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.     

Protein and peptide identification algorithms using MS for use in high-throughput, automated pipelines

Current proteomics experiments can generate vast quantities of data very quickly, but this has not been matched by data analysis capabilities. Although there have been a number of recent reviews covering various aspects of peptide and protein identification methods using MS, comparisons of which methods are either the most appropriate for, or the most effective at, their proposed tasks are not readily available. As the need for high-throughput, automated peptide and protein identification systems increases, the creators of such pipelines need to be able to choose algorithms that are going to perform well both in terms of accuracy and computational efficiency. This article therefore provides a review of the currently available core algorithms for PMF, database searching using MS/MS, sequence tag searches and de novo sequencing. We also assess the relative performances of a number of these algorithms. As there is limited reporting of such information in the literature, we conclude that there is a need for the adoption of a system of standardised reporting on the performance of new peptide and protein identification algorithms, based upon freely available datasets. We go on to present our initial suggestions for the format and content of these datasets.     

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.     

3DSDSCAR—a three dimensional structural database for sialic acid-containing carbohydrates through molecular dynamics simulation

The inherent flexibility and lack of strong intramolecular interactions of oligosaccharides demand the use of theoretical methods for their structural elucidation. In spite of the developments of theoretical methods, not much research on glycoinformatics is done so far when compared to bioinformatics research on proteins and nucleic acids. We have developed three dimensional structural database for a sialic acid-containing carbohydrates (3DSDSCAR). This is an open-access database that provides 3D structural models of a given sialic acid-containing carbohydrate. At present, 3DSDSCAR contains 60 conformational models, belonging to 14 different sialic acid-containing carbohydrates, deduced through 10 ns molecular dynamics (MD) simulations. The database is available at the URL: http://www.3dsdscar.org.     

Towards Efficient Isolation of R Gene Orthologs from Multiple Genotypes: Optimization of Long Range-PCR

Resistance (R) genes and the proteins they encode are key components of the defense system of plants. The exploration of R gene diversity enables the study of R gene evolution and may facilitate the isolation of new and functional alleles. Most cloned R genes occur in clusters of related sequences. Thus, the development of a tool for reliable recovery of orthologous R gene sequences to the exclusion of paralogous sequences will facilitate R gene diversity analysis. The late blight resistance gene RB is a single functional locus embedded within a cluster of related sequences. Previously, the functional RB allele was cloned from wild potato using a Long Range-PCR (LR-PCR) technique, suggesting this method may be a promising tool for recovery of R gene orthologs in other genotypes. Using the RB gene as a model, we explored the limitations and improved three technical aspects of LR-PCR for multi-genotype applications. We present improved primers for the recovery of the RB locus and have identified efficient DNA extraction procedures and reliable amplification systems. We document that consensus sequences built from three independently generated LR-PCR clones can be up to 100% accurate. Our results show encouraging advances toward successful application of LR-PCR for isolating alleles from orthologous R gene loci.     

Statistical methods in genetics

In recent years, a very large variety of statistical methodologies, at various levels of complexity, have been put forward to analyse genotype data and detect genetic variations that may be responsible for increasing the susceptibility to disease. This review provides a concise account of a number of selected statistical methods for population-based association mapping, from single-marker tests of association to multi-marker data mining techniques for gene-gene interaction detection.     

Biocatalytic ketone reduction: A green and efficient access to enantiopure alcohols

Chiral secondary alcohols play an important role in pharmaceutical, agrochemical, and chemical industries. In recent years, impressive steps forward have been achieved towards biocatalytic ketone reduction as a green and useful access to enantiopure alcohols. An increasing number of novel and robust enzymes are now accessible as a result of the ongoing progress in genomics, screening and evolution technologies, while process engineering provides further success in areas of biocatalytic reduction in meeting synthetic challenges. The versatile platform of these techniques and strategies offers the possibility to apply high substrate loading and thus to overcome the limitation of low volumetric productivity of usual enzymatic processes which is the bottleneck for their practical application. In addition, the integration of bioreduction with other enzymatic or chemical steps allows the efficient synthesis of more complex chiral products.     

Mapping QTLs and candidate genes for iron and zinc concentrations in unpolished rice of Madhukar×Swarna RILs

Identifying QTLs/genes for iron and zinc in rice grains can help in biofortification programs. 168 F(7) RILs derived from Madhukar×Swarna were used to map QTLs for iron and zinc concentrations in unpolished rice grains. Iron ranged from 0.2 to 224ppm and zinc ranged from 0.4 to 104ppm. Genome wide mapping using 101 SSRs and 9 gene specific markers showed 5 QTLs on chromosomes 1, 3, 5, 7 and 12 significantly linked to iron, zinc or both. In all, 14 QTLs were identified for these two traits. QTLs for iron were co-located with QTLs for zinc on chromosomes 7 and 12. In all, ten candidate genes known for iron and zinc homeostasis underlie 12 of the 14 QTLs. Another 6 candidate genes were close to QTLs on chromosomes 3, 5 and 7. Thus the high priority candidate genes for high Fe and Zn in seeds are OsYSL1 and OsMTP1 for iron, OsARD2, OsIRT1, OsNAS1, OsNAS2 for zinc and OsNAS3, OsNRAMP1, Heavy metal ion transport and APRT for both iron and zinc together based on our genetic mapping studies as these genes strictly underlie QTLs. Several elite lines with high Fe, high Zn and both were identified.     

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.