Targeting “Pocketless” Enzymes to Treat Cancer, Immune, and Other Diseases
Short Description:
Our platform enables the selective inhibition of HECT E3 ubiquitin ligases, key regulators of protein degradation-through a novel allosteric mechanism. Using advanced structural biology and machine learning-driven screening, we access a previously unreachable druggable space. Lead compounds identified by this approach have demonstrated in vivo efficacy in reversing pulmonary arterial hypertension (PAH) and modulating oncogenic pathways.
Unmet Need: HECT E3 ligases are implicated in cancer, neurodevelopmental disorders, vascular disease, viral infections, and immune dysregulation. Despite their therapeutic importance, they have remained largely “undruggable” due to the absence of a classical active-site pocket. To date, no selective inhibitors of HECT E3 ligases have reached clinical approval.
Our Solution: We developed a proprietary discovery platform that combines machine learning-based ligand prediction with a high-throughput, bacteria-based positive selection system, which minimizes false positives and enables efficient hit validation. This approach exposes cryptic allosteric pockets and blocks essential conformational changes in HECT E3 ligases. Structural insights from SMURF1 were successfully translated to other disease-linked HECTs, including E6AP (associated with autism) and NEDD4 (linked to cancer), enabling the identification of selective inhibitors. In vivo efficacy was demonstrated in PAH models via SMURF1 inhibition.

Unique Advantages
• First-in-class Mechanism: Targets a conserved glycine-hinge allosteric site across HECT ligases.
• High Selectivity: Potent inhibition of SMURF1 with minimal activity on closely related SMURF2.
• Broad Targetability: Extendable to other disease-related HECT ligases like E6AP (linked to cancer, Autism and Angelman syndromes).
• Clinically Relevant Efficacy: Reverses pathology in PAH models with a favorable safety profile.
• AI/ML Integration: Enables scalable, predictive inhibitor discovery for a large family of ligases.
• Opens a New Druggable Space: First method to enable rational drug discovery for catalytically exposed E3-ligases.
• Validated Mechanism: Structural resolution at atomic resolution confirms allosteric inhibition.
• Proven Platform Versatility: Applied to multiple chemical scaffolds and ligase targets.
• Reduced Toxicity: selectively inhibiting one ligase at a time.

Potential Applications
• Pulmonary Arterial Hypertension (PAH): Novel treatment by restoring BMP signaling.
• Cancer Therapy: Targeting E6AP, SMURF1, and other ligases regulating tumor growth and protein stability.
• Neurodevelopmental Disorders: Modulating E6AP activity implicated in Angelman syndrome and autism.
• Drug Discovery Platforms: Use in pharma for screening ligase inhibitors across therapeutic areas.

Research Achievements
• High-resolution structure of SMURF1 in complex with novel allosteric inhibitors
• Discovery of a cryptic allosteric pocket conserved across HECT ligases
• Lead compounds reversed PAH symptoms in two preclinical rodent models
• Achieved >100-fold selectivity for SMURF1 over SMURF2
• Demonstrated inhibition of E6AP in vitro and in cancer cell lines
• Developed a structure-guided AI pipeline for new inhibitor design
• Validated synergy with TGF-β/BMP signaling modulators

Current Status
• Lead compounds validated in preclinical PAH animal models (in vivo efficacy + safety)
• Efficacy shown in cancer cell lines and mechanistic studies
• AI-based platform established for rapid expansion to other HECT ligases
• Ready for drug development partnership or seed-stage investment to enter IND-enabling studies

Patents
• WO 2023/289024 – In Silico Method of Identifying Allosteric HECT E3-Ligase Inhibitors

References
• Rothman et al. Cell, 2025: Therapeutic Potential of Allosteric HECT E3 Ligase Inhibition
• Levin-Kravets et al. Nature methods, 2016: A bacterial genetic selection system for ubiquitylation cascade discovery.