Data mining of enzymes using specific peptides

<p>Abstract</p> <p>Background</p> <p>Predicting the function of a protein from its sequence is a long-standing challenge of bioinformatic research, typically addressed using either sequence-similarity or sequence-motifs. We employ the novel motif method that consists of Specific Peptides (SPs) that are unique to specific branches of the Enzyme Commission (EC) functional classification. We devise the Data Mining of Enzymes (DME) methodology that allows for searching SPs on arbitrary proteins, determining from its sequence whether a protein is an enzyme and what the enzyme's EC classification is.</p> <p>Results</p> <p>We extract novel SP sets from Swiss-Prot enzyme data. Using a training set of July 2006, and test sets of July 2008, we find that the predictive power of SPs, both for true-positives (enzymes) and true-negatives (non-enzymes), depends on the coverage length of all SP matches (the number of amino-acids matched on the protein sequence). DME is quite different from BLAST. Comparing the two on an enzyme test set of July 2008, we find that DME has lower recall. On the other hand, DME can provide predictions for proteins regarded by BLAST as having low homologies with known enzymes, thus supplying complementary information. We test our method on a set of proteins belonging to 10 bacteria, dated July 2008, establishing the usefulness of the coverage-length cutoff to determine true-negatives. Moreover, sifting through our predictions we find that some of them have been substantiated by Swiss-Prot annotations by July 2009. Finally we extract, for production purposes, a novel SP set trained on all Swiss-Prot enzymes as of July 2009. This new set increases considerably the recall of DME. The new SP set is being applied to three metagenomes: Sargasso Sea with over 1,000,000 proteins, producing predictions of over 220,000 enzymes, and two human gut metagenomes. The outcome of these analyses can be characterized by the enzymatic profile of the metagenomes, describing the relative numbers of enzymes observed for different EC categories.</p> <p>Conclusions</p> <p>Employing SPs for predicting enzymatic activity of proteins works well once one utilizes coverage-length criteria. In our analysis, L ≥ 7 has led to highly accurate results.</p

Fermi-bubble bulk and edge analysis reveals dust, cooling breaks, and cosmic-ray diffusion, facilitating a self-consistent model

The full, radio to $\gamma$-ray spectrum of the Fermi bubbles is shown to be consistent with standard strong-shock electron acceleration at the bubble edge, without ad-hoc energy cutoffs, if the ambient interstellar radiation is strong; the $\gamma$-ray cooling break should then have a microwave counterpart, undetected until now. Indeed, a broadband bubble-edge analysis uncovers a pronounced downstream dust component, which masked the anticipated $\sim35$ GHz spectral break, and the same overall radio softening consistent with Kraichnan diffusion previously reported in $\gamma$-rays.Comment: 6 pages, 3 figures, SI; comments welcom

Genetic diversity and population structure inferred from the partially duplicated genome of domesticated carp, Cyprinus carpio L.

Genetic relationships among eight populations of domesticated carp (Cyprinus carpio L.), a species with a partially duplicated genome, were studied using 12 microsatellites and 505 AFLP bands. The populations included three aquacultured carp strains and five ornamental carp (koi) variants. Grass carp (Ctenopharyngodon idella) was used as an outgroup. AFLP-based gene diversity varied from 5% (grass carp) to 32% (koi) and reflected the reasonably well understood histories and breeding practices of the populations. A large fraction of the molecular variance was due to differences between aquacultured and ornamental carps. Further analyses based on microsatellite data, including cluster analysis and neighbor-joining trees, supported the genetic distinctiveness of aquacultured and ornamental carps, despite the recent divergence of the two groups. In contrast to what was observed for AFLP-based diversity, the frequency of heterozygotes based on microsatellites was comparable among all populations. This discrepancy can potentially be explained by duplication of some loci in Cyprinus carpio L., and a model that shows how duplication can increase heterozygosity estimates for microsatellites but not for AFLP loci is discussed. Our analyses in carp can help in understanding the consequences of genotyping duplicated loci and in interpreting discrepancies between dominant and co-dominant markers in species with recent genome duplication

Four Linked Genes Participate in Controlling Sporulation Efficiency in Budding Yeast

Quantitative traits are conditioned by several genetic determinants. Since such genes influence many important complex traits in various organisms, the identification of quantitative trait loci (QTLs) is of major interest, but still encounters serious difficulties. We detected four linked genes within one QTL, which participate in controlling sporulation efficiency in Saccharomyces cerevisiae. Following the identification of single nucleotide polymorphisms by comparing the sequences of 145 genes between the parental strains SK1 and S288c, we analyzed the segregating progeny of the cross between them. Through reciprocal hemizygosity analysis, four genes, RAS2, PMS1, SWS2, and FKH2, located in a region of 60 kilobases on Chromosome 14, were found to be associated with sporulation efficiency. Three of the four “high” sporulation alleles are derived from the “low” sporulating strain. Two of these sporulation-related genes were verified through allele replacements. For RAS2, the causative variation was suggested to be a single nucleotide difference in the upstream region of the gene. This quantitative trait nucleotide accounts for sporulation variability among a set of ten closely related winery yeast strains. Our results provide a detailed view of genetic complexity in one “QTL region” that controls a quantitative trait and reports a single nucleotide polymorphism-trait association in wild strains. Moreover, these findings have implications on QTL identification in higher eukaryotes

Shaping Neuronal Network Activity by Presynaptic Mechanisms

<div><p>Neuronal microcircuits generate oscillatory activity, which has been linked to basic functions such as sleep, learning and sensorimotor gating. Although synaptic release processes are well known for their ability to shape the interaction between neurons in microcircuits, most computational models do not simulate the synaptic transmission process directly and hence cannot explain how changes in synaptic parameters alter neuronal network activity. In this paper, we present a novel neuronal network model that incorporates presynaptic release mechanisms, such as vesicle pool dynamics and calcium-dependent release probability, to model the spontaneous activity of neuronal networks. The model, which is based on modified leaky integrate-and-fire neurons, generates spontaneous network activity patterns, which are similar to experimental data and robust under changes in the model's primary gain parameters such as excitatory postsynaptic potential and connectivity ratio. Furthermore, it reliably recreates experimental findings and provides mechanistic explanations for data obtained from microelectrode array recordings, such as network burst termination and the effects of pharmacological and genetic manipulations. The model demonstrates how elevated asynchronous release, but not spontaneous release, synchronizes neuronal network activity and reveals that asynchronous release enhances utilization of the recycling vesicle pool to induce the network effect. The model further predicts a positive correlation between vesicle priming at the single-neuron level and burst frequency at the network level; this prediction is supported by experimental findings. Thus, the model is utilized to reveal how synaptic release processes at the neuronal level govern activity patterns and synchronization at the network level.</p></div

Model demonstrates how an increase in asynchronous release, and not spontaneous release, enhances neuronal network activity.

<p><b>(A)</b> Reduction of calcium clearance rate redistributes single-neuron vesicle release (left) and increases the proportion of asynchronous release to synchronous release in the model (right). <b>(B)</b> Higher simulated asynchronous release increases network burst firing rate (left). Experimentally, the increase in asynchronous release induced by strontium application is correlated with the increase in the network burst maximum firing rate (right; modified from Lavi et al [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004438#pcbi.1004438.ref003" target="_blank">3</a>]). <b>(C)</b> Illustration of the change in the release function leading to higher spontaneous release probability at low intracellular calcium concentration ([Ca²⁺]_I; top panel). Higher simulated spontaneous release reduces network burst firing rate (bottom left), in agreement with the reduction in activity induced by the expression of DOC2B^D218,220N (right) in the experimental MEA recordings. <b>(D)</b> Opposite effects of asynchronous and spontaneous release on network activity. In general, higher asynchronous release increases network activity whereas higher spontaneous release reduces network activity (error bars show SEM).</p