DrugMap
The bedside arrival of blockbuster medicines like ibrutinib and osimertinib changed the narrative on oncogene-driven cancers. How? Cysteine. Reporting in 2024, Takahashi et al. comprehensively map the covalently targetable cysteines across 416 cancer models, defining biological and structural principles of target tractability. Welcome to DrugMap.
Install / Use
/learn @bplab-compbio/DrugMapREADME
Welcome to the home-page of DrugMap: A quantitative pan-cancer analysis of cysteine ligandability!
<p align="center"> <img src="https://github.com/bplab-compbio/DrugMap/blob/main/src/images/circos.png" width="700" height="600"> </p>Toward systematically identifying proteins in cancer which contain covalent opportunities for cysteine liganding, we used a mass-spectrometry based assay to profile the landscape of cysteine reactivity across 416 cancer models.
<p align="center"> <img src="https://github.com/bplab-compbio/DrugMap/blob/main/src/images/cysteine.architecture.png" width="600" height="600"> </p>Along the way, we examined the influence of diverse protein-structural and oncogenic contexts on cysteine ligandability. More on this here.
To make this resource maximally helpful to the wider community of cancer biologists, cysteine sleuths, and other interested travelers, we have released essential methodology underpinning our analyses, including:
- The raw outputs of the software that we used to search our cysteine-targeted mass spectrometry data
- The complete, detailed workflow that we used to wrangle together DrugMap
- The function and initial database for our tool Cysteine Set Enrichment Analysis (CSEA), as well as an example of its use
- The code we used to train a neural network aimed at predicting cysteine ligandability
