients from pH 5.5 to 7 with defined concentrations of sodium chloride. Next, high‐throughput batch isotherm data are collected for a monoclonal antibody on the cation exchange resin POROS XS over a range of protein concentrations, salt concentrations, and solution pH. Finally, a previously developed empirical interpolation (EI) method is extended to describe protein binding as a function of the protein and salt concentration and solution pH without using an explicit isotherm model. The interpolated isotherm data are then used with a lumped kinetic model to predict the protein elution behavior. Experimental results obtained for laboratory scale columns show excellent agreement with the predicted elution curves for both individual or combined pH and salt gradients at protein loads up to 45 mg/mL of column. Numerical studies show that the model predictions are robust as long as the isotherm data cover the range of mobile phase compositions where the protein actually elutes from the column.     

Rat small-intestinal β-galactosidases. Studies on the fractionation of `acid'' β-galactosidase with isoelectric focusing, gel filtration and ion-exchange chromatography

1. Different forms of the rat small-intestinal ;acid' beta-galactosidase were separated by using the isoelectric-focusing technique. The isoelectric points of the different forms were at pH4.2, 4.6, 5.4, 6.1 and approx. 8. 2. The two forms of ;acid' beta-galactosidase isoelectric at pH4.2 and 4.6 were completely excluded from the Sephadex G-200 gel, whereas the form isoelectric at pH8 had K(av.) 0.4. The concentration and pH of the elution buffer influenced the distribution of enzyme activity between different forms. Thus, under certain conditions of ionic strength and pH, the enzyme seems to form high-molecular-weight aggregates with low isoelectric points. These may be homopolymeric aggregates or the result of binding of enzyme to, for example, membrane fragments. The forms isoelectric at pH5.4 and 6.1 are probably aggregates of intermediate size. 3. During ion-exchange chromatography at pH6.0 one fraction of ;acid' beta-galactosidase was not retained on the column and was isoelectric at pH8 and another fraction was eluted when the buffer concentration in the eluate had increased to about 50mm. The main part of enzyme eluted in this second fraction was also isoelectric at pH8, indicating that the elution of this fraction is not a simple ion-exchange procedure but probably also involves a splitting of high-molecular-weight aggregates, originally retained because of their low isoelectric points. The enzyme subunits have a higher isoelectric point, and are therefore no longer bound to the ion-exchange resin.     

The 2002 Olympic Games of protein structure prediction

The summer of every even year is considered by the protein structure prediction community as the Olympic Games season, because in addition to a number of continuous benchmarking experiments such as LiveBench, much effort is invested in the blind prediction experiments CASP and CAFASP. Here we report the major advances registered in the field since the last Games of 2000, as measured by the recently completed LiveBench-4 experiment. These results provide a timely measure of the capabilities of current methods and of their expected performance in the upcoming CASP-5 and CAFASP-3 experiments. We also describe the initiation of the two new, community-wide experiments, PDB-CAFASP and MR-CAFASP. These new experiments extend the scope of previous efforts and may have important implications for structural genomics.     

Prediction and analysis of protein methylarginine and methyllysine based on Multisequence features

Protein methylation, one of the most important post-translational modifications, typically takes place on arginine or lysine residue. The reversible modification involves a series of basic cellular processes. Identification of methyl proteins with their sites will facilitate the understanding of the molecular mechanism of methylation. Besides the experimental methods, computational predictions of methylated sites are much more desirable for their convenience and fast speed. Here, we propose a method dedicated to predicting methylated sites of proteins. Feature selection was made on sequence conservation, physicochemical/biochemical properties, and structural disorder by applying maximum relevance minimum redundancy and incremental feature selection methods. The prediction models were built according to nearest the neighbor algorithm and evaluated by the jackknife cross-validation. We built 11 and 9 predictors for methylarginine and methyllysine, respectively, and integrated them to predict methylated sites. As a result, the average prediction accuracies are 74.25%, 77.02% for methylarginine and methyllysine training sets, respectively. Feature analysis suggested evolutionary information, and physicochemical/biochemical properties play important roles in the recognition of methylated sites. These findings may provide valuable information for exploiting the mechanisms of methylation. Our method may serve as a useful tool for biologists to find the potential methylated sites of proteins.     

Blind Prediction of Charged Ligand Binding Affinities in a Model Binding Site

Predicting absolute protein–ligand binding affinities remains a frontier challenge in ligand discovery and design. This becomes more difficult when ionic interactions are involved because of the large opposing solvation and electrostatic attraction energies. In a blind test, we examined whether alchemical free-energy calculations could predict binding affinities of 14 charged and 5 neutral compounds previously untested as ligands for a cavity binding site in cytochrome c peroxidase. In this simplified site, polar and cationic ligands compete with solvent to interact with a buried aspartate. Predictions were tested by calorimetry, spectroscopy, and crystallography. Of the 15 compounds predicted to bind, 13 were experimentally confirmed, while 4 compounds were false negative predictions. Predictions had a root-mean-square error of 1.95 kcal/mol to the experimental affinities, and predicted poses had an average RMSD of 1.7 Å to the crystallographic poses. This test serves as a benchmark for these thermodynamically rigorous calculations at predicting binding affinities for charged compounds and gives insights into the existing sources of error, which are primarily electrostatic interactions inside proteins. Our experiments also provide a useful set of ionic binding affinities in a simplified system for testing new affinity prediction methods.     

pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes

MOTIVATION: There is a scarcity of efficient computational methods for predicting protein subcellular localization in eukaryotes. Currently available methods are inadequate for genome-scale predictions with several limitations. Here, we present a new prediction method, pTARGET that can predict proteins targeted to nine different subcellular locations in the eukaryotic animal species. RESULTS: The nine subcellular locations predicted by pTARGET include cytoplasm, endoplasmic reticulum, extracellular/secretory, golgi, lysosomes, mitochondria, nucleus, plasma membrane and peroxisomes. Predictions are based on the location-specific protein functional domains and the amino acid compositional differences across different subcellular locations. Overall, this method can predict 68-87% of the true positives at accuracy rates of 96-99%. Comparison of the prediction performance against PSORT showed that pTARGET prediction rates are higher by 11-60% in 6 of the 8 locations tested. Besides, the pTARGET method is robust enough for genome-scale prediction of protein subcellular localizations since, it does not rely on the presence of signal or target peptides. AVAILABILITY: A public web server based on the pTARGET method is accessible at the URL http://bioinformatics.albany.edu/~ptarget. Datasets used for developing pTARGET can be downloaded from this web server. Source code will be available on request from the corresponding author.     

Chromatographic separations of serum proteins on immobilized metal ion stationary phases

The separation of proteins on stationary phases consisting of a bound organic chelator and a chelated divalent transition metal has been studied as a function of (A) metal ion species; (B) mobile phase composition and pH; and (C) anion and cation concentration. Optimum separation was observed at alkaline pH on chelated nickel stationary phases. Ammonium and Tris salts reduced the affinity of the metal chelate packing for serum proteins. Halide ions caused the proteins to be more strongly bound to the stationary phase. High salt concentrations had only a small effect on the binding of serum proteins in the absence of amine containing buffers or salts. It was also observed that the ease of elution and the recovery of protein were dependent on pH and upon the presence of halides. The general order of elution of serum proteins, based on isoelectric focusing, was independent of metal ion species and elution conditions, suggesting that a single mechanism or a unique sequence of mechanisms was operative. The results suggest that ligand exchange is the major mechanism of separation under basic conditions and that hydrophobic effects are the result of the competition of nonnitrogen ions with ammonium ions or amines for ligand binding sites modifying or participating in protein binding. Protein binding studies under weak acidic conditions are also presented although the mechanism responsible for protein binding is unclear.     

Prediction of the exposure status of transmembrane beta barrel residues from protein sequence

We present BTMX (Beta barrel TransMembrane eXposure), a computational method to predict the exposure status (i.e. exposed to the bilayer or hidden in the protein structure) of transmembrane residues in transmembrane beta barrel proteins (TMBs). BTMX predicts the exposure status of known TM residues with an accuracy of 84.2% over 2,225 residues and provides a confidence score for all predictions. Predictions made are in concert with the fact that hydrophobic residues tend to be more exposed to the bilayer. The biological relevance of the input parameters is also discussed. The highest prediction accuracy is obtained when a sliding window comprising three residues with similar C(α)-C(β) vector orientations is employed. The prediction accuracy of the BTMX method on a separate unseen non-redundant test dataset is 78.1%. By employing out-pointing residues that are exposed to the bilayer, we have identified various physico-chemical properties that show statistically significant differences between the beta strands located at the oligomeric interfaces compared to the non-oligomeric strands. The BTMX web server generates colored, annotated snake-plots as part of the prediction results and is available under the BTMX tab at http://service.bioinformatik.uni-saarland.de/tmx-site/. Exposure status prediction of TMB residues may be useful in 3D structure prediction of TMBs.     

Accuracy of genotypic value predictions for marker-based selection in biparental plant populations

The availability of cheap and abundant molecular markers has led to plant-breeding methods that rely on the prediction of genotypic value from marker data, but published information is lacking on the accuracy of genotypic value predictions with empirical data in plants. Our objectives were to (1) determine the accuracy of genotypic value predictions from multiple linear regression (MLR) and genomewide selection via best linear unbiased prediction (BLUP) in biparental plant populations; (2) assess the accuracy of predictions for different numbers of markers (N _M) and progenies (N _P) used in estimation; and (3) determine if an empirical Bayes approach for modeling of the variances of individual markers and of epistatic effects leads to more accurate predictions in empirical data. We divided each of four maize (Zea mays L.) datasets, one Arabidopsis dataset, and two barley (Hordeum vulgare L.) datasets into an estimation set, where marker effects were calculated, and a test set, where genotypic values were predicted based on markers. Predictions were more accurate with BLUP than with MLR. Predictions became more accurate as N _P and N _M increased, until sufficient genome coverage was reached. Modeling marker variances with the empirical Bayes method sometimes led to slightly better predictions, but the accuracy with different variants of the empirical Bayes method was often inconsistent. In nearly all cases, the accuracy with BLUP was not significantly different from the highest accuracy across all methods. Accounting for epistasis in the empirical Bayes procedure led to poorer predictions. We concluded that among the methods considered, the quick and simple BLUP approach is the method of choice for predicting genotypic value in biparental plant populations.     

Ensemble approach to predict specificity determinants: benchmarking and validation

Background  

It is extremely important and challenging to identify the sites that are responsible for functional specification or diversification in protein families. In this study, a rigorous comparative benchmarking protocol was employed to provide a reliable evaluation of methods which predict the specificity determining sites. Subsequently, three best performing methods were applied to identify new potential specificity determining sites through ensemble approach and common agreement of their prediction results.     

资源竞争理论及其研究进展

介绍了国外近20年来发展起来的一种新的竞争理论——资源竞争理论。该理论包含两个主要假说,即R^*-法则和资源比假说。资源竞争理论已在微生物、微藻、高等植物及浮游动物中得到广泛的实验验证。阐述了资源竞争理论形成的基础——Monod竞争模型和Droop竞争模型。对两种模型在稳态及非稳态条件下的预测性能作了比较：对于稳态下的竞争,两种模型皆能做出较好的预测,但使用Monod模型更为简便;对于非稳态下的竞争,使用Droop模型更为合理。对资源竞争理论的发展趋势作了展望。     

Predicting diafiltration solution compositions for final ultrafiltration/diafiltration steps of monoclonal antibodies

Many liquid formulations for monoclonal antibodies (MAbs) require the final ultrafiltration/diafiltration step to operate at high protein concentrations, often at or above 100 g/L. When operating under these conditions, the excipient concentrations and pH of the final diafiltered retentate are frequently not equal to the corresponding excipient concentrations and pH of the diafiltration buffer. A model based on the Poisson-Boltzmann equation combined with volume exclusion was extended to predict both pH and excipient concentrations in the retentate for a given diafiltration buffer. This model was successfully applied to identify the diafiltration buffer composition required to achieve the desired pre-formulated bulk drug substance (retentate) conditions. Predictions were in good agreement with the experimental results, and reduced the number of experimental iterations needed to define the diafiltration buffer composition. Additionally, the predictive model was applied in a sensitivity analysis across ranges of protein charge, protein concentration, and diafiltration buffer pH and excipient concentration. This sensitivity analysis can facilitate the design of experiments for robustness testing, and allow for generalized predictions across classes of molecules such as MAbs.     

Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy

Identification of MHC binding peptides is essential for understanding the molecular mechanism of immune response. However, most of the prediction methods use motifs/profiles derived from experimental peptide binding data for specific MHC alleles, thus limiting their applicability only to those alleles for which such data is available. In this work we have developed a structure-based method which does not require experimental peptide binding data for training. Our method models MHC-peptide complexes using crystal structures of 170 MHC-peptide complexes and evaluates the binding energies using two well known residue based statistical pair potentials, namely Betancourt-Thirumalai (BT) and Miyazawa-Jernigan (MJ) matrices. Extensive benchmarking of prediction accuracy on a data set of 1654 epitopes from class I and class II alleles available in the SYFPEITHI database indicate that BT pair-potential can predict more than 60% of the known binders in case of 14 MHC alleles with AUC values for ROC curves ranging from 0.6 to 0.9. Similar benchmarking on 29,522 class I and class II MHC binding peptides with known IC(50) values in the IEDB database showed AUC values higher than 0.6 for 10 class I alleles and 9 class II alleles in predictions involving classification of a peptide to be binder or non-binder. Comparison with recently available benchmarking studies indicated that, the prediction accuracy of our method for many of the class I and class II MHC alleles was comparable to the sequence based methods, even if it does not use any experimental data for training. It is also encouraging to note that the ranks of true binding peptides could further be improved, when high scoring peptides obtained from pair potential were re-ranked using all atom forcefield and MM/PBSA method.