Determination of partial amino acid composition from tandem mass spectra for use in peptide identification strategies

We demonstrate a new approach to the determination of amino acid composition from tandem mass spectrometrically fragmented peptides using both experimental and simulated data. The approach has been developed to be used as a search-space filter in a protein identification pipeline with the aim of increased performance above that which could be attained by using immonium ion information. Three automated methods have been developed and tested: one based upon a simple peak traversal, in which all intense ion peaks are treated as being either a b- or y-ion using a wide mass tolerance; a second which uses a much narrower tolerance and does not perform transformations of ion peaks to the complementary type; and the unique fragments method which allows for b- or y-ion type to be inferred and corroborated using a scan of the other ions present in each peptide spectrum. The combination of these methods is shown to provide a high-accuracy set of amino acid predictions using both experimental and simulated data sets. These high quality predictions, with an accuracy of over 85%, may be used to identify peptide fragments that are hard to identify using other methods. The data simulation algorithm is also shown post priori to be a good model of noiseless tandem mass spectrometric peptide data.     

PRISM: a data management system for high-throughput proteomics

Advanced proteomic research efforts involving areas such as systems biology or biomarker discovery are enabled by the use of high level informatics tools that allow the effective analysis of large quantities of differing types of data originating from various studies. Performing such analyses on a large scale is not feasible without a computational platform that performs data processing and management tasks. Such a platform must be able to provide high-throughput operation while having sufficient flexibility to accommodate evolving data analysis tools and methodologies. The Proteomics Research Information Storage and Management system (PRISM) provides a platform that serves the needs of the accurate mass and time tag approach developed at Pacific Northwest National Laboratory. PRISM incorporates a diverse set of analysis tools and allows a wide range of operations to be incorporated by using a state machine that is accessible to independent, distributed computational nodes. The system has scaled well as data volume has increased over several years, while allowing adaptability for incorporating new and improved data analysis tools for more effective proteomics research.     

Identification of candidate SNPs for drug induced toxicity from differentially expressed genes in associated tissues

The growing collection of publicly available high-throughput data provides an invaluable resource for generating preliminary in silico data in support of novel hypotheses. In this study we used a cross-dataset meta-analysis strategy to identify novel candidate genes and genetic variations relevant to paclitaxel/carboplatin-induced myelosuppression and neuropathy. We identified genes affected by drug exposure and present in tissues associated with toxicity. From ten top-ranked genes 42 non-synonymous single nucleotide polymorphisms (SNPs) were identified in silico and genotyped in 94 cancer patients treated with carboplatin/paclitaxel. We observed variations in 11 SNPs, of which seven were present in a sufficient frequency for statistical evaluation. Of these seven SNPs, three were present in ABCA1 and ATM, and showed significant or borderline significant association with either myelosuppression or neuropathy. The strikingly high number of associations between genotype and clinically observed toxicity provides support for our data-driven computations strategy to identify biomarkers for drug toxicity.     

Catalytic cycle of human glutathione reductase near 1 A resolution

Efficient enzyme catalysis depends on exquisite details of structure beyond those resolvable in typical medium- and high-resolution crystallographic analyses. Here we report synchrotron-based cryocrystallographic studies of natural substrate complexes of the flavoenzyme human glutathione reductase (GR) at nominal resolutions between 1.1 and 0.95 Å that reveal new aspects of its mechanism. Compression in the active site causes overlapping van der Waals radii and distortion in the nicotinamide ring of the NADPH substrate, which enhances catalysis via stereoelectronic effects. The bound NADPH and redox-active disulfide are positioned optimally on opposite sides of the flavin for a 1,2-addition across a flavin double bond. The new structures extend earlier observations to reveal that the redox-active disulfide loop in GR is an extreme case of sequential peptide bonds systematically deviating from planarity—a net deviation of 53° across five residues. But this apparent strain is not a factor in catalysis, as it is present in both oxidized and reduced structures. Intriguingly, the flavin bond lengths in oxidized GR are intermediate between those expected for oxidized and reduced flavin, but we present evidence that this may not be due to the protein environment but instead due to partial synchrotron reduction of the flavin by the synchrotron beam. Finally, of more general relevance, we present evidence that the structures of synchrotron-reduced disulfide bonds cannot generally be used as reliable models for naturally reduced disulfide bonds.     

Remote access to ACNUC nucleotide and protein sequence databases at PBIL

The ACNUC biological sequence database system provides powerful and fast query and extraction capabilities to a variety of nucleotide and protein sequence databases. The collection of ACNUC databases served by the Pôle Bio-Informatique Lyonnais includes the EMBL, GenBank, RefSeq and UniProt nucleotide and protein sequence databases and a series of other sequence databases that support comparative genomics analyses: HOVERGEN and HOGENOM containing families of homologous protein-coding genes from vertebrate and prokaryotic genomes, respectively; Ensembl and Genome Reviews for analyses of prokaryotic and of selected eukaryotic genomes. This report describes the main features of the ACNUC system and the access to ACNUC databases from any internet-connected computer. Such access was made possible by the definition of a remote ACNUC access protocol and the implementation of Application Programming Interfaces between the C, Python and R languages and this communication protocol. Two retrieval programs for ACNUC databases, Query_win, with a graphical user interface and raa_query, with a command line interface, are also described. Altogether, these bioinformatics tools provide users with either ready-to-use means of querying remote sequence databases through a variety of selection criteria, or a simple way to endow application programs with an extensive access to these databases. Remote access to ACNUC databases is open to all and fully documented (http://pbil.univ-lyon1.fr/databases/acnuc/acnuc.html).     

世界水生植物科属检索系统的设计与建立

生物分类学涉及海量数据 ,而且随着研究的不断深入 ,各类相关信息还在以惊人的速度增长 ,这为利用传统方法和管理模式处理复杂的数据带来了很大困难。随着计算机技术的发展 ,其特有的高效性、稳定性等特点为大量生物信息的处理带来了革命性的变化。利用计算机技术管理生物信息不仅可以大幅度提高工作效率 ,而且对充分发挥其管理信息的作用具有重要的意义。人们已经在分类学方面进行了许多工作 ,也取得了不少成绩。例如英国的国立植被分类系统 ( Na-tional Vegetation Classification) ,它全面系统地总结了整个英国的植被类型 ,其支撑系统…     

Prediction of MHC Class I Binding Peptides by a Query Learning Algorithm Based on Hidden Markov Models

A query learning algorithm based on hidden Markov models (HMMs) isdeveloped to design experiments for string analysis and prediction of MHCclass I binding peptides. Query learning is introduced to aim at reducingthe number of peptide binding data for training of HMMs. A multiple numberof HMMs, which will collectively serve as a committee, are trained withbinding data and used for prediction in real-number values. The universeof peptides is randomly sampled and subjected to judgement by the HMMs.Peptides whose prediction is least consistent among committee HMMs aretested by experiment. By iterating the feedback cycle of computationalanalysis and experiment the most wanted information is effectivelyextracted. After 7 rounds of active learning with 181 peptides in all,predictive performance of the algorithm surpassed the so far bestperforming matrix based prediction. Moreover, by combining the bothmethods binder peptides (log Kd < -6) could be predicted with84% accuracy. Parameter distribution of the HMMs that can be inspectedvisually after training further offers a glimpse of dynamic specificity ofthe MHC molecules.