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Biology subjects

Canzani, D.

Publications and source records attributed to Canzani, D..

2 recordsLinked to original sources

Native, Spatiotemporal Profiling of the Global Human Regulome

The regulome, comprising transcription factors, cofactors, chromatin remodelers, and other regulatory proteins, forms the core machinery by which cells interpret signals and execute gene expression programs. Despite its central role in development, disease, and drug response, the regulome remains largely uncharted at scale due to its dynamic, low-abundance, and chromatin-associated nature. Here, we present a method for scalable, regulome profiling for global, compartment-resolved quantification of native regulome proteins. By enriching DNA- and chromatin-associated proteins and profiling them using high-throughput, label-free DIA mass spectrometry, regulome profiling captures chromatin-associated proteins across 36 human cell lines and thousands of perturbations. The resulting Regulome Atlas recovers nearly 60% of known human transcription factors, reveals lineage-specific TF localization, and distinguishes active nuclear engagement from latent, unbound states. We demonstrate that regulome profiles resolve acute immune pathway activation prior to transcriptional changes, identify previously unrecognized drug-induced regulome responses, and enable proteome-scale readouts of compound target engagement and complex remodeling. This work establishes a foundational resource for decoding the regulatory proteome and provides a blueprint for integrating regulome data into next-generation models of cellular behavior.

cell biology↗

An artificial intelligence accelerated virtual screening platform for drug discovery

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we developed a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screened multi-billion compound libraries against two unrelated targets, a novel ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. On both targets, we discover hits, including seven novel hits (14% hit rate) to KLHDC2 and four novel hits (44% hit rate) to NaV1.7 with single digit micromolar binding affinities. Screening in both cases was completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

biochemistry↗