Harnessing Ionic Selectivity in Acetyltransferase Chemoproteomic Probes.
Yihang JingJose L MontanoMichaella LevyJeffrey E LopezPei-Pei KungPaul RichardsonKrzysztof KrajewskiLaurence A FlorensMichael P WashburnJordan L MeierPublished in: ACS chemical biology (2020)
Chemical proteomics provides a powerful strategy for the high-throughput assignment of enzyme function or inhibitor selectivity. However, identifying optimized probes for an enzyme family member of interest and differentiating signal from the background remain persistent challenges in the field. To address this obstacle, here we report a physiochemical discernment strategy for optimizing chemical proteomics based on the coenzyme A (CoA) cofactor. First, we synthesize a pair of CoA-based sepharose pulldown resins differentiated by a single negatively charged residue and find this change alters their capture properties in gel-based profiling experiments. Next, we integrate these probes with quantitative proteomics and benchmark analysis of "probe selectivity" versus traditional "competitive chemical proteomics." This reveals that the former is well-suited for the identification of optimized pulldown probes for specific enzyme family members, while the latter may have advantages in discovery applications. Finally, we apply our anionic CoA pulldown probe to evaluate the selectivity of a recently reported small molecule N-terminal acetyltransferase inhibitor. These studies further validate the use of physical discriminant strategies in chemoproteomic hit identification and demonstrate how CoA-based chemoproteomic probes can be used to evaluate the selectivity of small molecule protein acetyltransferase inhibitors, an emerging class of preclinical therapeutic agents.
Keyphrases
- small molecule
- protein protein
- mass spectrometry
- living cells
- high throughput
- label free
- fatty acid
- structural basis
- single cell
- quantum dots
- physical activity
- high resolution
- computed tomography
- fluorescence imaging
- magnetic resonance imaging
- stem cells
- fluorescent probe
- single molecule
- big data
- cell therapy
- bioinformatics analysis
- amino acid
- artificial intelligence
- ionic liquid
- bone marrow
- deep learning
- hyaluronic acid