Secondary uranium mineralization in southern Finland and its relationship to recent glacial events

Uranium series dating has been carried out on secondary uranyl silicate minerals formed during sub-glacial and post-glacial weathering of Proterozoic uraninite ores in south west Finland. The samples were obtained from two sites adjacent to the Salpauselkä III ice marginal formation and cover a range of depths, from the surface to more than 60 m. Measured ages fall into three distinct groups, 70–100 ka, 28–36 ka and < 2500 yr. The youngest set is associated with surface exposures and the crystals display clear evidence of re-working. The most likely trigger for uranium release at depths below the surface weathering zone is intrusion of oxidising glacial melt water. The latter is often characterised by very high discharge rates along channels, which close once the overpressure generated at the ice margin is released. There is excellent correspondence between the two Finnish sites and published data for similar deposits over a large area of southern and central Sweden. None of the seventy samples analysed gave a U–Th age between 40 and 70 ka; a second hiatus is apparent at 20 ka, coinciding with the Last Glacial Maximum. Thus, the process responsible for uranyl silicate formation was halted for significant periods, owing to a change in geochemical conditions or the hydrogeological regime. These data support the presence of interstadial conditions during the Early and Middle Weichselian since in the absence of major climatic perturbations the uranium phases at depth are stable. When viewed in conjunction with proxy data from mammoth remains it would appear that the region was ice-free prior to the Last Glacial Maximum.

Assembling novel protein folds from super-secondary structural fragments

The results of applying a fragment-based protein tertiary structure prediction method to the prediction of 14 CASP5 target domains are described. The method is based on the assembly of supersecondary structural fragments taken from highly resolved protein structures using a simulated annealing algorithm. A number of good predictions for proteins with novel folds were produced, although not always as the first model. For two fold recognition targets, FRAGFOLD produced the most accurate model in both cases, despite the fact that the predictions were not based on a template structure. Although clear progress has been made in improving FRAGFOLD since CASP4, the ranking of final models still seems to be the main problem that needs to be addressed before the next CASP experiment

Secondary structure prediction with support vector machines

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

Benchmarking secondary structure prediction for fold recognition

If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the “usefulness” and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.