Relevant bibliographies by topics / Proteomics Peptides Bioinformatics. Tandem mass spectrometry

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Academic literature on the topic 'Proteomics Peptides Bioinformatics. Tandem mass spectrometry'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Proteomics Peptides Bioinformatics. Tandem mass spectrometry"

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Choi, Seunghyuk, and Eunok Paek. "MutCombinator: identification of mutated peptides allowing combinatorial mutations using nucleotide-based graph search." Bioinformatics 36, Supplement_1 (July 1, 2020): i203—i209. http://dx.doi.org/10.1093/bioinformatics/btaa504.

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Abstract Motivation Proteogenomics has proven its utility by integrating genomics and proteomics. Typical approaches use data from next-generation sequencing to infer proteins expressed. A sample-specific protein sequence database is often adopted to identify novel peptides from matched mass spectrometry-based proteomics; nevertheless, there is no software that can practically identify all possible forms of mutated peptides suggested by various genomic information sources. Results We propose MutCombinator, which enables us to practically identify mutated peptides from tandem mass spectra allowing combinatorial mutations during the database search. It uses an upgraded version of a variant graph, keeping track of frame information. The variant graph is indexed by nine nucleotides for fast access. Using MutCombinator, we could identify more mutated peptides than previous methods, because combinations of point mutations are considered and also because it can be practically applied together with a large mutation database such as COSMIC. Furthermore, MutCombinator supports in-frame search for coding regions and three-frame search for non-coding regions. Availability and implementation https://prix.hanyang.ac.kr/download/mutcombinator.jsp. Supplementary information Supplementary data are available at Bioinformatics online.
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Sulimov, Pavel, Anastasia Voronkova, and Attila Kertész-Farkas. "Annotation of tandem mass spectrometry data using stochastic neural networks in shotgun proteomics." Bioinformatics 36, no. 12 (March 24, 2020): 3781–87. http://dx.doi.org/10.1093/bioinformatics/btaa206.

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Abstract Motivation The discrimination ability of score functions to separate correct from incorrect peptide-spectrum-matches in database-searching-based spectrum identification is hindered by many superfluous peaks belonging to unexpected fragmentation ions or by the lacking peaks of anticipated fragmentation ions. Results Here, we present a new method, called BoltzMatch, to learn score functions using a particular stochastic neural networks, called restricted Boltzmann machines, in order to enhance their discrimination ability. BoltzMatch learns chemically explainable patterns among peak pairs in the spectrum data, and it can augment peaks depending on their semantic context or even reconstruct lacking peaks of expected ions during its internal scoring mechanism. As a result, BoltzMatch achieved 50% and 33% more annotations on high- and low-resolution MS2 data than XCorr at a 0.1% false discovery rate in our benchmark; conversely, XCorr yielded the same number of spectrum annotations as BoltzMatch, albeit with 4–6 times more errors. In addition, BoltzMatch alone does yield 14% more annotations than Prosit (which runs with Percolator), and BoltzMatch with Percolator yields 32% more annotations than Prosit at 0.1% FDR level in our benchmark. Availability and implementation BoltzMatch is freely available at: https://github.com/kfattila/BoltzMatch. Contact akerteszfarkas@hse.ru Supporting information Supplementary data are available at Bioinformatics online.
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He, Qianqian, Xinmei Fang, Tianhui Zhu, Shan Han, Hanmingyue Zhu, and Shujiang Li. "Differential Proteomics Based on TMT and PRM Reveal the Resistance Response of Bambusa pervariabilis × Dendrocalamopisis grandis Induced by AP-Toxin." Metabolites 9, no. 8 (August 10, 2019): 166. http://dx.doi.org/10.3390/metabo9080166.

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Bambusa pervariabilis McClure × Dendrocalamopsis grandis (Q.H.Dai & X.l.Tao ex Keng f.) Ohrnb. blight is a widespread and dangerous forest fungus disease, and has been listed as a supplementary object of forest phytosanitary measures. In order to study the control of B. pervariabilis × D. grandis blight, this experiment was carried out. In this work, a toxin purified from the pathogen Arthrinium phaeospermum (Corda) Elli, which causes blight in B. pervariabilis × D. grandis, with homologous heterogeneity, was used as an inducer to increase resistance to B. pervariabilis × D. grandis. A functional analysis of the differentially expressed proteins after induction using a tandem mass tag labeling technique was combined with mass spectrometry and liquid chromatography mass spectrometry in order to effectively screen for the proteins related to the resistance of B. pervariabilis × D. grandis to blight. After peptide labeling, a total of 3320 unique peptides and 1791 quantitative proteins were obtained by liquid chromatography mass spectrometry analysis. Annotation and enrichment analysis of these peptides and proteins using the Gene ontology and Kyoto Encyclopedia of Genes and Genomes databases with bioinformatics software show that the differentially expressed protein functional annotation items are mainly concentrated on biological processes and cell components. Several pathways that are prominent in the Kyoto Encyclopedia of Genes and Genomes annotation and enrichment include metabolic pathways, the citrate cycle, and phenylpropanoid biosynthesis. In the Protein-protein interaction networks four differentially expressed proteins-sucrose synthase, adenosine triphosphate-citrate synthase beta chain protein 1, peroxidase, and phenylalanine ammonia-lyase significantly interact with multiple proteins and significantly enrich metabolic pathways. To verify the results of tandem mass tag, the candidate proteins were further verified by parallel reaction monitoring, and the results were consistent with the tandem mass tag data analysis results. It is confirmed that the data obtained by tandem mass tag technology are reliable. Therefore, the differentially expressed proteins and signaling pathways discovered here is the primary concern for subsequent disease resistance studies.
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Karlsson, Roger, Annika Thorsell, Margarita Gomila, Francisco Salvà-Serra, Hedvig E. Jakobsson, Lucia Gonzales-Siles, Daniel Jaén-Luchoro, et al. "Discovery of Species-unique Peptide Biomarkers of Bacterial Pathogens by Tandem Mass Spectrometry-based Proteotyping." Molecular & Cellular Proteomics 19, no. 3 (January 15, 2020): 518–28. http://dx.doi.org/10.1074/mcp.ra119.001667.

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Mass spectrometry (MS) and proteomics offer comprehensive characterization and identification of microorganisms and discovery of protein biomarkers that are applicable for diagnostics of infectious diseases. The use of biomarkers for diagnostics is widely applied in the clinic and the use of peptide biomarkers is increasingly being investigated for applications in the clinical laboratory. Respiratory-tract infections are a predominant cause for medical treatment, although, clinical assessments and standard clinical laboratory protocols are time-consuming and often inadequate for reliable diagnoses. Novel methods, preferably applied directly to clinical samples, excluding cultivation steps, are needed to improve diagnostics of infectious diseases, provide adequate treatment and reduce the use of antibiotics and associated development of antibiotic resistance. This study applied nano-liquid chromatography (LC) coupled with tandem MS, with a bioinformatics pipeline and an in-house database of curated high-quality reference genome sequences to identify species-unique peptides as potential biomarkers for four bacterial pathogens commonly found in respiratory tract infections (RTIs): Staphylococcus aureus; Moraxella catarrhalis; Haemophilus influenzae and Streptococcus pneumoniae. The species-unique peptides were initially identified in pure cultures of bacterial reference strains, reflecting the genomic variation in the four species and, furthermore, in clinical respiratory tract samples, without prior cultivation, elucidating proteins expressed in clinical conditions of infection. For each of the four bacterial pathogens, the peptide biomarker candidates most predominantly found in clinical samples, are presented. Data are available via ProteomeXchange with identifier PXD014522. As proof-of-principle, the most promising species-unique peptides were applied in targeted tandem MS-analyses of clinical samples and their relevance for identifications of the pathogens, i.e. proteotyping, was validated, thus demonstrating their potential as peptide biomarker candidates for diagnostics of infectious diseases.
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Golenko, Ye, A. Ismailova, and Ye Rais. "PROTEIN IDENTIFICATION USING SEQUENCE DATABASES." Scientific Journal of Astana IT University, no. 4 (December 25, 2020): 14–23. http://dx.doi.org/10.37943/aitu.2020.91.98.002.

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The bottom-up proteomics approach (also known as the shotgun approach), based on the digestion of proteins in peptides and their sequencing using tandem mass spectrometry (MS/MS), has become widespread. The identification of peptides from the obtained MS/MS data is most often done using available sequence databases. In this paper, we present a detailed overview of the peptide identification workflow and description of the main protein bioinformatics databases. Choosing the correct search parameters and the sequence database is essential to the success of this method, and we pay special attention to the practical aspects of searching for efficient analysis of MS/MS spectra. We also consider possible reasons why database search tools cannot find the correct sequence for some MS/MS spectra, and highlight the issues of misidentification that can significantly reduce the value of published data. To help assess the assignment of peptides to MS/MS spectra, we will look at the scoring algorithms that are used in the most popular database search tools. We also analyze statistical methods and computational tools for validating peptide compliance with MS/MS data. The final part describes the process of determining the identity of protein samples from a list of peptide identifications and discusses the limitations of bottom-up proteomics
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Jabbour, Rabih E., Samir V. Deshpande, Mary Margaret Wade, Michael F. Stanford, Charles H. Wick, Alan W. Zulich, Evan W. Skowronski, and A. Peter Snyder. "Double-Blind Characterization of Non-Genome-Sequenced Bacteria by Mass Spectrometry-Based Proteomics." Applied and Environmental Microbiology 76, no. 11 (April 2, 2010): 3637–44. http://dx.doi.org/10.1128/aem.00055-10.

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ABSTRACT Due to the possibility of a biothreat attack on civilian or military installations, a need exists for technologies that can detect and accurately identify pathogens in a near-real-time approach. One technology potentially capable of meeting these needs is a high-throughput mass spectrometry (MS)-based proteomic approach. This approach utilizes the knowledge of amino acid sequences of peptides derived from the proteolysis of proteins as a basis for reliable bacterial identification. To evaluate this approach, the tryptic digest peptides generated from double-blind biological samples containing either a single bacterium or a mixture of bacteria were analyzed using liquid chromatography-tandem mass spectrometry. Bioinformatic tools that provide bacterial classification were used to evaluate the proteomic approach. Results showed that bacteria in all of the double-blind samples were accurately identified with no false-positive assignment. The MS proteomic approach showed strain-level discrimination for the various bacteria employed. The approach also characterized double-blind bacterial samples to the respective genus, species, and strain levels when the experimental organism was not in the database due to its genome not having been sequenced. One experimental sample did not have its genome sequenced, and the peptide experimental record was added to the virtual bacterial proteome database. A replicate analysis identified the sample to the peptide experimental record stored in the database. The MS proteomic approach proved capable of identifying and classifying organisms within a microbial mixture.
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Hu, Alex, William S. Noble, and Alejandro Wolf-Yadlin. "Technical advances in proteomics: new developments in data-independent acquisition." F1000Research 5 (March 31, 2016): 419. http://dx.doi.org/10.12688/f1000research.7042.1.

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The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low‑abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.
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Yang, Hao, Hao Chi, Wen-Feng Zeng, Wen-Jing Zhou, and Si-Min He. "pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework." Bioinformatics 35, no. 14 (July 2019): i183—i190. http://dx.doi.org/10.1093/bioinformatics/btz366.

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AbstractMotivationDe novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing.ResultsIn order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29–102% more correct spectra, and the precision was 11–89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21–50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences.Availability and implementationpNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html.Supplementary informationSupplementary data are available at Bioinformatics online.
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Li, Chuang, Kenli Li, Tao Chen, Yunping Zhu, and Qiang He. "SW-Tandem: a highly efficient tool for large-scale peptide identification with parallel spectrum dot product on Sunway TaihuLight." Bioinformatics 35, no. 19 (March 1, 2019): 3861–63. http://dx.doi.org/10.1093/bioinformatics/btz147.

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Abstract Summary Tandem mass spectrometry based database searching is a widely acknowledged and adopted method that identifies peptide sequence in shotgun proteomics. However, database searching is extremely computationally expensive, which can take days even weeks to process a large spectra dataset. To address this critical issue, this paper presents SW-Tandem, a new tool for large-scale peptide sequencing. SW-Tandem parallelizes the spectrum dot product scoring algorithm and leverages the advantages of Sunway TaihuLight, the No. 1 supercomputer in the world in 2017. Sunway TaihuLight is powered by the brand new many-core SW26010 processors and provides a peak computation performance greater than 100PFlops. To fully utilize the Sunway TaihuLights capacity, SW-Tandem employs three mechanisms to accelerate large-scale peptide identification, memory-access optimizations, double buffering and vectorization. The results of experiments conducted on multiple datasets demonstrate the performance of SW-Tandem against three state-of-the-art tools for peptide identification, including X!! Tandem, MR-Tandem and MSFragger. In addition, it shows high scalability in the experiments on extremely large datasets sized up to 12 GB. Availability and implementation SW-Tandem is an open source software tool implemented in C++. The source code and the parameter settings are available at https://github.com/Logic09/SW-Tandem. Supplementary information Supplementary data are available at Bioinformatics online.
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Mastronunzio, J. E., Y. Huang, and D. R. Benson. "Diminished Exoproteome of Frankia spp. in Culture and Symbiosis." Applied and Environmental Microbiology 75, no. 21 (September 11, 2009): 6721–28. http://dx.doi.org/10.1128/aem.01559-09.

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ABSTRACT Frankia species are the most geographically widespread gram-positive plant symbionts, carrying out N2 fixation in root nodules of trees and woody shrubs called actinorhizal plants. Taking advantage of the sequencing of three Frankia genomes, proteomics techniques were used to investigate the population of extracellular proteins (the exoproteome) from Frankia, some of which potentially mediate host-microbe interactions. Initial two-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis of culture supernatants indicated that cytoplasmic proteins appeared in supernatants as cells aged, likely because older hyphae lyse in this slow-growing filamentous actinomycete. Using liquid chromatography coupled to tandem mass spectrometry to identify peptides, 38 proteins were identified in the culture supernatant of Frankia sp. strain CcI3, but only three had predicted export signal peptides. In symbiotic cells, 42 signal peptide-containing proteins were detected from strain CcI3 in Casuarina cunninghamiana and Casuarina glauca root nodules, while 73 and 53 putative secreted proteins containing signal peptides were identified from Frankia strains in field-collected root nodules of Alnus incana and Elaeagnus angustifolia, respectively. Solute-binding proteins were the most commonly identified secreted proteins in symbiosis, particularly those predicted to bind branched-chain amino acids and peptides. These direct proteomics results complement a previous bioinformatics study that predicted few secreted hydrolytic enzymes in the Frankia proteome and provide direct evidence that the symbiosis succeeds partly, if not largely, because of a benign relationship.
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Dissertations / Theses on the topic "Proteomics Peptides Bioinformatics. Tandem mass spectrometry"

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Song, Zhao Xu Dong. "Bioinformatics methods for protein identification using peptide mass fingerprinting data." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6125.

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Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 16, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Dong Xu. Vita. Includes bibliographical references.
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Xu, Hua. "Novel data analysis methods and algorithms for identification of peptides and proteins by use of tandem mass spectrometry." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187113396.

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Tabb, David L. "Bioinformatics of proteomic tandem mass spectra : selection, characterization, and identification /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/10847.

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Granholm, Viktor. "The accuracy of statistical confidence estimates in shotgun proteomics." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-100769.

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High-throughput techniques are currently some of the most promising methods to study molecular biology, with the potential to improve medicine and enable new biological applications. In proteomics, the large scale study of proteins, the leading method is mass spectrometry. At present researchers can routinely identify and quantify thousands of proteins in a single experiment with the technique called shotgun proteomics. A challenge of these experiments is the computational analysis and the interpretation of the mass spectra. A shotgun proteomics experiment easily generates tens of thousands of spectra, each thought to represent a peptide from a protein. Due to the immense biological and technical complexity, however, our computational tools often misinterpret these spectra and derive incorrect peptides. As a consequence, the biological interpretation of the experiment relies heavily on the statistical confidence that we estimate for the identifications. In this thesis, I have included four articles from my research on the accuracy of the statistical confidence estimates in shotgun proteomics, how to accomplish and evaluate it. In the first two papers a new method to use pre-characterized protein samples to evaluate this accuracy is presented. The third paper deals with how to avoid statistical inaccuracies when using machine learning techniques to analyze the data. In the fourth paper, we present a new tool for analyzing shotgun proteomics results, and evaluate the accuracy of  its statistical estimates using the method from the first papers. The work I have included here can facilitate the development of new and accurate computational tools in mass spectrometry-based proteomics. Such tools will help making the interpretation of the spectra and the downstream biological conclusions more reliable.
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Nielsen, Michael Lund. "Characterization of Polypeptides by Tandem Mass Spectrometry Using Complementary Fragmentation Techniques." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7409.

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An, Zhenming. "New Peptide-pair Screening Strategy and Peptidylglycine a-Hydroxylating Monooxygenase (PHM) Based Enrichment Method for the Discovery of Novel a-Amidated Peptides." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3580.

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Peptide a-amidation is known as a signature of bioactivity due to the fact that half of the bioactive peptides found in the nervous and endocrine systems are a-amidated and that most known a-amidated peptides are bioactive. a-Amidated peptides are produced by the oxidative cleavage of glycine-extended precursors. Peptidylglycine a-amidating monooxygenase (PAM) is the only known enzyme responsible for catalyzing this reaction and its sole physiological function is to convert glycine extended prohormones to their a-amidated forms. High levels of PAM are found in certain tissues with no corresponding level of amidated products suggesting the presence of undiscovered a-amidated peptide hormones. Liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) has emerged as a powerful tool for peptide identification due to its advantages of speed, sensitivity and applicability to complex peptide mixtures. Normally, spectra are interpreted using database search engines. However, database searching is inefficient and ineffective for the identification of endogenous peptide with post-translational modifications (PTM) due to its low identification rate and high demand for computing power. There is a specific mass difference of 58.0055 units between an a-amidated peptide and its corresponding C-terminal glycine-extended precursor. The two peptides will have similar chromatographic retention time and MS/MS fragmentation patterns resulting from the identical amino acids sequences except for relatively the small differences at the C-termini. Based on this, a new LC-MS/MS based strategy for screening for a-amidated peptides was developed. This strategy depends on PAM inhibition and the mass accuracy of mass spectrometry (< 3 ppm). The coexistence of a-amidated peptides and their C-terminal glycine-extended precursors was insured by growing cells in the presence of a PAM inhibitor. After LC-MS/MS, masses and retention times of parent ions were extracted from raw data files and scanned by a script for peptide pairs with similar retention times and a mass difference around 58.0055. Resulting pairs were further validated by comparing their fragmentation patterns in MS/MS spectra. Only peptide pairs that met all three criteria were considered for further interpretation. This reduced the number of MS/MS spectra requiring interpretation by >99% and, thus, enable the manual inspection of MS/MS for the candidate peptide pairs. A total of 13 a-amidated peptides were successfully identified from cultured mouse pituitary AtT-20 cells using this method and a few of these newly identified a-amidated peptides exhibited bioactivity. The adaptability of this strategy to screening for other PTMs is also discussed. Peptidylglycine a-hydroxylating monooxygenase (PHM) is one of PAM domains which can be expressed separately. It is a copper dependent enzyme that catalyzes the first step of the two-step peptide amidation reaction. Removal of the copper ions results in the loss of enzyme catalytic activity. A PHM based a-amidated peptide enrichment method was developed. This method includes two steps. First, cells grown in culture were treated with a PAM inhibitor to effect the cellular accumulation of glycine-extended peptides. In the second step, copper-depleted PHM (apo-PHM) was used to selectively bind glycine-extended peptides present in the cell extract. All other unbound peptides were removed during wash runs. apo-PHM was then reinstated with copper to convert bound glycine-extended peptides to hydroxylated peptides and release them. Hydroxylated product can be converted to a-amidated peptide under basic conditions. Experiments carried out using model glycine extended peptides showed a 40 – 120-fold enrichment using HPLC-fluorometric assay or MALDI-TOF quantification. This method proved successful when working with complex samples like cell extracts. The relative intensity of a known a-amidated peptide mouse joining peptide (mJP) from an AtT-20 extract was dramatically increased after enrichment experiments.
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Han, Xi. "Effective Strategies for Improving Peptide Identification with Tandem Mass Spectrometry." Thesis, 2011. http://hdl.handle.net/10012/6413.

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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.
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LeDuc, Richard D. "Bioinformatics of high throughput proteomics using tandem mass spectrometry of intact proteins /." 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3290288.

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Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.
Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7046. Adviser: Gustavo Caetano-Anolles. Includes bibliographical references (leaves 117-129) Available on microfilm from Pro Quest Information and Learning.
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Froelich, Jennifer M. "Multistage tandem mass spectrometry strategies for the targeted analysis of oxidative protein modifications." Diss., 2008.

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Ma, Ze-Qiang. "IDPicker 2.0 protein assembly with high discrimination peptide identification filtering /." Diss., 2009. http://etd.library.vanderbilt.edu/available/etd-07142009-160837/.

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Books on the topic "Proteomics Peptides Bioinformatics. Tandem mass spectrometry"

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Proteome bioinformatics. New York, NY: Humana, 2010.

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Book chapters on the topic "Proteomics Peptides Bioinformatics. Tandem mass spectrometry"

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Bunkenborg, Jakob, and Rune Matthiesen. "Interpretation of Tandem Mass Spectra of Posttranslationally Modified Peptides." In Mass Spectrometry Data Analysis in Proteomics, 139–71. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-392-3_6.

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Bunkenborg, Jakob, and Rune Matthiesen. "Interpretation of Tandem Mass Spectra of Posttranslationally Modified Peptides." In Mass Spectrometry Data Analysis in Proteomics, 199–230. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9744-2_8.

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Eng, Jimmy K., Daniel B. Martin, and Ruedi Aebersold. "Tandem mass spectrometry database searching." In Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. Chichester: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/047001153x.g301204.

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Johnson, Richard S. "Interpreting tandem mass spectra of peptides." In Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. Chichester: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/047001153x.g301209.

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Eng, Jimmy K. "Tutorial on tandem mass spectrometry database searching." In Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. Chichester: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/047001153x.g301405.

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Delahunty, Claire M., and John R. Yates. "Multidimensional liquid chromatography tandem mass spectrometry for biological discovery." In Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. Chichester: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/047001153x.g301317.

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E., Irving, Dan Rittschof, Gary H., and Ian Musgrave. "Evolutionary Proteomics: Empowering Tandem Mass Spectrometry and Bioinformatics Tools for the Study of Evolution." In Tandem Mass Spectrometry - Applications and Principles. InTech, 2012. http://dx.doi.org/10.5772/31616.

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