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
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

64

Subventions

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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Auteurs

Clifford G Phaneuf (CG)

Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA.
Sanofi, Disease Profiling and Functional Genomics, Cambridge, MA, USA.

Konstantin Aizikov (K)

Thermo Fisher Scientific, Bremen, Germany.

Dmitry Grinfeld (D)

Thermo Fisher Scientific, Bremen, Germany.

Arne Kreutzmann (A)

Thermo Fisher Scientific, Bremen, Germany.

Daniel Mourad (D)

Thermo Fisher Scientific, Bremen, Germany.

Oliver Lange (O)

Thermo Fisher Scientific, Bremen, Germany.

Daniel Dai (D)

Sanofi, Disease Profiling and Functional Genomics, Cambridge, MA, USA.

Bailin Zhang (B)

Sanofi, Disease Profiling and Functional Genomics, Cambridge, MA, USA.

Alexei Belenky (A)

Tarmeta Biosciences, Natick, MA, USA.

Alexander A Makarov (AA)

Thermo Fisher Scientific, Bremen, Germany.

Alexander R Ivanov (AR)

Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA. a.ivanov@northeastern.edu.

Classifications MeSH