Improved peptide sequencing using isotope information inherent in tandem mass spectra

Introduction to Mass Spectrometry-Based Proteomics

Mass spectrometry, a technology to determine the mass of ionized molecules and biomolecules, is increasingly applied for the global identification and quantification of proteins. Proteomics applies mass spectrometry in many applications, and each application requires consideration of analytical choices, instrumental limitations and data processing steps. These depend on the aim of the study and means of conducting it. Choosing the right combination of sample preparation, MS instrumentation, and data processing allows exploration of different aspects of the proteome. This chapter gives an outline for some of these commonly used setups and some of the key concepts, many of which later chapters discuss in greater depth. Understanding and handling mass spectrometry data is a multifaceted task that requires many user decisions to obtain the most comprehensive information from an MS experiment. Later chapters in this book deal in-depth with various aspects of the process and how different tools addresses the many analytical challenges. This chapter revises the basic concept in mass spectrometry (MS)-based proteomics.

Introduction to mass spectrometry-based proteomics

Mass spectrometry has been widely applied to study biomolecules and one rapidly developing field is the global analysis of proteins, proteomics. Understanding and handling mass spectrometry data is a multifaceted task that requires many decisions to be made to get the most comprehensive information from an experiment. Later chapters in this book deal in-depth with various aspects of the process and how different tools can be applied to the many analytical challenges. This introductory chapter is intended as a basic introduction to mass spectrometry (MS)-based proteomics to set the scene for newcomers and give pointers to reference material. There are many applications of mass spectrometry in proteomics and each application is associated with some analytical choices, instrumental limitations and data processing steps that depend on the aim of the study and means of conducting it. Different aspects of the proteome can be explored by choosing the right combination of sample preparation, MS instrumentation and data processing. This chapter gives an outline for some of these commonly used setups and some of the key concepts, many of which are explored in greater depth in later chapters.

Algorithms for the de novo sequencing of peptides from tandem mass spectra

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field.

De novo peptide identification via tandem mass spectrometry and integer linear optimization

A novel methodology for the automated de novo identification of peptides via integer linear optimization (also referred to as integer linear programming or ILP) and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. A variety of challenging peptide identification problems, accompanied by a comparative study with five state-of-the-art methods, are examined to illustrate the proposed method's ability to address (a) residue-dependent fragmentation properties that result in missing ion peaks and (b) the variability of resolution in different mass analyzers. A preprocessing algorithm is utilized to identify important m/z values in the tandem mass spectrum. Missing peaks, due to residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to select the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the ILP sequencing stages with the experimental tandem mass spectrum. The novel, proposed de novo method, denoted as PILOT, is compared to existing popular methods such as Lutefisk, PEAKS, PepNovo, EigenMS, and NovoHMM for a set of spectra resulting from QTOF and ion trap instruments.

Low energy peptide fragmentations in an ESI-Q-Tof type mass spectrometer

Efficient peptide sequencing relies on both high quality MS/MS data acquisition and exhaustive knowledge of gas-phase dissociation mechanisms. We report our contribution to the elaboration of more comprehensive fragmentation models required for efficient automated MS/MS spectra interpretation. Following a statistical approach, various peptides (296 sequences of variable compositions and lengths) were prepared and subjected to low-energy collision-induced dissociations (CID) in an electrospray hybrid instrument (ESI-Q-q-Tof type mass spectrometer) that has retained relatively limited attention so far. Besides, our studies were focused on low molecular weight singly charged peptides that often failed to be identified by sequencing algorithms. Only half of the studied compounds showed charge directed dissociations in accordance with the mobile proton model producing fragment ions directly related to the primary sequence. For the peptides that did not exhibit the expected fragment ion series, alternative dissociation behaviors issued from complex rearrangements were evidenced.

A Mixed-Integer Optimization Framework for De Novo Peptide Identification

A novel methodology for the de novo identification of peptides by mixed-integer optimization and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. Several problems are examined to illustrate the proposed method's ability to address (1) residue-dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum.

Advances in the analysis of dynamic protein complexes by proteomics and data processing

Signal transduction governs virtually every cellular function of multicellular organisms, and its deregulation leads to a variety of diseases. This intricate network of molecular interactions is mediated by proteins that are assembled into complexes within individual signaling pathways, and their composition and function is often regulated by different post-translational modifications. Proteomic approaches are commonly used to analyze biological complexes and networks, but often lack the specificity to address the dynamic and hence transient nature of the interactions and the influence of the multiple post-translational modifications that govern these processes. Here we review recent developments in proteomic research to address these limitations, and discuss several technologies that have been developed for this purpose. The synergy between these proteomic and computational tools, when applied together with global methods to the analysis of individual proteins, complexes and pathways, may allow researchers to unravel the underlying mechanisms of signaling networks in greater detail than previously possible.

Shotgun Protein Sequencing by Tandem Mass Spectra Assembly

The analysis of mass spectrometry data is still largely based on identification of single MS/MS spectra and does not attempt to make use of the extra information available in multiple MS/MS spectra from partially or completely overlapping peptides. Analysis of MS/MS spectra from multiple overlapping peptides opens up the possibility of assembling MS/MS spectra into entire proteins, similarly to the assembly of overlapping DNA reads into entire genomes. In this paper, we present for the first time a way to detect, score, and interpret overlaps between uninterpreted MS/MS spectra in an attempt to sequence entire proteins rather than individual peptides. We show that this approach not only extends the length of reconstructed amino acid sequences but also dramatically improves the quality of de novo peptide sequencing, even for low mass accuracy MS/MS data.

Analysis, statistical validation and dissemination of large-scale proteomics datasets generated by tandem MS

Tandem mass spectrometry has been used increasingly for high-throughput analysis of complex protein samples. A major challenge lies in the consistent, objective and transparent analysis of the large amounts of data generated by such experiments and in their dissemination and publication. Here, we review currently available computational tools and discuss the need for statistical criteria in the analysis of large proteomics datasets.