Modified peptides constitute a sub-population among the tryptic peptides analyzed in LC-MS based shotgun proteomics experiments. For larger proteomes including the human proteome, the tryptic peptide pool is very large, which necessitates some form of sample fractionation. By carefully choosing the sample fractionation and separation methods applied as shown here for the combination of narrow-range immobilized pH gradient isoelectric focusing (IPG-IEF) and nanoUPLC-MS, significantly increased information content can be achieved. Relatively low standard deviations were obtained for such multidimensional separations in terms of peptide pI (<0.05 pI units) and retention time (<0.3 min for a 350 min gradient) for a selection of highly complex proteomics samples. Using narrow-range IPG-IEF, experimental and predicted pI were in relative good agreement. However, based on our data, retention time prediction algorithms need further improvements in accuracy to match state-of-the-art reversed-phase chromatography performance. General trends of peptide pI shifts induced by common modifications including deamidations and N-terminal modifications are described. Deamidations of glutamine and asparagines shift peptide pI by approximately 1.5 pI units, making the peptides more acidic. Additionally, a novel pI shift (+~0.4 pI units) was found associated with dethiomethyl Met modifications. Further, the effects of these modifications as well as methionine oxidation were investigated in terms of experimentally observed retention time shifts in the chromatographic separation step. Clearly, post-translational modification-induced influences on peptide pI and retention time can be accurately and reproducibly measured using narrow-range IPG-IEF and high-performance nanoLC-MS. Even at modest mass accuracy (±50 ppm), the inclusion of peptide pI (±0.2 pI units) and/or retention time (±20 min) criteria are highly informative for human proteome analyses. The applications of using this information to identify post-translationally modified peptides and improve data analysis workflows are discussed.
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http://dx.doi.org/10.1007/s00726-010-0704-2 | DOI Listing |
Clin Transl Med
January 2025
Department of Cell Biology, National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, China.
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Mil Med
January 2025
Research Unit of Clinical Medicine, Psychiatry, University of Oulu, Oulu, 90014, Finland.
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January 2025
School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei 230009, China.
In multi-dimensional nanopositioning and nanomeasuring devices, interference measurement is widely used. The three-dimensional (3D) target mirror serves as the spatial reference plane to achieve multidimensional interference measurements. However, the surface shape errors of the target mirror are superimposed on the geometric dimensions of the measured workpiece, which limits the system's overall measurement accuracy.
View Article and Find Full Text PDFJCI Insight
January 2025
Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, United States of America.
Pancreatic ductal adenocarcinoma (PDAC) is a drug resistant and lethal cancer. Identification of the genes that consistently show altered expression across patients' cohorts can expose effective therapeutic targets and strategies. To identify such genes, we separately analyzed five human PDAC microarray datasets.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
Remote photo-plethysmography (rPPG) is a useful camera-based health motioning method that can measure the heart rhythm from facial videos. Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these methods are unable to effectively eliminate illumination variation and motion artifact disturbances, and their substantial computational resource requirements significantly limit their applicability in real-world scenarios.
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