ArticleCommunications chemistry2023
Experimental strategies to improve drug-target identification in mass spectrometry-based thermal stability assays.
Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- DORSSAA: Drug-Target interactOmics Resource Based on Stability/Solubility Alteration Assay.Molecular & cellular proteomics : MCP · 2026Article
- Roles of Hydration in Protein-Ligand Binding: Passive or Active Participant?The journal of physical chemistry. A · 2025Article
- Evaluating Biocompatibility: From Classical Techniques to State-of-the-Art Functional Proteomics.Nanomaterials (Basel, Switzerland) · 2025Review
- On electrostatic interactions of adenosine triphosphate-insulin-degrading enzyme revealed by quantum mechanics/molecular mechanics and molecular dynamics.Quantitative biology (Beijing, China) · 2024Article
- Improved Data Acquisition Settings on Q Exactive HF-X and Fusion Lumos Tribrid Orbitrap-Based Mass Spectrometers for Proteomic Analysis of Limited Samples.Journal of proteome research · 2024Article
- Experimental and data analysis advances in thermal proteome profiling.Cell reports methods · 2024Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Mass spectrometry (MS)-based thermal stability assays have recently emerged as one of the most promising solutions for the identification of protein-ligand interactions. Here, we have investigated eight combinations of several recently introduced MS-based advancements, including the Phase-Constrained Spectral Deconvolution Method, Field Asymmetric Ion Mobility Spectrometry, and the implementation of a carrier sample as improved MS-based acquisition approaches for thermal stability assays (iMAATSA). We used intact Jurkat cells treated with a commercially available MEK inhibitor, followed by heat treatment, to prepare a set of unfractionated isobarically-labeled proof-of-concept samples to compare the performance of eight different iMAATSAs. Finally, the best-performing iMAATSA was compared to a conventional approach and evaluated in a fractionation experiment. Improvements of up to 82% and 86% were demonstrated in protein identifications and high-quality melting curves, respectively, over the conventional approach in the proof-of-concept study, while an approximately 12% improvement in melting curve comparisons was achieved in the fractionation experiment.
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Registered trials
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