This volume discusses the latest mass spectrometry (MS)-based technologies for proteoform identification, characterization, and quantification. Some of the topics covered in this book include sample preparation, proteoform separation, proteoform gas-phase fragmentation, and bioinformatics tools for MS data analysis. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and comprehensive, Proteoform Identification: Methods and Protocols is a valuable resource for researchers in both academia and the biopharmaceutical industry who are interested in proteoform analysis using MS.

This volume discusses the latest mass spectrometry (MS)-based technologies for proteoform identification, characterization, and quantification. Some of the topics covered in this book include sample preparation, proteoform separation, proteoform gas-phase fragmentation, and bioinformatics tools for MS data analysis. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and comprehensive, Proteoform Identification: Methods and Protocols is a valuable resource for researchers in both academia and the biopharmaceutical industry who are interested in proteoform analysis using MS.

Proteoforms are distinct protein molecule forms created by variations in genes, gene expression, and other biological processes. Many proteoforms contain multiple primary structural alterations, including amino acid substitutions, terminal truncations, and posttranslational modifications. These primary structural alterations play a crucial role in determining protein functions: proteoforms from the same protein with different alterations may exhibit different functional behaviors. Because top-down mass spectrometry directly analyzes intact proteoforms and provides complete sequence information of proteoforms, it has become the method of choice for the identification of complex proteoforms. Although instruments and experimental protocols for top-down mass spectrometry have been advancing rapidly in the past several years, many computational problems in this area remain unsolved, and the development of software tools for analyzing such data is still at its very early stage. In this dissertation, we propose several novel algorithms for challenging computational problems in proteoform identification by top-down mass spectrometry. First, we present two approximate spectrum-based protein sequence filtering algorithms that quickly find a small number of candidate proteins from a large proteome database for a query mass spectrum. Second, we describe mass graph-based alignment algorithms that efficiently identify proteoforms with variable post-translational modifications and/or terminal truncations. Third, we propose a Markov chain Monte Carlo method for estimating the statistical signi ficance of identified proteoform spectrum matches. They are the first efficient algorithms that take into account three types of alterations: variable post-translational modifications, unexpected alterations, and terminal truncations in proteoform identification. As a result, they are more sensitive and powerful than other existing methods that consider only one or two of the three types of alterations. All the proposed algorithms have been incorporated into TopMG, a complete software pipeline for complex proteoform identification. Experimental results showed that TopMG significantly increases the number of identifications than other existing methods in proteome-level top-down mass spectrometry studies. TopMG will facilitate the applications of top-down mass spectrometry in many areas, such as the identification and quantification of clinically relevant proteoforms and the discovery of new proteoform biomarkers.

The gap between genotype and phenotype is incredibly difficult to bridge. The complexity of the intervening processes is the reason for this challenge. We believe that proteoforms, the specific molecular products of genes, hold the key to bridging the genotype-phenotype gap with detailed molecular understanding. However, comprehensive proteoform characterization is incredibly difficult due to the wide range in both physicochemical properties and abundances of proteoforms in complex systems. Most commonly, the broad characterization of proteoforms is achieved by top-down proteomics, a technique in which intact proteoforms are analyzed by tandem mass spectrometry. To move towards comprehensive characterization of proteoforms in complex systems requires the development of novel techniques at all aspects of top-down proteomics, from sample preparation to data analysis.Even as deeper proteoform coverage becomes achievable, the practicality of the technique for routine biological analysis remains limited. This is because many of the strategies used to improve proteoform coverage also come with a significant time cost. In this work, I demonstrate our efforts toward developing techniques to make proteoform identification by mass spectrometry more efficient while also achieving reasonable depth. This work centers around the development of strategies for proteoform identification without the need for tandem MS. First, I present a NeuCode chemical labeling strategy that enables the experimental determination of tissue proteoform cysteine counts, which can be used to improve the confidence of proteoform identifications made sans-fragmentation. I then present a proof-of-concept study, where an E.coli proteoform atlas is constructed, and subsequently used to enable efficient proteoform re-identification.

A proteoform is the basic unit in a proteome, defined as its amino acid sequence + post-translational modifications + spatial conformation + localization + cofactors + binding partners + a function, which is the final functional performer of a gene. Studies on proteoforms offer in-depth insights and can lead to the discovery of reliable biomarkers and therapeutic targets for effective prediction, diagnosis, prognostic assessment, and therapy of disease. This book focuses on the concept, study, and applications of proteoforms. Chapters cover such topics as methodologies for identifying and preparing proteoforms, proteoform pattern alteration in pituitary adenomas, and proteoforms in leukemia.

Proteomic and Metabolomic Approaches to Biomarker Discovery demonstrates how to leverage biomarkers to improve accuracy and reduce errors in research. Disease biomarker discovery is one of the most vibrant and important areas of research today, as the identification of reliable biomarkers has an enormous impact on disease diagnosis, selection of treatment regimens, and therapeutic monitoring. Various techniques are used in the biomarker discovery process, including techniques used in proteomics, the study of the proteins that make up an organism, and metabolomics, the study of chemical fingerprints created from cellular processes. Proteomic and Metabolomic Approaches to Biomarker Discovery is the only publication that covers techniques from both proteomics and metabolomics and includes all steps involved in biomarker discovery, from study design to study execution. The book describes methods, and presents a standard operating procedure for sample selection, preparation, and storage, as well as data analysis and modeling. This new standard effectively eliminates the differing methodologies used in studies and creates a unified approach. Readers will learn the advantages and disadvantages of the various techniques discussed, as well as potential difficulties inherent to all steps in the biomarker discovery process. A vital resource for biochemists, biologists, analytical chemists, bioanalytical chemists, clinical and medical technicians, researchers in pharmaceuticals, and graduate students, Proteomic and Metabolomic Approaches to Biomarker Discovery provides the information needed to reduce clinical error in the execution of research. - Describes the use of biomarkers to reduce clinical errors in research - Includes techniques from a range of biomarker discoveries - Covers all steps involved in biomarker discovery, from study design to study execution

The term proteoform is used to denote all the molecular forms in which the protein product of a single gene can be found. The most frequent processes that lead to transcript modification and the biological implications of these changes observed in the final protein product will be discussed. Proteoforms arising from genetic variations, alternatively spliced RNA transcripts and post-translational modifications will be commented. This chapter will present an evolution of the techniques used to identify the proteoforms and the importance of this identification for understanding of biological processes. This chapter highlights the fundamental concepts in the field of top-down mass spectrometry (TDMS), and provides numerous examples for the use of knowledge obtained from the identification of proteoforms. The identification of mutant proteins is one of the emerging areas of proteogenomics and has the potential to recognize novel disease biomarkers and may point to useful targets for identification of therapeutic approaches.

Diversity at the protein level accounts for much of our biological complexity. A proteoform family consists of all of the different forms of a protein (proteoforms) arising from a single gene, including sequence variants, splice variants, and posttranslationally modified forms. It is important to identify and quantify proteoforms to understand biological systems because different proteoforms from the same family can exhibit different functions. The established technique to identify proteoforms is top-down proteomics, where intact proteins are analyzed by mass spectrometry. Available top-down search software programs require quality fragmentation spectra to identify proteoforms, limiting the number of proteoforms that can be identified due to instrument time and spectral complexity. The subset of proteoforms identified is typically in the highly abundant and low molecular weight portion of the proteome. This dissertation describes the development of integrated proteomic strategies to address these challenges. We created a software program that constructs proteoform families by grouping together observed proteoforms based on differences in intact mass, enabling the identification of proteoforms by intact-mass analysis. Chapter 1 provides an overview of mass spectrometry-based proteomics and outlines current challenges in proteoform analysis. Chapter 2 describes the integration of proteoform family construction into a typical top-down proteomic workflow, resulting in a 40% increase in the number of proteoform identifications in an analysis of yeast lysate. Chapter 3 demonstrates the application of this integrated intact-mass and top-down strategy to identify and quantify murine mitochondrial proteoforms. Chapter 4 presents the construction of proteoform families in a human breast cancer cell line using data acquired on the 21 tesla FT-ICR mass spectrometry platform. Chapter 5 describes augmenting intact-mass analysis to determine candidate identifications for isotopically unresolved proteoforms, facilitating identification of human heart proteoforms >50 kDa. Chapter 6 describes the integration of bottom-up peptide data and top-down proteoform data to improve proteoform identifications and to infer potential proteoform candidates with peptide-level evidence. Chapter 7 explains the research in this dissertation to a broader nonscientific audience. Finally, Chapter 8 discusses remaining challenges and future directions for integrated proteomic strategies towards the goal of comprehensive proteoform analysis.

This book constitutes the refereed proceedings of the 16th International Workshop on Algorithms in Bioinformatics, WABI 2016, held in Aarhus, Denmark. The 25 full papers together with 2 invited talks presented were carefully reviewed and selected from 54 submissions. The selected papers cover a wide range of topics from networks, tophylogenetic studies, sequence and genome analysis, comparative genomics, and mass spectrometry data analysis.

Proteins are critical actors within the cell, enabling complex biological processes essential for cell survival, development, and homeostasis. Generally, proteins function via their interactions with other biomolecules, such as nucleic acids, making knowledge of these interactions and the players involved essential to our understanding of even basic biological function. This dissertation describes the advancement of technologies for the identification of proteins interacting with target nucleic acid sequences (e.g., genomic loci or RNA transcripts), as well as the development of approaches for better understanding the diversity of proteins expressed in human cells. Chapter 2 presents the application of HyCCAPP (Hybridization Capture of Chromatin-Associated Proteins for Proteomics), a technology developed in the Smith lab for the study of protein-DNA interactions in a locus-specific manner, to identify the protein interactome of human centromeric alpha satellite DNA. We identified 90 proteins as enriched in alphoid chromatin, and this list included many known centromere-binding proteins in addition to multiple novel alpha satellite-binding proteins. This work represents the first application of the HyCCAPP technology in mammalian cells and is the first DNA-centric examination of human protein-alpha satellite interactions. In Chapter 4, we present a detailed and comprehensive guide for the application of HyPR-MS (Hybridization Purification of RNA-protein complexes followed by Mass Spectrometry), a technology developed in the Smith lab for the identification of proteins interacting with target RNA transcripts. It is our hope that the practical advice provided in this chapter will enable the widespread utilization of this technology. In Chapter 3, we explored how different types of proteomics data could be integrated to maximize proteoform identifications from a human cell line. Proteoforms are the specific molecular forms of proteins expressed in the cell, accounting for genetic variation, alternative splicing, and post-translational modifications. Through the integration of intact-mass, top-down, and bottom-up proteomics data, we were able to identify ~1,200 proteoforms representing 484 genes from the human Jurkat cell line. Finally, in Chapter 5, we present the current status of our work to combine proteoform analysis with HyPR-MS to enable the first ever study of the proteoforms bound to a target RNA transcript.