Publications by authors named "D Shteynberg"
Lyme disease is caused by an infection with the spirochete Borrelia burgdorferi, and is the most common vector-borne disease in North America. B. burgdorferi isolates harbor extensive genomic and proteomic variability and further comparison of isolates is key to understanding the infectivity of the spirochetes and biological impacts of identified sequence variants.
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- This study identifies the formation of covalent protein adducts from drug metabolism as significant risk factors for adverse drug reactions and cytochrome P450 enzyme inactivation.
- It introduces a novel liquid chromatography with tandem mass spectrometry (LC-MS/MS) approach to detect low abundance drug-protein adducts in human liver microsomes, using raloxifene as a model.
- The findings reveal adducts in multiple proteins, including CYP enzymes, and suggest that some adducts may be harmless, providing a framework for better understanding the human adductome related to drug exposure.
Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E.
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- Lyme disease, caused by a specific spirochete infection, is the most prevalent vector-borne disease in North America, with significant genetic and protein variation among its strains.
- Researchers used transcriptomics and mass spectrometry to create a comprehensive dataset of proteins from various laboratory and infective strains of the spirochete, leading to the development of a public database called Borrelia PeptideAtlas.
- This database contains extensive proteomic data, revealing 76,936 unique peptides and 1,221 proteins, which can help identify common protein targets linked to the infectivity of this disease.
Article Synopsis
- The Trans-Proteomic Pipeline (TPP) has been evolving for 20 years and now offers a comprehensive suite of tools for analyzing mass spectrometry data, covering areas like spectrum processing, searching, and protein inference.
- Many TPP tools utilize machine learning to enhance data analysis and create strong statistical models to validate results.
- The latest version supports various platforms, includes user tutorials, features collaborative projects, and outlines future development plans for TPP.