tsORFdb: Theoretical Small Open Reading Frames (ORFs) database and massProphet: Peptide Mass Fingerprinting (PMF) tool for unknown small functional ORFs

Peptide mass fingerprinting (PMF) has become one of the most widely used methods for rapid identification of proteins in proteomics research. Many peaks, however, remain unassigned after PMF analysis, partly because of post-translational modification and the limited scope of protein sequences. Almost all PMF tools employ only known or predicted protein sequences and do not include open reading frames (ORFs) in the genome, which eliminates the chance of finding novel functional peptides. Unlike most tools that search protein sequences from known coding sequences, the tool we developed uses a database for theoretical small ORFs (tsORFs) and a PMF application using a tsORFs database (tsORFdb). The tsORFdb is a database for ORFeome that encompasses all potential tsORFs derived from whole genome sequences as well as the predicted ones. The massProphet system tries to extend the search scope to include the ORFeome using the tsORFdb. The tsORFdb and massProphet should be useful for proteomics research to give information about unknown small ORFs as well as predicted and registered proteins.     

Using reaction mechanism to measure enzyme similarity

The concept of reaction similarity has been well studied in terms of the overall transformation associated with a reaction, but not in terms of mechanism. We present the first method to give a quantitative measure of the similarity of reactions based upon their explicit mechanisms. Two approaches are presented to measure the similarity between individual steps of mechanisms: a fingerprint-based approach that incorporates relevant information on each mechanistic step; and an approach based only on bond formation, cleavage and changes in order. The overall similarity for two reaction mechanisms is then calculated using the Needleman-Wunsch alignment algorithm. An analysis of MACiE, a database of enzyme mechanisms, using our measure of similarity identifies some examples of convergent evolution of chemical mechanisms. In many cases, mechanism similarity is not reflected by similarity according to the EC system of enzyme classification. In particular, little mechanistic information is conveyed by the class level of the EC system.     

Long‐term datasets: From descriptive to predictive data using ecoinformatics

This Special Feature includes contributions on data‐processing of large ecological datasets under the heading ecoinformatics. Herewith the latter term is now al so established in the Journal of Vegetation Science. Ecoinfomatics is introduced as a rapid growing field within community ecology which is generating exciting new developments in ecology and in particular vegetation ecology. In our field, ecoinformatics deals with the understanding of patterns of species distributions at local and regional scales, and on the assemblages of species in relation to their properties, the local environment and their distribution in the region. Community ecology using ecoinformatics is related to bioinformatics, community ecology, biogeography and macroecology. We make clear how ecoinformatics in vegetation science and particularly the IAVS Working Group on Ecoinformatics has developed from the work of the old Working Group for Data Processing which was active during the 1970s and 1980s. Recent developments, including the creation of TURBOVEG and Syn Bio Sys in Europa and VEGBANK in the USA, form a direct link with these pioneer activities, both scientifically and personally. The contributions collected in this Special Feature present examples of seco‐infeveral types of the use of databases and the application of programmes and models. The main types are the study of long‐term vegetation dynamics in different cases of primary and secondary succession and the understanding of successional developments in terms of species traits. Among the future developments of great significance we mention the use of a variety of different large datasets for the study of the distribution and ecology and conservation of rare and threatened species.     

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence,full structure,and active site microenvironment similarity

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods.     

Identification and engineering of the cytochalasin gene cluster from Aspergillus clavatus NRRL 1

Cytochalasins are a group of fungal secondary metabolites with diverse structures and bioactivities, including cytochalasin E produced by Aspergillus clavatus, which is a potent anti-angiogenic agent. Here, we report the identification and characterization of the cytochalasin gene cluster from A. clavatus NRRL 1. As a producer of cytochalasin E and K, the genome of A. clavatus was analyzed and the ∼30 kb ccs gene cluster was identified based on the presence of a polyketide synthase–nonribosomal peptide synthetases (PKS–NRPS) and a putative Baeyer–Villiger monooxygenase (BVMO). Deletion of the central PKS–NRPS gene, ccsA, abolished the production of cytochalasin E and K, confirming the association between the natural products and the gene cluster. Based on bioinformatic analysis, a putative biosynthetic pathway is proposed. Furthermore, overexpression of the pathway specific regulator ccsR elevated the titer of cytochalasin E from 25 mg/L to 175 mg/L. Our results not only shed light on the biosynthesis of cytochalasins, but also provided genetic tools for increasing and engineering the production.     

Shift in chicken intestinal gene association networks after infection with Salmonella

A primary infection of Salmonella enteritidis causes a spatial-temporal dependent change in the gene expression patterns in the intestine of chickens (Gallus gallus). This is the result of a dynamic intestinal response to adapt to the altered environment and to optimize its ‘health’ and functionality under the new circumstances. By inferring gene association networks (GANs), the complexities of and changes in biological networks can be uncovered. Within such GANs highly interacting (hub) genes can be identified, which are supposed to be high-level regulators connected to multiple processes. By exploring the intestinal expression of genes differing between control and Salmonella infected chicken in a time-dependent manner differences in GANs were found. In control chickens more developmental processes were observed, whereas in infected chickens relatively more processes were associated to ‘defense/pathogen response’. Moreover the conserved protein domains of the identified hub genes in controls were nuclear-associated, whereas hub genes in infected chickens were involved in ‘cellular communication’. The shift in topology and functionality of the intestinal GANs in control and Salmonella infected animals and the identification of GAN-specific hubs is a first step to understand the complexity of biological networks and processes regulating intestinal health and functionality under normal and disturbed conditions.     

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.