Download or read book Machine Learning Approaches to Refining Post translational Modification Predictions and Protein Identifications from Tandem Mass Spectrometry written by Clement Chung and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Analysis of Protein Post Translational Modifications by Mass Spectrometry written by John R. Griffiths and published by John Wiley & Sons. This book was released on 2016-11-07 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers all major modifications, including phosphorylation, glycosylation, acetylation, ubiquitination, sulfonation and and glycation Discussion of the chemistry behind each modification, along with key methods and references Contributions from some of the leading researchers in the field A valuable reference source for all laboratories undertaking proteomics, mass spectrometry and post-translational modification research

Download or read book Novel Data Analysis Approaches for Cross linking Mass Spectrometry Proteomics and Glycoproteomics written by Lei Lu and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Bottom-up proteomics has emerged as a powerful technology for biological studies. The technique is used for a myriad of purposes, including among others protein identification, post-translational modification identification, protein-protein interaction analysis, protein quantification analysis, and protein structure analysis. The data analysis approaches of bottom-up proteomics have evolved over the past two decades, and many different algorithms and software programs have been developed for these varied purposes. In this thesis, I have focused on improving the database search strategies for the important special applications of bottom-up proteomics, including cross-linking mass spectrometry proteomics and O-glycoproteomics. In cross-linking mass spectrometry proteomics, a sample of proteins is treated with a chemical cross-linking reagent. This causes peptides within the proteins to be cross-linked to one another, forming peptide doublets that are released by treatment of the sample with a protease such as trypsin. The data analysis tools are designed to identify the cross-linked peptides. In O-glycoproteomics, the peptides that are released by protease digestion of the protein sample can be modified with any of or even multiple distinct O-glycans, and the data analysis tools should be able to identify all of the glycans and the modification sites at which they are located. In both cases, traditional database searching strategies which try to match the experimental spectra to all potential theoretical spectra is not practical due to the large increases in search space. Researchers suffered from a lack of efficient data analysis tools for these two applications. Here we successfully devised new search algorithms to address these problems, and impemented them in two new software modules in our laboratories' bottom-up software engine MetaMorpheus (Crosslinking data analysis via MetaMorpheusXL and O-glycoproteomics data analysis via O-Pair Search). The new search strategies used in the software program are both based on ion-indexed open search, which was first developed for large scale proteomic studies in the programs MSFragger and Open-pFind. The ion-indexed open search was optimized for cross-linking mass spectrometry proteomics and O-glycoproteomics in this study, and combined with other algorithms. In O-glycoproteomics, a graph-based algorithm is used to speed up the identification and localization of O-glycans. Other useful features have been added in the software program, such as enabling analysis of both cleavable cross-links and non-cleavable cross-links in the cross-link search module, and calculating localization probabilities in the O-glyco search module. Further optimizations including machine learning methods for false discovery rate (FDR) analysis, retention time prediction and spectral prediction could further improve the current best search approaches for cross-link proteomics and O-glycoproteomics data analysis. Chapter 1 provides an overview of bottom-up proteomics data analysis methods and outlines how ion-indexed open search could be useful for special bottom-up proteomics studies. Chapter 2 describes the development of a cross-linking mass spectrometry proteomics search module, resulting in efficiency improvements for both cleavable and non-cleavable cross-link proteomics data analysis. Chapter 3 describes the development of an O-glycoproteomics search module; by combining the ion-indexed open search algorithm with the graph-based localization algorithm, the O-pair Search is more than 2000 times faster than the currently widely used software program Byonic. In Chapter 4, a novel top-down data acquisition method is described. Chapter 5 provides conclusions and future directions.

Download or read book Novel Data Analysis Approaches for Cross linking Mass Spectrometry Proteomics and Glycoproteomics written by Lei Lu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bottom-up proteomics has emerged as a powerful technology for biological studies. The technique is used for a myriad of purposes, including among others protein identification, post-translational modification identification, protein-protein interaction analysis, protein quantification analysis, and protein structure analysis. The data analysis approaches of bottom-up proteomics have evolved over the past two decades, and many different algorithms and software programs have been developed for these varied purposes. In this thesis, I have focused on improving the database search strategies for the important special applications of bottom-up proteomics, including cross-linking mass spectrometry proteomics and O-glycoproteomics. In cross-linking mass spectrometry proteomics, a sample of proteins is treated with a chemical cross-linking reagent. This causes peptides within the proteins to be cross-linked to one another, forming peptide doublets that are released by treatment of the sample with a protease such as trypsin. The data analysis tools are designed to identify the cross-linked peptides. In O-glycoproteomics, the peptides that are released by protease digestion of the protein sample can be modified with any of or even multiple distinct O-glycans, and the data analysis tools should be able to identify all of the glycans and the modification sites at which they are located. In both cases, traditional database searching strategies which try to match the experimental spectra to all potential theoretical spectra is not practical due to the large increases in search space. Researchers suffered from a lack of efficient data analysis tools for these two applications. Here we successfully devised new search algorithms to address these problems, and impemented them in two new software modules in our laboratories' bottom-up software engine MetaMorpheus (Crosslinking data analysis via MetaMorpheusXL and O-glycoproteomics data analysis via O-Pair Search). The new search strategies used in the software program are both based on ion-indexed open search, which was first developed for large scale proteomic studies in the programs MSFragger and Open-pFind. The ion-indexed open search was optimized for cross-linking mass spectrometry proteomics and O-glycoproteomics in this study, and combined with other algorithms. In O-glycoproteomics, a graph-based algorithm is used to speed up the identification and localization of O-glycans. Other useful features have been added in the software program, such as enabling analysis of both cleavable cross-links and non-cleavable cross-links in the cross-link search module, and calculating localization probabilities in the O-glyco search module. Further optimizations including machine learning methods for false discovery rate (FDR) analysis, retention time prediction and spectral prediction could further improve the current best search approaches for cross-link proteomics and O-glycoproteomics data analysis. Chapter 1 provides an overview of bottom-up proteomics data analysis methods and outlines how ion-indexed open search could be useful for special bottom-up proteomics studies. Chapter 2 describes the development of a cross-linking mass spectrometry proteomics search module, resulting in efficiency improvements for both cleavable and non-cleavable cross-link proteomics data analysis. Chapter 3 describes the development of an O-glycoproteomics search module; by combining the ion-indexed open search algorithm with the graph-based localization algorithm, the O-pair Search is more than 2000 times faster than the currently widely used software program Byonic. In Chapter 4, a novel top-down data acquisition method is described. Chapter 5 provides conclusions and future directions.

Download or read book Characterization and Identification of Protein Posttranslational Modifications Using Protein Enrichment and Mass Spectrometry written by Liwen Wang and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This dissertation describes a proteomic workflow for the analysis of protein post-translational modifications (PTMs). The workflow combines the techniques for protein enrichment, multi-dimensional separations, mass spectrometry (MS) and automatic data analysis. The workflow was developed to improve the application of proteomic analysis in the realms of biomarker discovery and experimental therapeutic research. Chapter 2 presents an immunoaffinity chromatography method that was developed to enrich acetylated histones. A self-packed immunoaffinity capillary column was developed using commercial antibodies that could be recycled and used for on-line and off-line enrichment. The acetylated fractions were collected and identified by Matrix Assisted Laser Desorption (MALDI) MS and electrospray ionization (ESI) liquid chromatography tandem mass spectrometry (LC-MS/MS). In chapter 3 an optimized phosphoproteomic analysis workflow based on phosphopeptide enrichment, data-dependant neutral loss mass spectrometry and a novel hierarchical database searching is described. The combination of these approaches improved the confidence of phosphopeptide identifications. Chapter 4 describes the use of phosphoprotein enrichment and a tandem phosphoprotein and phosphopeptide enrichment to improve the identification of phosphoproteins and localization of the phosphorylation sites. Purification of global phosphoproteins from primary CLL B-cells was conducted by use of PhosTag Zn2 enrichment strategy at neutral pH. SDS-PAGE gel was used to separate the purified phosphoprotein fraction and Pro-Q diamond staining was employed to visualize those phosphoprotein bands. Shot-gun proteomic analysis was then performed to identify all the enriched phosphoproteins in the gel. Phosphopeptide enrichment was used in tandem to map phosphorylation sites of the enriched phosphoproteins. Chapter 5 describes the identification of tyrosine phosphoproteins associated with immunotherapy of malignant cells with the small modular immunopharmaceutical targeted against CD37 (CD37-SMIPTM). This drug induces apoptosis and antibody-dependent cellular cytotoxicity (ADCC) in primary Chronic Lymphocyte Leukemia (CLL) cells. Tyrosine phosphorylation of proteins was investigated as an early activation event for the cytotoxicity. Immunoprecipitation was used to purify the phosphotyrosine proteins from treated cell lysate and untreated cell lysate. Detection of modulation of tyrosine phosphorylation and identification of those tyrosine phosphoproteins after treatment by proteomic approaches revealed proteins associated with the signaling pathway activated by immunotherapy. Chapter 6 describes a direct application of the proteomic platform developed in Chapter 3 combined with LC-MS protein profiling. The modulation of histone phosphorylation isoforms induced by various chemotherapy drugs was detected by LC-MS screening. We detected the dephosphorylation of histones H1 and hyperphosphorylation of H2A.X associated with the different drug treatments.

Download or read book Computational Methods for Mass Spectrometry Proteomics written by Ingvar Eidhammer and published by John Wiley & Sons. This book was released on 2008-02-28 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteomics is the study of the subsets of proteins present in different parts of an organism and how they change with time and varying conditions. Mass spectrometry is the leading technology used in proteomics, and the field relies heavily on bioinformatics to process and analyze the acquired data. Since recent years have seen tremendous developments in instrumentation and proteomics-related bioinformatics, there is clearly a need for a solid introduction to the crossroads where proteomics and bioinformatics meet. Computational Methods for Mass Spectrometry Proteomics describes the different instruments and methodologies used in proteomics in a unified manner. The authors put an emphasis on the computational methods for the different phases of a proteomics analysis, but the underlying principles in protein chemistry and instrument technology are also described. The book is illustrated by a number of figures and examples, and contains exercises for the reader. Written in an accessible yet rigorous style, it is a valuable reference for both informaticians and biologists. Computational Methods for Mass Spectrometry Proteomics is suited for advanced undergraduate and graduate students of bioinformatics and molecular biology with an interest in proteomics. It also provides a good introduction and reference source for researchers new to proteomics, and for people who come into more peripheral contact with the field.

Download or read book Expanding the Toolbox of Tandem Mass Spectrometry with Algorithms to Identify Mass Spectra from More Than One Peptide written by Jian Wang and published by . This book was released on 2013 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In several areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass (MS/MS) spectra. While there are numerous situations where two or more peptides are co-fragmented in the same MS/MS spectrum, nearly all mainstream computational approaches still make the ubiquitous assumption that each MS/MS spectrum comes from only one peptide. In this thesis we addressed problems in three emerging areas where computational tools that relax the above assumption are crucial for the success application of these approaches on a large-scale. In the first chapter we describe algorithms for the identification of mixture spectra that are from more than one co-eluting peptide precursors. The ability to interpret mixture spectra not only improves peptide identification in traditional data-dependent-acquisition (DDA) workflows but is also crucial for the success application of emerging data-independent-acquisition (DIA) techniques that have the potential to greatly improve the throughput of peptide identification. In chapter two, we address the problem of identification of peptides with complex post-translational modification (PTM). Detection of PTMs is important to understand the functional dynamics of proteins. Complex PTMs resulted from the conjugation of another macromolecule onto the substrate protein. The resultant modified peptides not only generate spectrum that contains a mixture of fragment ions from both the PTM and the substrate peptide but they also display substantially different fragmentation patterns as compared to conventional, unmodified peptides. We describe a hybrid experimental and computational approach to build search tools that capture the specific fragmentation patterns of modified peptides. Finally in chapter three we address the problem of identification of linked peptides. Linked peptides are two peptides that are covalently linked together. The generation and identification of linked peptides has recently been demonstrated to be a versatile tool to study protein-protein interactions and protein structures, however the identification of linked peptides face many challenges. We integrate lessons learned in the previous chapters to build an efficient and sensitive tool to identify linked peptides from MS/MS spectra.

Download or read book Tr s humbles et tr s respectueuses remontrances du second ordre du clerg au roi au sujet du vingti me written by and published by . This book was released on 1844 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book New Methods for the Analysis of Noncovalent Biological Complexes and Protein Posttranslational Modifications written by Yonghao Yu and published by . This book was released on 2006 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Penalty Based Dynamic Programming for the Identification of Post Translational Modifications in Peptide Mass Spectra written by Laurence Elliot Bernstein and published by . This book was released on 2018 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tandem mass spectrometry (MS/MS) has long been the leading method of identifying peptides and proteins in complex biological samples and many algorithms have been created for this purpose. Many of the methods for searching MS/MS spectra against a database of known proteins must restrict the number of post-translational modifications (PTMs) that they can identify because the larger the number of PTMs being considered, the larger the search space, which in turn increases both computational complexity and the potential for false matches. In addition these algorithms cannot discover new peptides or homologues or be used with species for which a protein database does not exist. Newer algorithms have been developed that perform "open" or "blind" searches capable of finding any possible modifications, however these methods increase the search space even further, often resulting in lower performance and the generation of many putative modification masses that must be sifted through manually to determine which are real. To address the shortcomings of the existing methods, we created a new blind database search algorithm based on spectral networks. Our method uses a modification of the standard spectral tagging filtration techniques tailored for contig-consensus spectra generated from spectral networks, along with, the first of its kind, penalty-based, dynamic programming spectrum-database alignment algorithm that is able to accurately to identify both a priori specified modifications as well as novel PTMs. We then developed a workflow based on these new techniques that combines previous work in clustering, spectral alignment, spectral networks, and multi-spectral assembly. Because our new algorithm only identifies spectra that lie within the spectral networks, we created a workflow, called RaVen, that merged our method with MS-GF+ and combines the results from both methods resulting in a method with massive improvement in overall identification rates above existing methods while at the same time identifying many more rare modifications in samples. We also propose an improved way of measuring the accuracy of blind search algorithms: "peptide variants" which better meet captures the goals of blind search methods and does not rely on precise localization of modifications (which is very difficult to achieve for most algorithms).

Download or read book Machine Learning for Protein Subcellular Localization Prediction written by Shibiao Wan and published by Walter de Gruyter GmbH & Co KG. This book was released on 2015-05-19 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction.

Download or read book Effective Strategies for Improving Peptide Identification with Tandem Mass Spectrometry written by Han, Xi and published by . This book was released on 2011 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tandem mass spectrometry (MS/MS) has been routinely used to identify peptides from protein mixtures in the field of proteomics. However, only about 30% to 40% of current MS/MS spectra can be identified, while many of them remain unassigned, even though they are of reasonable quality. The ubiquitous presence of post-translational modifications (PTMs) is one of the reasons for current low spectral identification rate. In order to identify post-translationally modified peptides, most existing software requires the specification of a few possible modifications. However, such knowledge of possible modifications is not always available. In this thesis, we describe a new algorithm for identifying modified peptides without requiring users to specify the possible modifications before the search routine; instead, all modifications from the Unimod database are considered. Meanwhile, several new techniques are employed to avoid the exponential growth of the search space, as well as to control the false discoveries due to this unrestricted search approach. A software tool, PeaksPTM, has been developed and it has already achieved a stronger performance than competitive tools for unrestricted identification of post-translationally modified peptides. Another important reason for the failure of the search tools is the inaccurate mass or charge state measurement of the precursor peptide ion. In this thesis, we study the precursor mono-isotopic mass and charge determination problem, and propose an algorithm to correct precursor ion mass error by assessing the isotopic features in its parent MS spectrum. The algorithm has been tested on two annotated data sets and achieved almost 100 percent accuracy. Furthermore, we have studied a more complicated problem, the MS/MS preprocessing problem, and propose a spectrum deconvolution algorithm. Experiments were provided to compare its performance with other existing software.

Download or read book Computational Methods for Protein protein Complex Structure Prediction and Mass Spectrometry based Identification written by Weiwei Tong and published by . This book was released on 2008 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Nearly all major processes in living cells are carried out by complex apparatus consisting of protein molecules. This thesis describes computational tools developed to help investigate two fundamental questions about proteins that underlie cell functions: how they interact with each other and form complex structures; and how they are expressed and modified in different cell states. In order to address the first question, several methods are developed to predict protein-protein complex structures. Protein interactions are energy driven processes. The prediction of protein complex structures is the search for the global minimum on the binding free-energy landscape. An approach is described that uses Van der Wools energy, desolvation energy and shape complementarity as the scoring functions and a five-dimensional fast Fourier transform algorithm to expedite the search. Two methods to screen and optimize the predicted protein complex structures are also introduced. They incorporate additional energy terms and clustering algorithms to provide more precise estimations of the binding free-energy. The same methods can also be used to predict hot spots, the mutations of which significantly alter the binding kinetics. To study the protein expression profiles, a two-step approach for protein identification using peptide mass fingerprinting data is developed. Peptide mass fingerprinting uses peptide masses determined by mass spectrometry to identify the peptides and subsequently, the proteins in the sample Peaks in the mass spectrum are associated with known peptide sequences in the database based on log-likelihood ratio test. A statistical algorithm is then used to identify proteins by comparing the probability of each protein's presence in the sample, given the peak assignments with the background probability. This method also discovers post-translational modifications in the identified proteins. The protein binding prediction program successfully predicts protein complex structures that closely resemble their native forms, as observed by x-ray crystallography or NMR. The refinements and hot spot predictions also give accurate and consistent results. The database search program that interprets mass spectrometry data is evaluated with artificial and experimental data. The program identifies proteins in the sample with high sensitivity and specificity. The results presented in this thesis demonstrate that computational methods help to better understand the structure and the composition of the protein machineries. All of the methods described herein have been implemented and made available for the research community over the Internet.

Download or read book Development and Application of Mass Spectrometry Methods for Proteomic and Post translational Modification Analysis written by Danqing Wang (Ph.D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteins are essential biomolecules that perform a wide range of biological functions. Post-translational modifications (PTMs) substantially impact protein structure and function, making their characterization essential for understanding complex biological systems. This dissertation focuses on developing and applying novel mass spectrometry (MS)-based methodologies to address challenges in studying two common and important PTMs: phosphorylation and glycosylation. To this end, new enrichment materials and their corresponding workflows, including Cotton Ti-IMAC, epoxy-ATP-Ti4+ IMAC, and Very Weak Anion Exchange (VWAX) have been introduced for efficient phosphopeptide and glycopeptide enrichment. A strategy combining boronic acid enrichment, high-pH fractionation, and EThcD has been developed for comprehensive O-glycosylation profiling. Additionally, the Boost-DiLeu quantitative approach has been introduced to enhance glycopeptide quantification in size-limited samples, while a periodate oxidation-based SUGAR tag labeling method has been established for high-throughput, intact sialylated glycopeptide-specific quantification. These methods have been applied to study human diseases, such as Alzheimer's Disease, providing insights into dysregulated glycosylation patterns and their potential implications in disease pathogenesis. Overall, this work contributes to advancing MS-based proteomics strategies and broadening our understanding of the roles of PTMs in biological systems and is anticipated to inspire future research endeavors in related fields.

Download or read book Development and Application of Mass Spectrometry Methods for Proteomic and Post translational Modification Analysis written by Danqing Wang (Ph.D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteins are essential biomolecules that perform a wide range of biological functions. Post-translational modifications (PTMs) substantially impact protein structure and function, making their characterization essential for understanding complex biological systems. This dissertation focuses on developing and applying novel mass spectrometry (MS)-based methodologies to address challenges in studying two common and important PTMs: phosphorylation and glycosylation. To this end, new enrichment materials and their corresponding workflows, including Cotton Ti-IMAC, epoxy-ATP-Ti4+ IMAC, and Very Weak Anion Exchange (VWAX) have been introduced for efficient phosphopeptide and glycopeptide enrichment. A strategy combining boronic acid enrichment, high-pH fractionation, and EThcD has been developed for comprehensive O-glycosylation profiling. Additionally, the Boost-DiLeu quantitative approach has been introduced to enhance glycopeptide quantification in size-limited samples, while a periodate oxidation-based SUGAR tag labeling method has been established for high-throughput, intact sialylated glycopeptide-specific quantification. These methods have been applied to study human diseases, such as Alzheimer's Disease, providing insights into dysregulated glycosylation patterns and their potential implications in disease pathogenesis. Overall, this work contributes to advancing MS-based proteomics strategies and broadening our understanding of the roles of PTMs in biological systems and is anticipated to inspire future research endeavors in related fields.

Download or read book New Tools for Chemically Directed Proteomics written by Austin Arlo Pitcher and published by . This book was released on 2010 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, mass spectrometry has become a staple technique in biochemistry and molecular biology, with mass spectrometry based proteomics being one of its greatest successes. The standard method for determining protein identifications though the use of mass spectrometry involves a number of steps. First, a solution of whole proteins is digested with a protease, typically trypsin. The resulting peptides are then separated by liquid chromatography, and a full scan mass spectrum is obtained for each eluting fraction. As ions themselves produce little information that can be used to determine protein identifications, peptide ions are then selected for secondary fragmentation and a tandem mass spectrum is obtained. From this secondary spectrum, peptide sequence information can be obtained after comparison to proteome databases. However, despite being a powerful tool for peptide identification, the traditional shotgun proteomics approach often suffers from limited sensitivity and a lack of reproducibility between replicate analyses. A major source of these limitations is due to the way in which ions are chosen for fragmentation. As unmodified peptide ions are virtually indistinguishable in a full scan mass spectrum, the vast majority of experiments select ions for fragmentation based solely on the signal intensity of each ion. In complex samples, this has the often undesired consequence of biasing the search towards the most abundant, though often uninteresting peptides. Furthermore, due to the stochastic nature of ion selection, it is often difficult to reproduce a list of protein identifications even if the same biological sample is used for multiple experiments. This dissertation focuses on the idea of using chemical tagging strategies to introduce information into a complex sample that can then be used to direct MS analysis away from the most abundant species and towards those most likely to be interesting in a given biological context. The technology developed is then applied to the study of protein glycosylation, a type of protein post-translational modification ubiquitous in eukaryotic organisms. In Chapter 1, current technologies for studying glycoproteins using mass spectrometry are surveyed. The emphasis in this chapter is on the use of unnatural sugar substrates for the metabolic engineering of glycan structures, and applications of metabolic engineering to glycoproteomics. This chapter also reviews the use of bioorthogonal reactions in the context of glycoproteomics. Finally, the standard workflow for proteomics experiments is examined and the concept of directed mass spectrometry is introduced. Chapter 2 proposes a method for using chemistry to add information to a biological system which can then be used to direct the MS analysis of biomolecules to bias analysis towards a subset of so-called ̀̀information-rich'' ions. This system uses the distinctive isotopic distribution of a chemical label to perturb the isotopic envelope of a biomolecule in a way that is detectable in a full-scan mass spectrum. Coupled with a computational algorithm described in Chapter 3, we term this methodology the IsoStamp system. The isotopic pattern searching algorithm introduced relies on the ability to accurately predict the isotopic envelope of a peptide solely from the molecular weight of the ion. Such a system is analyzed in Chapter 4, and the scope is extended to applications in glycobiology including the prediction of isotopic envelopes of biomolecules such as mucins, where a large percentage of the molecular weight is attributed to carbohydrate content. Potential weaknesses of current metabolic oligosaccharide engineering techniques as they are employed in mass spectrometry is that they typically require a secondary labeling step, and that unnatural sugars may not be incorporated into glycan structures at stoichiometric levels. Chapter 5 introduces an alternative approach whereby an isotopically labeled mixture of a natural substrate, GlcNAc, is fed to cells and is subsequently incorporated into N-glycan structures at stoichiometric levels. N-glycosylated peptides are then targeted for MS/MS analysis based on their isotopic distribution, and sites of modification are determined by comparison to a proteome database. Finally, Chapter 6 examines the future of isotopic labeling in biological mass spectrometry, suggesting a number of applications of the IsoStamp technology.

Download or read book Studies of Protein Post translational Modifications Using High Resolution Tandem Mass Spectrometry written by Huilin Li and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: