Prospects for a Statistical Theory of LC/TOFMS Data

The critical importance of employing sound statistical arguments when seeking to draw inferences from inexact measurements is well-established throughout the sciences. Yet fundamental statistical methods such as hypothesis testing can currently be applied to only a small subset of the data analytical problems encountered in LC/MS experiments. The means of inference that are more generally employed are based on a variety of heuristic techniques and a largely qualitative understanding of their behavior. In this article, we attempt to move towards a more formalized approach to the analysis of LC/TOFMS data by establishing some of the core concepts required for a detailed mathematical description of the data. Using arguments that are based on the fundamental workings of the instrument, we derive and validate a probability distribution that approximates that of the empirically obtained data and on the basis of which formal statistical tests can be constructed. Unlike many existing statistical models for MS data, the one presented here aims for rigor rather than generality. Consequently, the model is closely tailored to a particular type of TOF mass spectrometer although the general approach carries over to other instrument designs. Looking ahead, we argue that further improvements in our ability to characterize the data mathematically could enable us to address a wide range of data analytical problems in a statistically rigorous manner

In Silico Analysis of the Apolipoprotein E and the Amyloid β Peptide Interaction: Misfolding Induced by Frustration of the Salt Bridge Network

The relationship between Apolipoprotein E (ApoE) and the aggregation processes of the amyloid β (Aβ) peptide has been shown to be crucial for Alzheimer's disease (AD). The presence of the ApoE4 isoform is considered to be a contributing risk factor for AD. However, the detailed molecular properties of ApoE4 interacting with the Aβ peptide are unknown, although various mechanisms have been proposed to explain the physiological and pathological role of this relationship. Here, computer simulations have been used to investigate the process of Aβ interaction with the N-terminal domain of the human ApoE isoforms (ApoE2, ApoE3 and ApoE4). Molecular docking combined with molecular dynamics simulations have been undertaken to determine the Aβ peptide binding sites and the relative stability of binding to each of the ApoE isoforms. Our results show that from the several ApoE isoforms investigated, only ApoE4 presents a misfolded intermediate when bound to Aβ. Moreover, the initial α-helix used as the Aβ peptide model structure also becomes unstructured due to the interaction with ApoE4. These structural changes appear to be related to a rearrangement of the salt bridge network in ApoE4, for which we propose a model. It seems plausible that ApoE4 in its partially unfolded state is incapable of performing the clearance of Aβ, thereby promoting amyloid forming processes. Hence, the proposed model can be used to identify potential drug binding sites in the ApoE4-Aβ complex, where the interaction between the two molecules can be inhibited

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics

<p>Abstract</p> <p>Background</p> <p>High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usually require large amounts of training data.</p> <p>Results</p> <p>We introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems. We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC). Furthermore, the predicted retention times are used to improve spectrum identifications by a <it>p</it>-value-based filtering approach. The approach was tested on a number of different datasets and shows excellent performance while requiring only very small training sets (about 40 peptides instead of thousands). Using the retention time predictor in our retention time filter improves the fraction of correctly identified peptide mass spectra significantly.</p> <p>Conclusion</p> <p>The proposed kernel function is well-suited for the prediction of chromatographic separation in computational proteomics and requires only a limited amount of training data. The performance of this new method is demonstrated by applying it to peptide retention time prediction in IP-RP-HPLC and prediction of peptide sample fractionation in SAX-SPE. Finally, we incorporate the predicted chromatographic behavior in a <it>p</it>-value based filter to improve peptide identifications based on liquid chromatography-tandem mass spectrometry.</p

Emission of volatile halogenated compounds, speciation and localization of bromine and iodine in the brown algal genome model Ectocarpus siliculosus

This study explores key features of bromine and iodine metabolism in the filamentous brown alga and genomics model Ectocarpus siliculosus. Both elements are accumulated in Ectocarpus, albeit at much lower concentration factors (2-3 orders of magnitude for iodine, and < 1 order of magnitude for bromine) than e.g. in the kelp Laminaria digitata. Iodide competitively reduces the accumulation of bromide. Both iodide and bromide are accumulated in the cell wall (apoplast) of Ectocarpus, with minor amounts of bromine also detectable in the cytosol. Ectocarpus emits a range of volatile halogenated compounds, the most prominent of which by far is methyl iodide. Interestingly, biosynthesis of this compound cannot be accounted for by vanadium haloperoxidase since the latter have not been found to catalyze direct halogenation of an unactivated methyl group or hydrocarbon so a methyl halide transferase-type production mechanism is proposed

Metagenomics of the Deep Mediterranean, a Warm Bathypelagic Habitat

BACKGROUND: Metagenomics is emerging as a powerful method to study the function and physiology of the unexplored microbial biosphere, and is causing us to re-evaluate basic precepts of microbial ecology and evolution. Most marine metagenomic analyses have been nearly exclusively devoted to photic waters. METHODOLOGY/PRINCIPAL FINDINGS: We constructed a metagenomic fosmid library from 3,000 m-deep Mediterranean plankton, which is much warmer (approximately 14 degrees C) than waters of similar depth in open oceans (approximately 2 degrees C). We analyzed the library both by phylogenetic screening based on 16S rRNA gene amplification from clone pools and by sequencing both insert extremities of ca. 5,000 fosmids. Genome recruitment strategies showed that the majority of high scoring pairs corresponded to genomes from Rhizobiales within the Alphaproteobacteria, Cenarchaeum symbiosum, Planctomycetes, Acidobacteria, Chloroflexi and Gammaproteobacteria. We have found a community structure similar to that found in the aphotic zone of the Pacific. However, the similarities were significantly higher to the mesopelagic (500-700 m deep) in the Pacific than to the single 4000 m deep sample studied at this location. Metabolic genes were mostly related to catabolism, transport and degradation of complex organic molecules, in agreement with a prevalent heterotrophic lifestyle for deep-sea microbes. However, we observed a high percentage of genes encoding dehydrogenases and, among them, cox genes, suggesting that aerobic carbon monoxide oxidation may be important in the deep ocean as an additional energy source. CONCLUSIONS/SIGNIFICANCE: The comparison of metagenomic libraries from the deep Mediterranean and the Pacific ALOHA water column showed that bathypelagic Mediterranean communities resemble more mesopelagic communities in the Pacific, and suggests that, in the absence of light, temperature is a major stratifying factor in the oceanic water column, overriding pressure at least over 4000 m deep. Several chemolithotrophic metabolic pathways could supplement organic matter degradation in this most depleted habitat