Cupricyclins, Novel Redox-Active Metallopeptides Based on Conotoxins Scaffold

Highly stable natural scaffolds which tolerate multiple amino acid substitutions represent the ideal starting point for the application of rational redesign strategies to develop new catalysts of potential biomedical and biotechnological interest. The knottins family of disulphide-constrained peptides display the desired characteristics, being highly stable and characterized by hypervariability of the inter-cysteine loops. The potential of knottins as scaffolds for the design of novel copper-based biocatalysts has been tested by engineering a metal binding site on two different variants of an ω-conotoxin, a neurotoxic peptide belonging to the knottins family. The binding site has been designed by computational modelling and the redesigned peptides have been synthesized and characterized by optical, fluorescence, electron spin resonance and nuclear magnetic resonance spectroscopy. The novel peptides, named Cupricyclin-1 and -2, bind one Cu2+ ion per molecule with nanomolar affinity. Cupricyclins display redox activity and catalyze the dismutation of superoxide anions with an activity comparable to that of non-peptidic superoxide dismutase mimics. We thus propose knottins as a novel scaffold for the design of catalytically-active mini metalloproteins

Understanding mortality in the 1918-1919 influenza pandemic in England and Wales

BACKGROUND: The causes of recurrent waves in the 1918-1919 influenza pandemic are not fully understood. OBJECTIVES: To identify the risk factors for influenza onset, spread and mortality in waves 1, 2 and 3 (summer, autumn and winter) in England and Wales in 1918-1919. METHODS: Influenza mortality rates for 333 population units and putative risk factors were analysed by correlation and by regressions weighted by population size and adjusted for spatial trends. RESULTS: For waves 1 and 3, influenza mortality was higher in younger, northerly and socially disadvantaged populations experiencing higher all-cause mortality in 1911-1914. Influenza mortality was greatest in wave 2, but less dependent on underlying population characteristics. Wave duration was shorter in areas with higher influenza mortality, typically associated with increasing population density. Regression analyses confirmed the importance of geographical factors and pre-pandemic mortality for all three waves. Age effects were complex, with the suggestion that younger populations with greater mortality in wave 1 had lesser mortality in wave 2. CONCLUSIONS: Our findings suggest that socially disadvantaged populations were more vulnerable, that older populations were partially protected by prior immunity in wave 1 and that exposure of (younger) populations in one wave could protect against mortality in the subsequent wave. An increase in viral virulence could explain the greater mortality in wave 2. Further modelling of causal processes will help to explain, in considerable detail, how social and geographical factors, season, pre-existing and acquired immunity and virulence affected viral transmission and pandemic mortality in 1918-1919

SVM-based prediction of propeptide cleavage sites in spider toxins identifies toxin innovation in an Australian tarantula

Spider neurotoxins are commonly used as pharmacological tools and are a popular source of novel compounds with therapeutic and agrochemical potential. Since venom peptides are inherently toxic, the host spider must employ strategies to avoid adverse effects prior to venom use. It is partly for this reason that most spider toxins encode a protective proregion that upon enzymatic cleavage is excised from the mature peptide. In order to identify the mature toxin sequence directly from toxin transcripts, without resorting to protein sequencing, the propeptide cleavage site in the toxin precursor must be predicted bioinformatically. We evaluated different machine learning strategies (support vector machines, hidden Markov model and decision tree) and developed an algorithm (SpiderP) for prediction of propeptide cleavage sites in spider toxins. Our strategy uses a support vector machine (SVM) framework that combines both local and global sequence information. Our method is superior or comparable to current tools for prediction of propeptide sequences in spider toxins. Evaluation of the SVM method on an independent test set of known toxin sequences yielded 96% sensitivity and 100% specificity. Furthermore, we sequenced five novel peptides (not used to train the final predictor) from the venom of the Australian tarantula Selenotypus plumipes to test the accuracy of the predictor and found 80% sensitivity and 99.6% 8-mer specificity. Finally, we used the predictor together with homology information to predict and characterize seven groups of novel toxins from the deeply sequenced venom gland transcriptome of S. plumipes, which revealed structural complexity and innovations in the evolution of the toxins. The precursor prediction tool (SpiderP) is freely available on ArachnoServer (http://www.arachnoserver.org/spiderP.html), a web portal to a comprehensive relational database of spider toxins. All training data, test data, and scripts used are available from the SpiderP website