Healy, A. | Search bioRxiv

bioRxiv2023

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors of a wide-range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed DrugMap, an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF{kappa}B1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF{kappa}B1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription factor activity.

systems biology↗

bioRxiv2023

Development and validation of a high throughput screening platform to enable target identification in skeletal muscle cells from Duchenne Muscular Dystrophy (DMD) patients

Duchenne muscular dystrophy (DMD) is a progressive and fatal muscle degenerating disease caused by dystrophin deficiency. Effective methods for drug discovery for the treatment of DMD requires systems to be physiologically relevant, scalable, and effective. To this end, the Myoscreen platform offers a scalable and physiologically relevant system for generating and characterizing patient-derived myotubes. Morphological profiling is a powerful technique involving the simultaneous measurement of hundreds of morphological parameters from fluorescence microscopy images and using machine learning to predict cellular activity. Here, we describe combining the Myoscreen platform and high dimensional morphological profiling to accurately predict a phenotype associated with the lack of Dystrophin expression in patient derived myotubes. Using this methodology, we evaluated a series of Dystrophin-associated protein complex (DAPC) candidates and identified that the combination of Utrophin and - Sarcoglycan yielded highest morphological differences between DMD and non-DMD donors. Finally, we validated this methodology by knocking down Dystrophin expression in non-DMD cells as well as introducing Dystrophin expression in DMD cells. Knocking down Dystrophin in non- DMD cells shifted their morphological profile to one that is similar to DMD cells while introducing Dystrophin in DMD cells shifted their morphological profile towards non-DMD cells. In conclusion, we have developed a platform that accurately predicts the DMD disease phenotype in a disease relevant cell type. Ultimately this platform may have wide applications in the drug development process include identification of disease modifier genes, screening of novel therapeutic moieties, and as a potency assay for future therapeutics.

cell biology↗