Search

The identification and characterization of peptides from MS/MS data represents a critical aspect of proteomics. It has been the subject of extensive research in bioinformatics resulting in the generation of a fair number of identification software tools. Most often, only one program with a specific and unvarying set of parameters is selected for identifying proteins. Hence, a significant proportion of the experimental spectra do not match the peptide sequences in the screened database due to inappropriate parameters or scoring schemes. The Swiss protein identification toolbox (swissPIT) project provides the scientific community with an expandable multitool platform for automated in-depth analysis of MS data also able to handle data from high-throughput experiments. With swissPIT many problems have been solved: The missing standards for input and output formats (A), creation of analysis workflows (B), unified result visualization (C), and simplicity of the user interface (D). Currently, swissPIT supports four different programs implementing two different search strategies to identify MS/MS spectra. Conceived to handle the calculation-intensive needs of each of the programs, swissPIT uses the distributed resources of a Swiss-wide computer Grid (http://www.swing-grid.ch).

Bioinformatics tools may assist scientists in all steps of a typical 2-DE gel analysis workflow, that is, from the description of the sample preparation protocols, going through the gel image analysis and protein identification, to the publication of Internet-ready 2-DE gel databases. This short communication highlights in a single and summarised view, this workflow and the current bioinformatics solutions developed by the Proteome Informatics Group at the Swiss Institute of Bioinformatics.

On-line databases targeted towards protein contents in biological fluids are scarce. Consequently, the investigation of proteins identified in a biological fluid most importantly depends on crosschecking information gathered from less specific resources. This review summarises the key databases and tools for collecting information on tissue specificity or expression profiles. It also emphasises the high connectivity between databases fruitfully used to corroborate and piece information together. Finally, selected issues related to appropriate bioinformatics tools in the context of clinical applications are succinctly discussed., (Copyright © 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The present review attempts to cover the most recent initiatives directed towards representing, storing, displaying and processing protein-related data suited to undertake "comparative proteomics" studies. Data interpretation is brought into focus. Efforts invested into analysing and interpreting experimental data increasingly express the need for adding meaning. This trend is perceptible in work dedicated to determining ontologies, modelling interaction networks, etc. In parallel, technical advances in computer science are spurred by the development of the Web and the growing need to channel and understand massive volumes of data. Biology benefits from these advances as an application of choice for many generic solutions. Some examples of bioinformatics solutions are discussed and directions for on-going and future work conclude the review.

Bioinformatics tools for proteomics, also called proteome informatics tools, span today a large panel of very diverse applications ranging from simple tools to compare protein amino acid compositions to sophisticated software for large-scale protein structure determination. This review considers the available and ready to use tools that can help end-users to interpret, validate and generate biological information from their experimental data. It concentrates on bioinformatics tools for 2-DE analysis, for LC followed by MS analysis, for protein identification by PMF, by peptide fragment fingerprinting and by de novo sequencing and for data quantitation with MS data. It also discloses initiatives that propose to automate the processes of MS analysis and enhance the quality of the obtained results.

In the last ten years, the field of proteomics has expanded at a rapid rate. A range of exciting new technology has been developed and enthusiastically applied to an enormous variety of biological questions. However, the degree of stringency required in proteomic data generation and analysis appears to have been underestimated. As a result, there are likely to be numerous published findings that are of questionable quality, requiring further confirmation and/or validation. This manuscript outlines a number of key issues in proteomic research, including those associated with experimental design, differential display and biomarker discovery, protein identification and analytical incompleteness. In an effort to set a standard that reflects current thinking on the necessary and desirable characteristics of publishable manuscripts in the field, a minimal set of guidelines for proteomics research is then described. These guidelines will serve as a set of criteria which editors of PROTEOMICS will use for assessment of future submissions to the Journal.

Images obtained from high-throughput mass spectrometry (MS) contain information that remains hidden when looking at a single spectrum at a time. Image processing of liquid chromatography-MS datasets can be extremely useful for quality control, experimental monitoring and knowledge extraction. The importance of imaging in differential analysis of proteomic experiments has already been established through two-dimensional gels and can now be foreseen with MS images. We present MSight, a new software designed to construct and manipulate MS images, as well as to facilitate their analysis and comparison.

The SWISS-2DPAGE database was established in 1993 and is maintained collaboratively by the Swiss Institute of Bioinformatics (SIB) and the Biomedical Proteomics Research Group (BPRG) of the Geneva University Hospital. During these years, SWISS-2DPAGE underwent constant modification and improvement. Current content includes about 4000 identified spots corresponding to 1200 different protein entries in 36 reference maps from human, mouse, Arabidopsis thaliana, Dictyostelium discoideum, Escherichia coli, Saccharomyces cerevisiae and Staphylococcus aureus origins. With a high level of annotation and integration with other relevant databases, SWISS-2DPAGE is a reference source in the proteomics world. Queries to SWISS-2DPAGE database currently reach 1000 hits per day.

The Make 2D-DB tool has been previously developed to help build federated two-dimensional gel electrophoresis (2-DE) databases on one's own web site. The purpose of our work is to extend the strength of the first package and to build a more efficient environment. Such an environment should be able to fulfill the different needs and requirements arising from both the growing use of 2-DE techniques and the increasing amount of distributed experimental data.

In recent years, proteomics research has gained importance due to increasingly powerful techniques in protein purification, mass spectrometry and identification, and due to the development of extensive protein and DNA databases from various organisms. Nevertheless, current identification methods from spectrometric data have difficulties in handling modifications or mutations in the source peptide. Moreover, they have low performance when run on large databases (such as genomic databases), or with low quality data, for example due to bad calibration or low fragmentation of the source peptide. We present a new algorithm dedicated to automated protein identification from tandem mass spectrometry (MS/MS) data by searching a peptide sequence database. Our identification approach shows promising properties for solving the specific difficulties enumerated above. It consists of matching theoretical peptide sequences issued from a database with a structured representation of the source MS/MS spectrum. The representation is similar to the spectrum graphs commonly used by de novo sequencing software. The identification process involves the parsing of the graph in order to emphasize relevant sections for each theoretical sequence, and leads to a list of peptides ranked by a correlation score. The parsing of the graph, which can be a highly combinatorial task, is performed by a bio-inspired algorithm called Ant Colony Optimization algorithm.

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry has become a valuable tool in proteomics. With the increasing acquisition rate of mass spectrometers, one of the major issues is the development of accurate, efficient and automatic peptide mass fingerprinting (PMF) identification tools. Current tools are mostly based on counting the number of experimental peptide masses matching with theoretical masses. Almost all of them use additional criteria such as isoelectric point, molecular weight, PTMs, taxonomy or enzymatic cleavage rules to enhance prediction performance. However, these identification tools seldom use peak intensities as parameter as there is currently no model predicting the intensities based on the physicochemical properties of peptides. In this work, we used standard datamining methods such as classification and regression methods to find correlations between peak intensities and the properties of the peptides composing a PMF spectrum. These methods were applied on a dataset comprising a series of PMF experiments involving 157 proteins. We found that the C4.5 method gave the more informative results for the classification task (prediction of the presence or absence of a peptide in a spectra) and M5' for the regression methods (prediction of the normalized intensity of a peptide peak). The C4.5 result correctly classified 88% of the theoretical peaks; whereas the M5' peak intensities had a correlation coefficient of 0.6743 with the experimental peak intensities. These methods enabled us to obtain decision and model trees that can be directly used for prediction and identification of PMF results. The work performed permitted to lay the foundations of a method to analyze factors influencing the peak intensity of PMF spectra. A simple extension of this analysis could lead to improve the accuracy of the results by using a larger dataset. Additional peptide characteristics or even PMF experimental parameters can also be taken into account in the datamining process to analyze their influence on the peak intensity. Furthermore, this datamining approach can certainly be extended to the tandem mass spectrometry domain or other mass spectrometry derived methods.

The development of high throughput utilities to identify proteins is a major challenge in present research in the field of proteomics. One such utility, the molecular scanner, uses proteins separated by two-dimensional polyacrylamide gel electrophoresis that are digested in the gel and during transfer onto a collecting membrane. After adding a matrix, the membrane is inserted into a matrix-assisted laser desorption/ionization-time of flight mass spectrometer and a peptide mass fingerprint (PMF) is measured for every scanned site. Since the spacing between scanned sites is much smaller than the size of the most abundant protein spots, there is a certain redundancy in the data that was used in an earlier experiment with Escherichia coli [1] to improve mass calibration and PMF identification results. It was observed that the signal intensity of a peptide mass as a function of the position on the membrane showed similar patterns if peptides stemmed from the same protein. Taking account of these similarities a clustering algorithm was used to find lists of experimental masses with similar intensity distributions, which provided clearer identification of the corresponding proteins. Here, these methods are applied to a human plasma scan, where proteins were highly modified and less separated. The presence of very abundant proteins like albumin and immunoglobulins added another difficulty. The calibration of the initial PMFs was not satisfactory and masses had to be recalibrated. After discarding chemical noise, the membrane was partitioned into regions and for each region protein identification was carried out separately. A new scoring method was used, where the PMF score was multiplied by a factor that measures the similarity of matching peptides. This method proved to be more robust than the method developed in [1] if the region where a protein was found had an extended, nonspherical shape and strong overlap with regions of other proteins. Many proteins annotated on the SWISS-2D PAGE human plasma master gel could be clearly identified and many interesting properties were observed.

The complexity of nature is tremendous, particularly at the epigenetic level. Proteomic studies must therefore complement genomic discoveries to better understand biological processes. Because of the very large number of modified proteins and their great variability in physico-chemical properties, no single method can be used to analyze all of them. Mass spectrometry has demonstrated its superior ability to rapidly identify and partially characterize numerous proteins in low abundance and has become a central element in most proteomic projects. Studies of protein function are necessary to understand biological pathways and this is being tackled using several approaches such as two hybrid systems, phage technology or affinity methods. Finally, mathematical and bioinformatic developments will be essential to study nature's complex systems.