Letteratura scientifica selezionata sul tema "Crosslinking mass spectrometry"
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Articoli di riviste sul tema "Crosslinking mass spectrometry":
Sinz, Andrea. "Crosslinking Mass Spectrometry Goes In-Tissue". Cell Systems 6, n. 1 (gennaio 2018): 10–12. http://dx.doi.org/10.1016/j.cels.2018.01.005.
Schneider, Michael, Adam Belsom e Juri Rappsilber. "Protein Tertiary Structure by Crosslinking/Mass Spectrometry". Trends in Biochemical Sciences 43, n. 3 (marzo 2018): 157–69. http://dx.doi.org/10.1016/j.tibs.2017.12.006.
Chen, Zhuo Angel, e Juri Rappsilber. "Protein structure dynamics by crosslinking mass spectrometry". Current Opinion in Structural Biology 80 (giugno 2023): 102599. http://dx.doi.org/10.1016/j.sbi.2023.102599.
Xia, Yingzi. "Exploring misfolded proteins with crosslinking mass spectrometry". Biophysical Journal 123, n. 3 (febbraio 2024): 206a. http://dx.doi.org/10.1016/j.bpj.2023.11.1301.
Petrotchenko, Evgeniy V., e Christoph H. Borchers. "Crosslinking combined with mass spectrometry for structural proteomics". Mass Spectrometry Reviews 29, n. 6 (21 agosto 2010): 862–76. http://dx.doi.org/10.1002/mas.20293.
Dancy, Beverley M., Fan Liu, Philip Lössl, Albert J. R. Heck e Robert S. Balaban. "The mitochondrial interactome visualized by crosslinking mass spectrometry". Biochimica et Biophysica Acta (BBA) - Bioenergetics 1857 (agosto 2016): e22. http://dx.doi.org/10.1016/j.bbabio.2016.04.045.
Singh, Arunima. "Crosslinking mass spectrometry data bolster protein structure prediction". Nature Methods 20, n. 5 (maggio 2023): 633. http://dx.doi.org/10.1038/s41592-023-01890-3.
Graziadei, Andrea, e Juri Rappsilber. "Leveraging crosslinking mass spectrometry in structural and cell biology". Structure 30, n. 1 (gennaio 2022): 37–54. http://dx.doi.org/10.1016/j.str.2021.11.007.
Chen, Zhuo A., e Juri Rappsilber. "Protein Dynamics in Solution by Quantitative Crosslinking/Mass Spectrometry". Trends in Biochemical Sciences 43, n. 11 (novembre 2018): 908–20. http://dx.doi.org/10.1016/j.tibs.2018.09.003.
Bullock, Joshua Matthew Allen, Neeladri Sen, Konstantinos Thalassinos e Maya Topf. "Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry". Structure 26, n. 7 (luglio 2018): 1015–24. http://dx.doi.org/10.1016/j.str.2018.04.016.
Tesi sul tema "Crosslinking mass spectrometry":
Taverner, Thomas. "Protein complex architecture from mass spectrometry, crosslinking and informatics". Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612836.
Mak, Esther W. M. "Using Chemical Crosslinking and Mass Spectrometry for Protein Model Validation and Fold Recognition". Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/1228.
Braun, Craig Ronald. "Structural Characterization of BCL-2 Family Protein Interactions Using Photoreactive Stapled Peptides and Mass Spectrometry". Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10139.
DAI, ZHENYU. "PROTEIN CROSSLINKING BY THE MAILLARD REACTION WITH ASCORBIC ACID AND GLUCOSE". Case Western Reserve University School of Graduate Studies / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=case1184176746.
Müller, Fränze [Verfasser], Juri [Akademischer Betreuer] Rappsilber, Juri [Gutachter] Rappsilber e Markus [Gutachter] Ralser. "Quantitative crosslinking mass spectrometry : development and application to protein conformation changes / Fränze Müller ; Gutachter: Juri Rappsilber, Markus Ralser ; Betreuer: Juri Rappsilber". Berlin : Technische Universität Berlin, 2020. http://d-nb.info/1213348498/34.
Giese, Sven Hans-Joachim [Verfasser], Juri [Akademischer Betreuer] Rappsilber, Matthias [Gutachter] Selbach e Juri [Gutachter] Rappsilber. "Computational methods and machine learning for crosslinking mass spectrometry data analysis / Sven Hans-Joachim Giese ; Gutachter: Matthias Selbach, Juri Rappsilber ; Betreuer: Juri Rappsilber". Berlin : Technische Universität Berlin, 2021. http://d-nb.info/1238140718/34.
Ferrari, Állan Jhonathan Ramos 1991. "Caracterização estrutural da Stanniocalcina-1 por Proteômica Estrutural". [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/250217.
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Química
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Resumo: A Stanniocalcina-1 (STC1) é um hormônio glicoproteico que apresenta padrão de expressão diferencial destacado em diversas patologias, notadamente em neoplasias, mas seus aspectos funcionais e estruturais são pouco explorados até o momento. Nesse sentido, a STC1 foi escolhida como alvo para a utilização de uma abordagem integrativa das técnicas que utilizam os reagentes de ligação cruzada, espectrometria de massas e modelagem molecular para a modelagem estrutural. A partir dos experimentos de ligação cruzada, foram obtidas 37 restrições de distância envolvendo espécies ligadas com DSS, sendo 11 destas espécies com N-terminal e uma restrição envolvendo a espécie dimérica, além das cinco ligações de dissulfeto já publicadas. Essas restrições foram utilizadas para a geração de modelos estruturais nas plataformas online I-Tasser e Quark e, localmente, mais de 100.000 modelos pelo protocolo Ab Initio Relax do software Rosetta em quatro diferentes condições iniciais de modelagem. O Rosetta apresentou maior eficiência na geração de modelos quando ausente arquivo de predição de estrutura secundária. As restrições de distância foram ferramenta discriminatória fundamental para a seleção de estruturas candidatas para a STC1. O agrupamento utilizando o parâmetro global distante test (gdt) das 1500 modelos de menor score que respeitavam todas as restrições identificou 22 estruturas representativas estruturalmente distintas. Essas estruturas representativas podem agora ser utilizadas em testes envolvendo substituição molecular nos dados de difração de raios-X
Abstract: The Stanniocalcin-1 (STC1) is a glycoproteic hormone, which shows a differential expression pattern in a variety of pathologies, especially in neoplasia, but its functional and structural aspects have not been explored. Accordingly, the STC1 was chosen as a target to the use of an integrative approach including chemical cross-linking, mass spectrometry and molecular modeling. From cross-linking experiments,37 distance constrains were identified involving the DSS cross-linker, 11 of them in the N-terminus part of the protein and one involving the dimeric specie, in addition to five disulfide bonds already published. These constrains were used to generate structural models by I-Tasser and Quark online platforms and, locally, more than 100,000 models in the Ab Initio Relax protocol package present in the Rosetta software in four different modeling conditions. Rosetta was the most efficient in generating models when secondary structure prediction was absent. The distance constrains were found to be a key discriminatory tool for the selection of candidate structures for the STC1. For the 1,500 lowest score structures that satisfied the distance constrains, the clustering method employing the global distance test parameter (gdt) identified 22 structurally distinct representative structures. These representative structures can be used to in molecular replacement test to solve the X-Ray diffraction data
Mestrado
Quimica Organica
Mestre em Química
Garcia, del Rio Diego Fernando. "Studying protein complexes for assessing the function of ghost proteins (Ghost in the Cell)". Electronic Thesis or Diss., Université de Lille (2022-....), 2023. https://pepite-depot.univ-lille.fr/ToutIDP/EDBSL/2023/2023ULILS115.pdf.
Ovarian cancer (OvCa) has the highest mortality rate among female reproductive cancers worldwide. OvCa is often referred to as a stealth killer because it is commonly diagnosed late or misdiagnosed. Once diagnosed, OvCa treatment options include surgery or chemotherapy. However, chemotherapy resistance is a significant obstacle. Therefore, there is an urgent need to identify new targets and develop novel therapeutic strategies to overcome therapy resistance.In this context the ghost proteome is a potentially rich source of biomarkers. The ghost proteome, also known as the alternative proteome, consists of proteins translated from alternative open reading frames (AltORFs). These AltORFs originate from different start codons within mRNA molecules, such as the coding DNA sequence (CDS) in frameshifts (+1, +2), the 5'-UTR, 3'-UTR, and possible translation products from non-coding RNAs (ncRNA).Studies on alternative proteins (AltProts) are often limited due to their case-by-case occurrence and complexity. Obtaining functional protein information for AltProts requires complex and costly biomolecular studies. However, their functions can be inferred by profiling their interaction partners, known as "guilty by association" approaches. Indeed, assessing AltProts' protein-protein interactions (PPIs) with reference proteins (RefProts) can help identify their function and set them as research targets. Since there is a lack of antibodies against AltProts, crosslinking mass spectrometry (XL-MS) is an appropriate tool for this task. Additionally, bioinformatic tools that link protein functional information through networks and gene ontology (GO) analysis are also powerful. These tools enable the visualization of signaling pathways and the grouping of RefProts based on their biological process, molecular function, or cellular localization, thus enhancing our understanding of cellular mechanisms.In this work, we developed a methodology that combines XL-MS and subcellular fractionation. The key step of subcellular fractionation allowed us to reduce the complexity of the samples analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). To assess the validity of crosslinked interactions, we performed molecular modeling of the 3D structures of the AltProts, followed by docking studies and measurement of the corresponding crosslink distances. Network analysis indicated potential roles for AltProts in biological functions and processes. The advantages of this workflow include non-targeted AltProt identification and subcellular identification.Additionally, a proteogenomic analysis was performed to investigate the proteomes of two ovarian cancer cell lines (PEO-4 and SKOV-3 cells) in comparison to a normal ovarian epithelial cell line (T1074 cell). Using RNA-seq data, customized protein databases for each cell line were generated. Differential expression of several proteins, including AltProts, was identified between the cancer and normal cell lines. The expression of some RefProts and their transcripts were associated with cancer-related pathways. Moreover, the XL-MS methodology described above was used to identify PPIs in the cancerous cell lines.This work highlights the significant potential of proteogenomics in uncovering new aspects of ovarian cancer biology. It enables us to identify previously unknown proteins and variants that may have functional significance. The use of customized protein databases and the crosslinking approach have shed light on the "ghost proteome," an area that has remained unexplored until now
Gafken, Philip R. "Characterization of UV-crosslinked protein-nucleic acid interfaces by Maldi MS and ESI MS/MS". Thesis, 2000. http://hdl.handle.net/1957/32805.
Jensen, Ole Norregaard. "Characterization of photochemically cross-linked protein-nucleic acid complexes by mass spectrometry". Thesis, 1994. http://hdl.handle.net/1957/35130.
Graduation date: 1995
Libri sul tema "Crosslinking mass spectrometry":
Jensen, Ole Nørregaard. Characterization of photochemically cross-linked protein-nucleic acid complexes by mass spectrometry. 1994.
Capitoli di libri sul tema "Crosslinking mass spectrometry":
Urlaub, Henning, Eva Kühn-Hölsken e Reinhard Lührmann. "Analyzing RNA-Protein Crosslinking Sites in Unlabeled Ribonucleoprotein Complexes by Mass Spectrometry". In Methods in Molecular Biology, 221–45. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-475-3_16.
Rafiei, Atefeh, e David C. Schriemer. "A Crosslinking Mass Spectrometry Protocol for the Structural Analysis of Microtubule-Associated Proteins". In Methods in Molecular Biology, 211–22. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2124-0_14.
Deterding, Leesa J., e Kenneth B. Tomer. "Chemical Surface Modification and Chemical Crosslinking Combined with Mass Spectrometry for Protein Tertiary Structural Information". In NATO Science for Peace and Security Series A: Chemistry and Biology, 139–50. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8811-7_10.
Calabrese, Antonio N. "Characterization of β-Barrel Outer Membrane Proteins and Their Interactions with Chaperones by Chemical-Crosslinking Mass Spectrometry". In Methods in Molecular Biology, 259–72. New York, NY: Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-3734-0_16.
Happonen, Lotta J. "Affinity-Purification Combined with Crosslinking Mass Spectrometry for Identification and Structural Modeling of Host–Pathogen Protein–Protein Complexes". In Methods in Molecular Biology, 181–200. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3243-7_12.
Hiratsuka, Takuya, e Tatsuaki Tsuruyama. "Mass Spectrometry Analysis Using Formalin-Fixed Paraffin-Embedded Pathological Samples". In Mass Spectrometry - Recent Advances and Key Aspects [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1002728.
Rapporti di organizzazioni sul tema "Crosslinking mass spectrometry":
Haskins, William E., Michael D. Leavell, Pamela Lane, Richard B. Jacobsen, Joohee Hong, Marites J. Ayson, Nichole L. Wood et al. Chemical crosslinking and mass spectrometry studies of the structure and dynamics of membrane proteins and receptors. Office of Scientific and Technical Information (OSTI), marzo 2005. http://dx.doi.org/10.2172/922763.