Literature DB >> 36178622

Proteome Integral Solubility Alteration (PISA) for High-Throughput Ligand Target Deconvolution with Increased Statistical Significance and Reduced Sample Amount.

Massimiliano Gaetani1,2,3, Roman A Zubarev4,5.   

Abstract

Proteome Integral Solubility Alteration (PISA) is a recently developed mass spectrometry-based, deep proteomics method for unbiased, proteome-wide target deconvolution of ligands, requiring no chemical ligand modification. PISA can be applied to living cells for studying target engagement in vivo or alternatively to protein extracts to identify in vitro ligand-interacting proteins. Here we describe the PISA workflow optimized in our lab. PISA improves the target discovery throughput 10-100 folds compared to the previously used proteomics methods and provides higher statistical significance for target candidates by enabling several biological replicates. Sample multiplexing makes all-in-one analysis of multiple ligands simultaneously possible. PISA dramatically reduces analysis costs, allowing many research questions in need of target deconvolution to be addressed, and unlocks the potential of miniaturizing biological models, including primary cells.
© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Chemoproteomics; Ligand; Ligand target deconvolution; PISA; Protein–metabolite interactions; Solubility alteration

Mesh:

Substances:

Year:  2023        PMID: 36178622     DOI: 10.1007/978-1-0716-2624-5_7

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  14 in total

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Journal:  Nat Protoc       Date:  2015-09-17       Impact factor: 13.491

2.  Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay.

Authors:  Daniel Martinez Molina; Rozbeh Jafari; Marina Ignatushchenko; Takahiro Seki; E Andreas Larsson; Chen Dan; Lekshmy Sreekumar; Yihai Cao; Pär Nordlund
Journal:  Science       Date:  2013-07-05       Impact factor: 47.728

Review 3.  Target profiling of small molecules by chemical proteomics.

Authors:  Uwe Rix; Giulio Superti-Furga
Journal:  Nat Chem Biol       Date:  2009-09       Impact factor: 15.040

Review 4.  How were new medicines discovered?

Authors:  David C Swinney; Jason Anthony
Journal:  Nat Rev Drug Discov       Date:  2011-06-24       Impact factor: 84.694

5.  Cytotoxic and Proinflammatory Effects of Metal-Based Nanoparticles on THP-1 Monocytes Characterized by Combined Proteomics Approaches.

Authors:  Nataliya K Tarasova; Audrey Gallud; A Jimmy Ytterberg; Alexey Chernobrovkin; Jaime Ruiz Aranzaes; Didier Astruc; Alexei Antipov; Yuri Fedutik; Bengt Fadeel; Roman A Zubarev
Journal:  J Proteome Res       Date:  2016-12-28       Impact factor: 4.466

6.  Functional Identification of Target by Expression Proteomics (FITExP) reveals protein targets and highlights mechanisms of action of small molecule drugs.

Authors:  Alexey Chernobrovkin; Consuelo Marin-Vicente; Neus Visa; Roman A Zubarev
Journal:  Sci Rep       Date:  2015-06-08       Impact factor: 4.379

7.  Expression proteomics study to determine metallodrug targets and optimal drug combinations.

Authors:  Ronald F S Lee; Alexey Chernobrovkin; Dorothea Rutishauser; Claire S Allardyce; David Hacker; Kai Johnsson; Roman A Zubarev; Paul J Dyson
Journal:  Sci Rep       Date:  2017-05-08       Impact factor: 4.379

8.  ProTargetMiner as a proteome signature library of anticancer molecules for functional discovery.

Authors:  Amir Ata Saei; Christian Michel Beusch; Alexey Chernobrovkin; Pierre Sabatier; Bo Zhang; Ülkü Güler Tokat; Eleni Stergiou; Massimiliano Gaetani; Ákos Végvári; Roman A Zubarev
Journal:  Nat Commun       Date:  2019-12-16       Impact factor: 14.919

Review 9.  Chemical proteomics approaches for identifying the cellular targets of natural products.

Authors:  M H Wright; S A Sieber
Journal:  Nat Prod Rep       Date:  2016-05-04       Impact factor: 13.423

10.  Chemical proteomics, an integrated research engine for exploring drug-target-phenotype interactions.

Authors:  Ho Jeong Kwon; Peter Karuso
Journal:  Proteome Sci       Date:  2018-01-09       Impact factor: 2.480

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