In-depth protein characterization by mass spectrometry.

Within this chapter, various techniques and instructions for characterizing primary structure of proteins are presented, whereas the focus lies on obtaining as much complete sequence information of single proteins as possible. Especially, in the area of protein production, mass spectrometry-based detailed protein characterization plays an increasing important role for quality control. In comparison to typical proteomics applications, wherein it is mostly sufficient to identify proteins by few peptides, several complementary techniques have to be applied to maximize primary structure information and analysis steps have to be specifically adopted. Starting from sample preparation down to mass spectrometry analysis and finally to data analysis, some of the techniques typically applied are outlined here in a summarizing and introductory manner.

An easy-to-use Decoy Database Builder software tool, implementing different decoy strategies for false discovery rate calculation in automated MS/MS protein identifications.

One of the major challenges for large scale proteomics research is the quality evaluation of results. Protein identification from complex biological samples or experimental setups is often a manual and subjective task which lacks profound statistical evaluation. This is not feasible for high-throughput proteomic experiments which result in large datasets of thousands of peptides and proteins and their corresponding mass spectra. To improve the quality, reliability and comparability of scientific results, an estimation of the rate of erroneously identified proteins is advisable. Moreover, scientific journals increasingly stipulate that articles containing considerable MS data should be subject to stringent statistical evaluation. We present a newly developed easy-to-use software tool enabling quality evaluation by generating composite target-decoy databases usable with all relevant protein search engines. This tool, when used in conjunction with relevant statistical quality criteria, enables to reliably determine peptides and proteins of high quality, even for nonexperienced users (e.g. laboratory staff, researchers without programming knowledge). Different strategies for building decoy databases are implemented and the resulting databases are characterized and compared. The quality of protein identification in high-throughput proteomics is usually measured by the false positive rate (FPR), but it is shown that the false discovery rate (FDR) delivers a more meaningful, robust and comparable value.

Gaining knowledge from previously unexplained spectra-application of the PTM-Explorer software to detect PTM in HUPO BPP MS/MS data.

A novel software tool named PTM-Explorer has been applied to LC-MS/MS datasets acquired within the Human Proteome Organisation (HUPO) Brain Proteome Project (BPP). PTM-Explorer enables automatic identification of peptide MS/MS spectra that were not explained in typical sequence database searches. The main focus was detection of PTMs, but PTM-Explorer detects also unspecific peptide cleavage, mass measurement errors, experimental modifications, amino acid substitutions, transpeptidation products and unknown mass shifts. To avoid a combinatorial problem the search is restricted to a set of selected protein sequences, which stem from previous protein identifications using a common sequence database search. Prior to application to the HUPO BPP data, PTM-Explorer was evaluated on excellently manually characterized and evaluated LC-MS/MS data sets from Alpha-A-Crystallin gel spots obtained from mouse eye lens. Besides various PTMs including phosphorylation, a wealth of experimental modifications and unspecific cleavage products were successfully detected, completing the primary structure information of the measured proteins. Our results indicate that a large amount of MS/MS spectra that currently remain unidentified in standard database searches contain valuable information that can only be elucidated using suitable software tools.

Functional annotation of proteins identified in human brain during the HUPO Brain Proteome Project pilot study.

The HUPO Brain Proteome Project is an initiative coordinating proteomics studies to characterise human and mouse brain proteomes. Proteins identified in human brain samples during the project's pilot phase were put into biological context through integration with various annotation sources followed by a bioinformatics analysis. The data set was related to the genome sequence via the genes encoding identified proteins including an assessment of splice variant identification as well as an analysis of tissue specificity of the respective transcripts. Proteins were furthermore categorised according to subcellular localisation, molecular function and biological process, grouped into protein families and mapped to biological pathways they are known to act in. Involvement in pathological conditions was examined based on association with entries in the online version of Mendelian Inheritance in Man and an interaction network was derived from curated protein-proteininteraction data. Overall a non-redundant set of 1804 proteins was identified in human brain samples. In the majority of cases splice variants could be unambiguously identified by unique peptides, including matches to several hypothetical transcripts of known as well as predicted genes.

The power of cooperative investigation: summary and comparison of the HUPO Brain Proteome Project pilot study results.

Within the pilot phase of the HUPO Brain Proteome Project, nine participating laboratories analysed human (epilepsy and/or post mortem material) and mouse brain samples (embryonic, juvenile and adult), respectively, using a variety of different state of the art techniques. Thirty-seven different analytical approaches were accomplished. Of these analyses, 17 were done differentially, i.e. the protein expression patterns of the different samples (human or mouse) were compared. A catalogue of all proteins present in the respective sample was built in 20 analyses (mapping). All data were collected in the Data Collection Center in Bochum, Germany, and were reprocessed according to thoroughly defined parameters. In this report, a summary of all results and inter-laboratory comparisons with respect to the number of identified proteins, the analysed organism, and the used techniques is presented.

Automated reprocessing pipeline for searching heterogeneous mass spectrometric data of the HUPO Brain Proteome Project pilot phase.

The newly available techniques for sensitive proteome analysis and the resulting amount of data require a new bioinformatics focus on automatic methods for spectrum reprocessing and peptide/protein validation. Manual validation of results in such studies is not feasible and objective enough for quality relevant interpretation. The necessity for tools enabling an automatic quality control is, therefore, important to produce reliable and comparable data in such big consortia as the Human Proteome Organization Brain Proteome Project. Standards and well-defined processing pipelines are important for these consortia. We show a way for choosing the right database model, through collecting data, processing these with a decoy database and end up with a quality controlled protein list merged from several search engines, including a known false-positive rate.

The HUPO Brain Proteome Project jamboree: centralised summary of the pilot studies.

The Bioinformatics Committee of the HUPO Brain Proteome Project (HUPO BPP) meets regularly to execute the post-lab analyses of the data produced in the HUPO BPP pilot studies. On January 9-11, 2006 the members as well as invited analysts came together at the European Bioinformatics Institute in Hinxton, UK for the pilot studies jamboree. The results of the reprocessing were presented and tasks forces were initiated to compile, to interpret and to summarise the data obtained.

5th HUPO BPP Bioinformatics Meeting at the European Bioinformatics Institute in Hinxton, UK--Setting the analysis frame.

The Bioinformatics Committee of the HUPO Brain Proteome Project (HUPO BPP) meets regularly to execute the post-lab analyses of the data produced in the HUPO BPP pilot studies. On July 7, 2005 the members came together for the 5th time at the European Bioinformatics Institute (EBI) in Hinxton, UK, hosted by Rolf Apweiler. As a main result, the parameter set of the semi-automated data re-analysis of MS/MS spectra has been elaborated and the subsequent work steps have been defined.

Towards data management of the HUPO Human Brain Proteome Project pilot phase.

The pilot phase of the Human Brain Proteome Project as a part of the Human Proteome Organisation has just been started. In two pilot studies, 18 different laboratories are analyzing mouse brains of three age stages and human brain autopsy versus biopsy material, respectively. The overall aim is to elucidate the portfolio of available techniques as well as to elaborate common standards. As a first step, it was decided to use the common bioinformatics platform ProteinScape that was introduced to the participating groups in a two day course in Bochum, Germany.

Evaluation of algorithms for protein identification from sequence databases using mass spectrometry data.

In this work, the commonly used algorithms for mass spectrometry based protein identification, Mascot, MS-Fit, ProFound and SEQUEST, were studied in respect to the selectivity and sensitivity of their searches. The influence of various search parameters were also investigated. Approximately 6600 searches were performed using different search engines with several search parameters to establish a statistical basis. The applied mass spectrometric data set was chosen from a current proteome study. The huge amount of data could only be handled with computational assistance. We present a software solution for fully automated triggering of several peptide mass fingerprinting (PMF) and peptide fragmentation fingerprinting (PFF) algorithms. The development of this high-throughput method made an intensive evaluation based on data acquired in a typical proteome project possible. Previous evaluations of PMF and PFF algorithms were mainly based on simulations.