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As a vital project of forward chemical genetic research, target deconvolution aims to identify the molecular targets of an active hit compound. Chemoproteomics, either with chemical probe-facilitated target enrichment or probe-free, provides a straightforward and effective approach to profile the target landscape and unravel the mechanisms of action. Canonical methods rely on chemical probes to enable target engagement, enrichment, and identification, whereas click chemistry and photoaffinity labeling techniques improve the efficiency, sensitivity, and spatial accuracy of target recognition. In comparison, recently developed probe-free methods detect protein-ligand interactions without the need to modify the ligand molecule. This review provides a comprehensive overview of different approaches and recent advancements for target identification and highlights the significance of chemoproteomics in investigating biological processes and advancing drug discovery processes.
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http://dx.doi.org/10.1002/advs.202305608 | DOI Listing |
Mol Cell Proteomics
March 2025
Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany; SciLifeLab, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden. Electronic address:
Limited proteolysis coupled with mass spectrometry (LiP-MS) has emerged as a powerful technique for detecting protein structural changes and drug-protein interactions on a proteome-wide scale. However, there is no consensus on the best quantitative proteomics workflow for analyzing LiP-MS data. In this study, we comprehensively benchmarked two major quantification approaches-data-independent acquisition (DIA) and tandem mass tag (TMT) isobaric labeling-in combination with LiP-MS, using a drug-target deconvolution assay as a model system.
View Article and Find Full Text PDFSci Rep
March 2025
Gradalis, Inc, 2545 Golden Bear Dr., Suite 110, Carrollton, Dallas, TX, 75006, USA.
Retrospective data support overall survival (OS) advantage to high clonal tumor mutation burden (cTMB), high clonal neoantigen load (cNEO) and low intratumor heterogeneity (ITH) in cancer patients who receive immunotherapy. In order to explore this relationship prospectively with Vigil, a triple function targeted immunotherapy involving ovarian cancer patients in long term follow up of the Phase 2b VITAL trial, we developed an exome sequencing procedure and associated bioinformatics pipeline to determine clonal signal patterns. DNA libraries containing exome sequences tagged with unique molecular identifiers (UMI) were prepared from paired samples and sequenced on Illumina sequencers to high coverage depths of ~ 930X (tumor) and ~ 130X (normal).
View Article and Find Full Text PDFBMC Cancer
March 2025
Department of Surgery, University of South Florida, 560 Channelside Drive, Tampa, FL, 33602, USA.
Background: Accumulating evidence has suggested that cancer progression and therapeutic response depend on both tumor epithelium (EPI) and tumor microenvironment (TME). However, the dependency of clinical outcomes on the tumor EPI vs. the TME has neither been clearly defined nor quantified.
View Article and Find Full Text PDFFront Immunol
March 2025
Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland.
Introduction: Systemic lupus erythematosus (SLE) is characterized by dysregulated humoral immunity, leading to the generation of autoreactive B cells that can differentiate both within and outside of lymph node (LN) follicles.
Methods: Here, we employed spatial transcriptomics and multiplex imaging to investigate the follicular immune landscaping and the transcriptomic profile in LNs from SLE individuals.
Results: Our spatial transcriptomic analysis revealed robust type I IFN and plasma cell signatures in SLE compared to reactive, control follicles.
J Gerontol A Biol Sci Med Sci
March 2025
The Jackson Laboratory, Bar Harbor, Maine, USA.
Historically, aging research has focused primarily on the study of differences in means of varied measures obtained at different ages. However, growing evidence has shown that for many parameters, variability in measurements obtained both between- and within-age groups increases with aging. Moreover, growing heterogeneity may become especially apparent when examined via longitudinal as opposed to cross-sectional aging data.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!