Decrypting drug actions and protein modifications by dose- and time-resolved proteomics, Science (2023)

https://www.science.org/doi/10.1126/science.ade3925  

Proteome으로 drug의 Mode of Action을 스크리닝 한 논문은 봄. 

Time, dose-dependent proteome을 볼 수 있다는 점이 특히 새로운 부분인 것 같음 

 

DecryptM이 어떻게 작동하는지?

사람 혈액 샘플 같은 것의 프로테옴도 측정 가능한 것인지?

Decrypting drug actions through proteomics

Many cancer drugs aim to inhibit abnormally active proteins that drive the growth of tumors, but it is often not understood just how cancer cells react to these drugs. Zecha et al. devised a proteomic technology called DecryptM that can measure the time- and dose-dependent response of thousands of proteins and reveal changes in response to small-molecule or antibody-based drugs. Application of DecryptM to 31 drugs generated millions of measurements. These data helped to place protein modifications into new functional contexts, created fingerprints of drug response, and provided insights into how certain drugs kill cancerous blood cells. The DecryptM data are available as a community resource for use in future basic biology, drug discovery, and clinical research. —PNK

Structured Abstract

INTRODUCTION

Most drugs act on proteins and engage cellular pathways regulated by protein posttranslational modifications (PTMs), such as phosphorylation, acetylation, or ubiquitinylation, to exert their therapeutic effects. Because polypharmacology is common, it is important to characterize drugs on a proteome-wide scale to understand all their mechanisms of action.

RATIONALE

There is a lack of dose- and time-dependent drug characterization at the level of proteins and PTMs, arguably the most important characteristics of drug action in any biological context. To address this gap, this study presents a quantitative proteomic approach called decryptM, which is able to assess drug target and pathway engagement as well as cellular mechanism of action by measuring thousands of PTM responses in a dose- and time-resolved fashion.

RESULTS

DecryptM profiling of 31 cancer drugs in 13 human cancer cell line models resulted in 1.8 million dose-response curves, including 47,502 regulated phosphopeptides, 7316 ubiquitinylated peptides, and 546 regulated acetylated peptides, all of which can be explored in ProteomicsDB (www.proteomicsdb.org/decryptM).

The observed close coherence between drug-target affinity and drug-PTM modulation potency enabled placing functionally uncharacterized PTM sites into known pathways and thus decrypting them on the grounds of guilt by association. Examples include previously uncharacterized phosphorylation sites linking chemotherapeutic drugs to the DNA damage response, regulated phosphorylation sites indicating breakdown of oncogenic signaling in response to phosphatase and kinase inhibitors, or activation of the unfolded protein response upon proteasome inhibition. Similarly, decryptM profiles enabled the identification of previously unknown substrates of kinases as well as lysine acetyltransferases and deacetylases. Each drug appeared to leave a cell line–specific decryptM signature that may constitute a pharmacodynamic marker for target and pathway engagement, identify points of conversion of signaling axes, or distinguish closely related compounds.

DecryptM analysis of therapeutic anti-HER2 antibodies revealed differences in their mechanisms of action. Whereas pertuzumab cuts off the HER3–mitogen-activated protein kinase (MAPK) and HER3-PI3K/AKT signaling axis in breast cancer cells, trastuzumab had no effect on any of the phosphoproteomes investigated. By contrast, the anti-CD20 antibody rituximab massively activates signaling in B cells. The collective decryptM and functional evidence supports a model in which rituximab binds to CD20, located in lipid rafts alongside the B cell receptor complex, and leads to strong activation of the MAPK and calcineurin–nuclear factor of activated T cells (NFAT) axis, tipping the signaling balance toward apoptotic cell death.

CONCLUSION

The examples presented in this study illustrate the potential of decryptM to characterize drugs’ mechanisms of action, generate drug-specific PTM signatures, study resistance mechanisms, and place drug-regulated PTM sites of unclear significance into a functional context. The workflow developed supports decryptM profiling at scale and should be extendable to any molecule that modulates cellular activity by affecting PTMs or protein expression. This may include G protein–coupled receptor (GPCR) ligands, cytokines, chemokines, cofactors, metabolites, biologics, peptides, and hormones among many other factors. In the future, decryptM profiles may also serve to monitor and potentially predict drug responses in vivo. Looking further, we envision that matching decryptM profiles of cancer drugs with PTM profiles of cancer patients will become important for evidence-based treatment recommendations by molecular tumor boards.