Experimental strategies to improve drug-target identification in mass spectrometry-based thermal stability assays.
Journal
Communications chemistry
ISSN: 2399-3669
Titre abrégé: Commun Chem
Pays: England
ID NLM: 101725670
Informations de publication
Date de publication:
06 Apr 2023
06 Apr 2023
Historique:
received:
25
07
2022
accepted:
23
03
2023
medline:
7
4
2023
entrez:
6
4
2023
pubmed:
7
4
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37024568
doi: 10.1038/s42004-023-00861-1
pii: 10.1038/s42004-023-00861-1
pmc: PMC10079678
doi:
Types de publication
Journal Article
Langues
eng
Pagination
64Subventions
Organisme : NCI NIH HHS
ID : R01 CA218500
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM136421
Pays : United States
Organisme : U.S. Department of Health & Human Services | NIH | Center for Information Technology (Center for Information Technology, National Institutes of Health)
ID : R35GM136421
Organisme : U.S. Department of Health & Human Services | NIH | Center for Information Technology (Center for Information Technology, National Institutes of Health)
ID : R01CA218500
Informations de copyright
© 2023. The Author(s).
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