Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Computational methods and tools for protein phosphorylation analysis

Signaling pathways represent a central regulatory mechanism of biological systems where a key event in their correct functioning is the reversible phosphorylation of proteins. Protein phosphorylation affects at least one-third of all proteins and is the most widely studied posttranslational modification. Phosphorylation analysis is still perceived, in general, as difficult or cumbersome and not readily attempted by many, despite the high value of such information. Specifically, determining the exact location of a phosphorylation site is currently considered a major hurdle, thus reliable approaches are necessary for the detection and localization of protein phosphorylation. The goal of this PhD thesis was to develop computation methods and tools for mass spectrometry-based protein phosphorylation analysis, particularly validation of phosphorylation sites. In the first two studies, we developed methods for improved identification of phosphorylation sites in MALDI-MS. In the first study it was achieved through the automatic combination of spectra from multiple matrices, while in the second study, an optimized protocol for sample loading and washing conditions was suggested. In the third study, we proposed and evaluated the hypothesis that in ESI-MS, tandem CID and HCD spectra of phosphopeptides can be accurately predicted and used in spectral library searching. This novel strategy for phosphosite validation and identification offered accuracy that outperformed the other currently existing popular methods and proved applicable to complex biological samples. And finally, we significantly improved the performance of our command-line prototype tool, added graphical user interface, and options for customizable simulation parameters and filtering of selected spectra, peptides or proteins. The new software, SimPhospho, is open-source and can be easily integrated in a phosphoproteomics data analysis workflow. Together, these bioinformatics methods and tools enable confident phosphosite assignment and improve reliable phosphoproteome identification and reportin

A high-throughput \u3ci\u3ede novo\u3c/i\u3e sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry

Abstract Background High-resolution tandem mass spectra can now be readily acquired with hybrid instruments, such as LTQ-Orbitrap and LTQ-FT, in high-throughput shotgun proteomics workflows. The improved spectral quality enables more accurate de novo sequencing for identification of post-translational modifications and amino acid polymorphisms. Results In this study, a new de novo sequencing algorithm, called Vonode, has been developed specifically for analysis of such high-resolution tandem mass spectra. To fully exploit the high mass accuracy of these spectra, a unique scoring system is proposed to evaluate sequence tags based primarily on mass accuracy information of fragment ions. Consensus sequence tags were inferred for 11,422 spectra with an average peptide length of 5.5 residues from a total of 40,297 input spectra acquired in a 24-hour proteomics measurement of Rhodopseudomonas palustris. The accuracy of inferred consensus sequence tags was 84%. According to our comparison, the performance of Vonode was shown to be superior to the PepNovo v2.0 algorithm, in terms of the number of de novo sequenced spectra and the sequencing accuracy. Conclusions Here, we improved de novo sequencing performance by developing a new algorithm specifically for high-resolution tandem mass spectral data. The Vonode algorithm is freely available for download at http://compbio.ornl.gov/Vonode webcite

A high-throughput de novo sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry

<p>Abstract</p> <p>Background</p> <p>High-resolution tandem mass spectra can now be readily acquired with hybrid instruments, such as LTQ-Orbitrap and LTQ-FT, in high-throughput shotgun proteomics workflows. The improved spectral quality enables more accurate <it>de novo </it>sequencing for identification of post-translational modifications and amino acid polymorphisms.</p> <p>Results</p> <p>In this study, a new <it>de novo </it>sequencing algorithm, called Vonode, has been developed specifically for analysis of such high-resolution tandem mass spectra. To fully exploit the high mass accuracy of these spectra, a unique scoring system is proposed to evaluate sequence tags based primarily on mass accuracy information of fragment ions. Consensus sequence tags were inferred for 11,422 spectra with an average peptide length of 5.5 residues from a total of 40,297 input spectra acquired in a 24-hour proteomics measurement of <it>Rhodopseudomonas palustris</it>. The accuracy of inferred consensus sequence tags was 84%. According to our comparison, the performance of Vonode was shown to be superior to the PepNovo v2.0 algorithm, in terms of the number of <it>de novo </it>sequenced spectra and the sequencing accuracy.</p> <p>Conclusions</p> <p>Here, we improved <it>de novo </it>sequencing performance by developing a new algorithm specifically for high-resolution tandem mass spectral data. The Vonode algorithm is freely available for download at <url>http://compbio.ornl.gov/Vonode</url>.</p

Optimized GeLC-MS/MS for Bottom-Up Proteomics

Despite tremendous advances in mass spectrometry instrumentation and mass spectrometry-based methodologies, global protein profiling of organellar, cellular, tissue and body fluid proteomes in different organisms remains a challenging task due to the complexity of the samples and the wide dynamic range of protein concentrations. In addition, large amounts of produced data make result exploitation difficult. To overcome these issues, further advances in sample preparation, mass spectrometry instrumentation as well as data processing and data analysis are required. The presented study focuses as first on the improvement of the proteolytic digestion of proteins in in-gel based proteomic approach (Gel-LCMS). To this end commonly used bovine trypsin (BT) was modified with oligosaccharides in order to overcome its main disadvantages, such as weak thermostability and fast autolysis at basic pH. Glycosylated trypsin derivates maintained their cleavage specifity and showed better thermostability, autolysis resistance and less autolytic background than unmodified BT. In line with the “accelerated digestion protocol” (ADP) previously established in our laboratory modified enzymes were tested in in-gel digestion of proteins. Kinetics of in-gel digestion was studied by MALDI TOF mass spectrometry using 18O-labeled peptides as internal standards as well as by label-free quantification approach, which utilizes intensities of peptide ions detected by nanoLC-MS/MS. In the performed kinetic study the effect of temperature, enzyme concentration and digestion time on the yield of digestion products was characterized. The obtained results showed that in-gel digestion of proteins by glycosylated trypsin conjugates was less efficient compared to the conventional digestion (CD) and achieved maximal 50 to 70% of CD yield, suggesting that the attached sugar molecules limit free diffusion of the modified trypsins into the polyacrylamide gel pores. Nevertheless, these thermostable and autolysis resistant enzymes can be regarded as promising candidates for gel-free shotgun approach. To address the reliability issue of proteomic data I further focused on protein identifications with borderline statistical confidence produced by database searching. These hits are typically produced by matching a few marginal quality MS/MS spectra to database peptide sequences and represent a significant bottleneck in proteomics. A method was developed for rapid validation of borderline hits, which takes advantage of the independent interpretation of the acquired tandem mass spectra by de novo sequencing software PepNovo followed by mass-spectrometry driven BLAST (MS BLAST) sequence similarity searching that utilize all partially accurate, degenerate and redundant proposed peptide sequences. It was demonstrated that a combination of MASCOT software, de novo sequencing software PepNovo and MS BLAST, bundled by a simple scripted interface, enabled rapid and efficient validation of a large number of borderline hits, produced by matching of one or two MS/MS spectra with marginal statistical significance

Molecular Formula Identification using High Resolution Mass Spectrometry: Algorithms and Applications in Metabolomics and Proteomics

Wir untersuchen mehrere theoretische und praktische Aspekte der Identifikation der Summenformel von Biomolekülen mit Hilfe von hochauflösender Massenspektrometrie. Durch die letzten Forschritte in der Instrumentation ist die Massenspektrometrie (MS) zur einen der Schlüsseltechnologien für die Analyse von Biomolekülen in der Proteomik und Metabolomik geworden. Sie misst die Massen der Moleküle in der Probe mit hoher Genauigkeit, und ist für die Messdatenerfassung im Hochdurchsatz gut geeignet. Eine der Kernaufgaben in der MS-basierten Proteomik und Metabolomik ist die Identifikation der Moleküle in der Probe. In der Metabolomik unterliegen Metaboliten der Strukturaufklärung, beginnend bei der Summenformel eines Moleküls, d.h. der Anzahl der Atome jedes Elements. Dies ist der entscheidende Schritt in der Identifikation eines unbekannten Metabolits, da die festgelegte Formel die Anzahl der möglichen Molekülstrukturen auf eine viel kleinere Menge reduziert, die mit Methoden der automatischen Strukturaufklärung weiter analysiert werden kann. Nach der Vorverarbeitung ist die Ausgabe eines Massenspektrometers eine Liste von Peaks, die den Molekülmassen und deren Intensitäten, d.h. der Anzahl der Moleküle mit einer bestimmten Masse, entspricht. Im Prinzip können die Summenformel kleiner Moleküle nur mit präzisen Massen identifiziert werden. Allerdings wurde festgestellt, dass aufgrund der hohen Anzahl der chemisch legitimer Formeln in oberen Massenbereich eine exzellente Massengenaugkeit alleine für die Identifikation nicht genügt. Hochauflösende MS erlaubt die Bestimmung der Molekülmassen und Intensitäten mit hervorragender Genauigkeit. In dieser Arbeit entwickeln wir mehrere Algorithmen und Anwendungen, die diese Information zur Identifikation der Summenformel der Biomolekülen anwenden

DEVELOPMENT AND APPLICATION OF MASS SPECTROMETRY-BASED PROTEOMICS TO GENERATE AND NAVIGATE THE PROTEOMES OF THE GENUS POPULUS

Historically, there has been tremendous synergy between biology and analytical technology, such that one drives the development of the other. Over the past two decades, their interrelatedness has catalyzed entirely new experimental approaches and unlocked new types of biological questions, as exemplified by the advancements of the field of mass spectrometry (MS)-based proteomics. MS-based proteomics, which provides a more complete measurement of all the proteins in a cell, has revolutionized a variety of scientific fields, ranging from characterizing proteins expressed by a microorganism to tracking cancer-related biomarkers. Though MS technology has advanced significantly, the analysis of complicated proteomes, such as plants or humans, remains challenging because of the incongruity between the complexity of the biological samples and the analytical techniques available. In this dissertation, analytical methods utilizing state-of-the-art MS instrumentation have been developed to address challenges associated with both qualitative and quantitative characterization of eukaryotic organisms. In particular, these efforts focus on characterizing Populus, a model organism and potential feedstock for bioenergy. The effectiveness of pre-existing MS techniques, initially developed to identify proteins reliably in microbial proteomes, were tested to define the boundaries and characterize the landscape of functional genome expression in Populus. Although these approaches were generally successful, achieving maximal proteome coverage was still limited by a number of factors, including genome complexity, the dynamic range of protein identification, and the abundance of protein variants. To overcome these challenges, improvements were needed in sample preparation, MS instrumentation, and bioinformatics. Optimization of experimental procedures and implementation of current state-of-the-art instrumentation afforded the most detailed look into the predicted proteome space of Populus, offering varying proteome perspectives: 1) network-wide, 2) pathway-specific, and 3) protein-level viewpoints. In addition, we implemented two bioinformatic approaches that were capable of decoding the plasticity of the Populus proteome, facilitating the identification of single amino acid polymorphisms and generating a more accurate profile of protein expression. Though the methods and results presented in this dissertation have direct implications in the study of bioenergy research, more broadly this dissertation focuses on developing techniques to contend with the notorious challenges associated with protein characterization in all eukaryotic organisms

Quantitative analysis of mass spectrometry proteomics data : Software for improved life science

The rapid advances in life science, including the sequencing of the human genome and numerous other techiques, has given an extraordinary ability to aquire data on biological systems and human disease. Even so, drug development costs are higher than ever, while the rate of new approved treatments is historically low. A potential explanation to this discrepancy might be the difficulty of understanding the biology underlying the acquired data; the difficulty to refine the data to useful knowledge through interpretation. In this thesis the refinement of the complex data from mass spectrometry proteomics is studied. A number of new algorithms and programs are presented and demonstrated to provide increased analytical ability over previously suggested alternatives. With the higher goal of increasing the mass spectrometry laboratory scientific output, pragmatic studies were also performed, to create new set on compression algorithms for reduced storage requirement of mass spectrometry data, and also to characterize instrument stability. The final components of this thesis are the discussion of the technical and instrumental weaknesses associated with the currently employed mass spectrometry proteomics methodology, and the discussion of current lacking academical software quality and the reasons thereof. As a whole, the primary algorithms, the enabling technology, and the weakness discussions all aim to improve the current capability to perform mass spectrometry proteomics. As this technology is crucial to understand the main functional components of biology, proteins, this quest should allow better and higher quality life science data, and ultimately increase the chances of developing new treatments or diagnostics