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Relevant bibliographies by topics / Peptide mass fingerprinting

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Academic literature on the topic 'Peptide mass fingerprinting'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Peptide mass fingerprinting"

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Thiede, Bernd, Wolfgang Höhenwarter, Alexander Krah, Jens Mattow, Monika Schmid, Frank Schmidt, and Peter R. Jungblut. "Peptide mass fingerprinting." Methods 35, no. 3 (March 2005): 237–47. http://dx.doi.org/10.1016/j.ymeth.2004.08.015.

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He, Z., C. Yang, and W. Yu. "Peak bagging for peptide mass fingerprinting." Bioinformatics 24, no. 10 (April 7, 2008): 1293–99. http://dx.doi.org/10.1093/bioinformatics/btn123.

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Damodaran, Senthilkumar, Troy D. Wood, Priyadharsini Nagarajan, and Richard A. Rabin. "Evaluating Peptide Mass Fingerprinting-based Protein Identification." Genomics, Proteomics & Bioinformatics 5, no. 3-4 (2007): 152–57. http://dx.doi.org/10.1016/s1672-0229(08)60002-9.

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Alty, Lisa T., and Frederick J. LaRiviere. "Peptide Mass Fingerprinting of Egg White Proteins." Journal of Chemical Education 93, no. 4 (February 9, 2016): 772–77. http://dx.doi.org/10.1021/acs.jchemed.5b00625.

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van Ginkel, Jetty, Mike Filius, Malwina Szczepaniak, Pawel Tulinski, Anne S. Meyer, and Chirlmin Joo. "Single-molecule peptide fingerprinting." Proceedings of the National Academy of Sciences 115, no. 13 (March 12, 2018): 3338–43. http://dx.doi.org/10.1073/pnas.1707207115.

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Proteomic analyses provide essential information on molecular pathways of cellular systems and the state of a living organism. Mass spectrometry is currently the first choice for proteomic analysis. However, the requirement for a large amount of sample renders a small-scale proteomics study challenging. Here, we demonstrate a proof of concept of single-molecule FRET-based protein fingerprinting. We harnessed the AAA+ protease ClpXP to scan peptides. By using donor fluorophore-labeled ClpP, we sequentially read out FRET signals from acceptor-labeled amino acids of peptides. The repurposed ClpXP exhibits unidirectional processing with high processivity and has the potential to detect low-abundance proteins. Our technique is a promising approach for sequencing protein substrates using a small amount of sample.

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Gay, Steven, Pierre-Alain Binz, Denis F. Hochstrasser, and Ron D. Appel. "Modeling peptide mass fingerprinting data using the atomic composition of peptides." Electrophoresis 20, no. 18 (December 1, 1999): 3527–34. http://dx.doi.org/10.1002/(sici)1522-2683(19991201)20:18<3527::aid-elps3527>3.0.co;2-9.

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Bogdan, IstvÁn A., Daniel Coca, and Rob J. Beynon. "Peptide Mass Fingerprinting Using Field-Programmable Gate Arrays." IEEE Transactions on Biomedical Circuits and Systems 3, no. 3 (June 2009): 142–49. http://dx.doi.org/10.1109/tbcas.2008.2010945.

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Henzel, William J., Colin Watanabe, and John T. Stults. "Protein identification: The origins of peptide mass fingerprinting." Journal of the American Society for Mass Spectrometry 14, no. 9 (September 2003): 931–42. http://dx.doi.org/10.1016/s1044-0305(03)00214-9.

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Pappin, D. J. C., P. Hojrup, and A. J. Bleasby. "Rapid identification of proteins by peptide-mass fingerprinting." Current Biology 3, no. 6 (June 1993): 327–32. http://dx.doi.org/10.1016/0960-9822(93)90195-t.

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Chernobrovkin, A. L., O. P. Trifonova, N. A. Petushkova, E. A. Ponomarenko, and A. V. Lisitsa. "Selection of the peptide mass tolerance value for protein identification with peptide mass fingerprinting." Russian Journal of Bioorganic Chemistry 37, no. 1 (January 2011): 119–22. http://dx.doi.org/10.1134/s1068162011010055.

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Dissertations / Theses on the topic "Peptide mass fingerprinting"

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Kaltenbach, Hans-Michael. "Statistics and algorithms for peptide mass fingerprinting." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=983903395.

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Ganapathy, Ashwin. "Computational analysis of protein identification using peptide mass fingerprinting approach /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1426056.

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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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Brown, Samantha [Verfasser]. "Identifying hominin remains in Siberia using peptide mass fingerprinting (ZooMS) / Samantha Brown." Tübingen : Universitätsbibliothek Tübingen, 2021. http://d-nb.info/1240673051/34.

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Jain, Rachana. "Novel Computational Methods for Mass Spectrometry based Protein Identification." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1266601291.

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Tummala, Manorama. "Surfactant-Aided Matrix Assisted Laser Desorption/Ionization Mass Spectrometry (SA-MALDI MS)." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1100672049.

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Jain, Parul. "Characterizing Interactions between Chromophores in Synthetic and Natural Macromolecular Films via MALDI-TOF, IBF and Dielectric Analyzer." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4512.

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With the emergence of Matrix Assisted Laser Desorption/Ionization-Time-of-flight as a tool for diagnosis of diseases via proteomics, there is an increasing need for greater sensitivity. Analysis of peptides by MALDI-TOF-MS is affected by sample formulation and spotting onto a MALDI target. This dissertation investigates a novel MALDI sample preparation technique, Induction Based Fluidics (IBF), for depositing precise volumes (pL to nL) of samples onto the target. We have seen that while using IBF, the induced electric field accompanying deposition enhances matrix crystallization yielding smaller crystals with more homogeneity, as compared to conventional manual micropipette (MP) depositions. An investigation of the signal-to-noise (S/N) for IBF deposition of tryptic digested Bovine Serum Albumin (BSA) showed a significant improvement in the signal-to-noise ratio for 0.5 and 0.25 pmol/µL BSA sample compared to equivalent MP depositions. The S/N enhancement for IBF and MP depositions of BSA were studied using à-cyano-4-hydroycinnamic acid (CHCA) and 2,5-dihydroxybenzoic acid (DHB) matrices, and CHCA showed better results than DHB . The exciting results obtained by IBF prompted us to probe sample morphology more fully and to relate morphology to the detections level and hopefully, to increase the utility of MALDI-TOF-MS for detection of a larger range of peptides. Morphology results were correlated to sensitivity limits using both dispensing techniques. Because of dissimilar rates of evaporation, different or uneven deposition thickness, or crystal lattice morphology, discontinuous crystallization patterns were observed for MP depositions. However, IBF deposited samples occupied less planar area with uniform distribution of crystals, thereby reducing sample crystal heterogeneity and laborious hunt for a "sweet" or "hot" spot to produce high quality spectra. The application of IBF was extended to the tryptic digested BSA protein using peptide mass fingerprinting. IBF deposition resulted in a larger number of detectable peptides as well as higher sequence coverage as compared to equivalent MP depositions. In last few decades, advanced research and potential applications in the field of microelectronics have spurred interest in the development of reticulated doped polymer films. Bis (ethylenedioxy) tetrathiafulvalene (BEDO-TTF)/Polycarbonate (PC) films were synthesized and characterized for use in hand-held real time explosives sensors, capable of detecting nitro-based compounds (nitroaromatics, nitoamines and nitroesters), which are the main components of Improvised Explosive Devices or IEDs. Reticulated doped polymer films were prepared by exposing solid solutions of BEDO-TTF in PC to iodine to form conductive charge transfer complexes. The resulting films exhibited room temperature conductivities ranging from 6.33-90.4*10-5 S cm-1. The colored iodine complexes in the film were reduced by cyclic voltammetry yielding conductive, colorless, transparent films. Dielectric analysis (DEA) was used to probe relaxations in neat PC and BEDO-TTF/PC showed that BEDO-TTF plasticized the PC and decreased the glass transition temperature. Two secondary relaxations appeared in PC films, whereas the transitions merged in the doped film. DEA also revealed conductivity relaxations above 180°C, which were characterized by the electric modulus formalism and showed that BEDO-TTF increased the alternating current, (AC) conductivity in PC.

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Palmblad, Magnus. "Identification and Characterization of Peptides and Proteins using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry." Doctoral thesis, Uppsala universitet, Institutionen för materialvetenskap, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-1999.

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Mass spectrometry has in recent years been established as the standard method for protein identification and characterization in proteomics with excellent intrinsic sensitivity and specificity. Fourier transform ion cyclotron resonance is the mass spectrometric technique that provides the highest resolving power and mass accuracy, increasing the amount of information that can be obtained from complex samples. This thesis concerns how useful information on proteins of interest can be extracted from mass spectrometric data on different levels of protein structure and how to obtain this data experimentally. It was shown that it is possible to analyze complex mixtures of protein tryptic digests by direct infusion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry and identify abundant proteins by peptide mass fingerprinting. Coupling on-line methods such as liquid chromatography and capillary electrophoresis increased the number of proteins that could be identified in human body fluids. Protein identification was also improved by novel statistical methods utilizing prediction of chromatographic behavior and the non-randomness of enzymatic digestion. To identify proteins by short sequence tags, electron capture dissociation was implemented, improved and finally coupled on-line to liquid chromatography for the first time. The combined techniques can be used to sequence large proteins de novo or to localize and characterize any labile post-translational modification. New computer algorithms for the automated analysis of isotope exchange mass spectra were developed to facilitate the study of protein structural dynamics. The non-covalent interaction between HIV-inhibitory peptides and the oligomerization of amyloid β-peptides were investigated, reporting several new findings with possible relevance for development of anti-HIV drug therapies and understanding of fundamental mechanisms in Alzheimer’s disease.

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Xie, Jing. "Establishment of a two dimensional electrophoresis map of human mitochondrial proteins." Doctoral thesis, [S.l.] : [s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=969847734.

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al-hawri, eman. "A computational tool for peptide mass fingerprinting." Master's thesis, 2013. http://hdl.handle.net/10400.1/7764.

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Dissertação de mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2013
Protein identification using Mass Spectrometry (MS) is essential in the study of proteomics. Two popular techniques are used in the identification: Tandem Mass Spectrometry (MS/MS) and Peptide Mass Fingerprinting (PMF), which is considered in this work. PMF is widely used in the proteomics field. It is faster and more economic when compared to MS/MS. This work focuses on the development of a computational tool for protein identification using PMF data. The main objective for any PMF tool is to identify the correct protein (if it exists) by searching a peak list, produced by MS, against a protein database. However, one of the great challenges to these tools is related to the size of the databases that result in many random matches. In fact, the main difference between these tools is the scoring method which is responsible of minimizing these random matches. Therefore, a review of PMF tools and their scoring methods is presented and discussed. There are many tools on the Internet (both commercial or academic) for PMF protein identification using public databases. These tools do not offer a locally installable version, and do not allow the use of in-house databases, a feature that is of great importance to biologists who work on non-model systems. In contrast, the tool developed in this work is free, can be installed locally, and can be used with both public and local databases. Additionally, it supports different sorts of protein modifications and contaminants suppression, features that are not available by some of the existing tools. A new scoring method is proposed and incorporated in the proposed tool. The proposed tool is compared with two of the most popular software packages (commercial and academic), showing a good accuracy and being very competitive with the most popular and robust commercial software (Mascot). The developed prototype is platform-independent and is very easy to install. To allow users to work and interact with the system in an easy-to-use environment, a friendly graphical user interface is developed to allow them to manage their files very efficiently. In addition, it can work with single or multiple query files to support different work scales. The features this new tool offers make it an important assist to the biological laboratories concerning the PMF task.

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Book chapters on the topic "Peptide mass fingerprinting"

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Webster, Judith, and David Oxley. "Peptide Mass Fingerprinting." In Methods in Molecular Biology™, 227–40. Totowa, NJ: Humana Press, 2005. http://dx.doi.org/10.1007/978-1-59259-948-6_16.

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Dainese, Paola, and Peter James. "Protein Identification by Peptide-Mass Fingerprinting." In Proteome Research: Mass Spectrometry, 103–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56895-4_6.

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Eidhammer, Ingvar, Harald Barsnes, and Svein-Ole Mikalsen. "MassSorter: Peptide Mass Fingerprinting Data Analysis." In Functional Proteomics, 345–59. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-398-1_23.

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Aitken, Alastair. "Protein Identification by Peptide Mass Fingerprinting." In The Proteomics Protocols Handbook, 355–65. Totowa, NJ: Humana Press, 2005. http://dx.doi.org/10.1385/1-59259-890-0:355.

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Pappin, Darryl J. C. "Peptide Mass Fingerprinting Using MALDI-TOF Mass Spectrometry." In Protein Sequencing Protocols, 211–19. Totowa, NJ: Humana Press, 2003. http://dx.doi.org/10.1385/1-59259-342-9:211.

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Webster, Judith, and David Oxley. "Protein Identification by Peptide Mass Fingerprinting using MALDI-TOF Mass Spectrometry." In Springer Protocols Handbooks, 1117–29. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-198-7_120.

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He, Zengyou, Chao Yang, Can Yang, Robert Z. Qi, Jason Po-Ming Tam, and Weichuan Yu. "Optimization-Based Peptide Mass Fingerprinting for Protein Mixture Identification." In Lecture Notes in Computer Science, 16–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02008-7_2.

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Song, Zhao, Luonan Chen, and Dong Xu. "Bioinformatics Methods for Protein Identification Using Peptide Mass Fingerprinting." In Methods in Molecular Biology, 7–22. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-444-9_2.

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Wilson, Nicole, Raina Simpson, and Catherine Cooper-Liddell. "Introductory Glycosylation Analysis Using SDS-PAGE and Peptide Mass Fingerprinting." In Glycomics, 205–12. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-022-5_15.

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Coca, Daniel, Istvan Bogdan, and Robert J. Beynon. "A High-Performance Reconfigurable Computing Solution for Peptide Mass Fingerprinting." In Methods in Molecular Biology, 163–85. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-444-9_12.

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Conference papers on the topic "Peptide mass fingerprinting"

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Bogdan, I., R. J. Beynon, and D. Coca. "Reconfigurable computing solution for Peptide Mass Fingerprinting." In 2008 11th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). IEEE, 2008. http://dx.doi.org/10.1109/optim.2008.4602499.

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Liang, Zhewei, Gilles Lajoie, and Kaizhong Zhang. "NBPMF: Novel network-based inference methods for peptide mass fingerprinting." In 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2017. http://dx.doi.org/10.1109/icci-cc.2017.8109752.

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Ganapathy, A., X. F. Wan, J. Wan, J. Thelen, D. W. Emerich, G. Stacey, and D. Xu. "Statistical assessment for mass-spec protein identification using peptide fingerprinting approach." In Conference Proceedings. 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2004. http://dx.doi.org/10.1109/iembs.2004.1403863.

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"APPLICATION OF COMBINATORIAL METHODS TO PROTEIN IDENTIFICATION IN PEPTIDE MASS FINGERPRINTING." In International Conference on Knowledge Discovery and Information Retrieval. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003102703070313.

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Song, Zhao, Luonan Chen, Chao Zhang, and Dong Xu. "Design and Implementation of Probability-Based Scoring Function for Peptide Mass Fingerprinting Protein Identification." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.260150.

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Song, Zhao, Luonan Chen, Chao Zhang, and Dong Xu. "Design and Implementation of Probability-Based Scoring Function for Peptide Mass Fingerprinting Protein Identification." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.4398466.

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Liu, Jikun, Chien-Fu Chen, Chien-Cheng Chang, and Don L. DeVoe. "Isoelectric Focusing-Reversed Phase Liquid Chromatography Polymer Microchip With Integrated High-Pressure Valves." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12147.

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A cyclicolefin polymer (COP) microchip supporting parallel 2-D peptide separations is described. By combining isoelectric focusing (IEF) as a first dimension and parallel reversed-phase liquid chromatography (RPLC) as a second dimension, the system enables efficient high-throughput fractionation prior to mass spectrometry in support of peptide mass fingerprinting for global proteomic analysis from highly limited specimens. The IEF-RPLC chip incorporates high-pressure micro shut-off valves, allowing uniform sample transfer and gradient elution from each micro LC column, and ensuring hydrodynamic isolation between the separation dimensions. The utility of the initial microchip is demonstrated by separation of a fluorescein labeled bovine serum albumin tryptic digest in a chip containing a five channel RPLC array.

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