Post-translational modifications (PTMs) of proteins add to the complexity of proteomes, thereby complicating the task of proteome characterization. An efficient strategy to identify this peptide heterogeneity is important for determination of protein function, as well as for mass spectrometry-based protein quantification. Furthermore, studies of allelic variation or single nucleotide polymorphisms (SNPs) at the proteome level, as well as mRNA editing, are increasingly relevant, but validation and determination of false positive rates are challenging. Here we describe an effective workflow for large scale PTM and amino acid substitution identification based on high resolution and high mass accuracy RPLC-MS data sets. A systematic validation strategy of PTMs using RPLC retention time shifts was implemented, and a decision tree for validation is presented. This workflow was applied to Arabidopsis proteome preparations; 1.5 million MS/MS spectra were processed resulting in 20% sequence assignments, with 5% from modified sequences and matching to 2904 proteins; this high assignment rate is in part due to the high quality spectral data. A searchable modified peptide library for Arabidopsis is available online at http://ppdb.tc.cornell.edu/. We discuss confidence in peptide and PTM assignment based on the acquired data set, as well as implications for quantitative analysis of physiologically induced and preparation-related modifications.
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Adv Methods Pract Psychol Sci
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Department of Psychology, University of Washington, Seattle, Washington.
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View Article and Find Full Text PDFJ Proteome Res
January 2025
Functional Genomics Center Zurich (FGCZ) - University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.
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View Article and Find Full Text PDFIntroduction: Limited research is available regarding recommendations about which drug allergy alerts (DAAs) in clinical decision support (CDS) systems should interrupt provider workflow. The objective was to evaluate the frequency of penicillin and cephalosporin DAA overrides at two institutions. A secondary objective was to redesign DAAs using a new tiered alerting system based on patient factors.
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Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA.
Purpose Of Review: Artificial intelligence (AI) offers a new frontier for aiding in the management of both acute and chronic pain, which may potentially transform opioid prescribing practices and addiction prevention strategies. In this review paper, not only do we discuss some of the current literature around predicting various opioid-related outcomes, but we also briefly point out the next steps to improve trustworthiness of these AI models prior to real-time use in clinical workflow.
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Magn Reson Med
January 2025
MR Physics, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
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