AI and Proteomics Accelerate VAV1 Molecular Glue Discovery

Molecular glues act as matchmakers with a destructive streak: They bring disease-linked proteins into contact with the cell’s disposal machinery, marking them for elimination. Now, researchers have combined high-throughput proteomics with artificial intelligence (AI) to discover and optimize molecular glues that degrade VAV1, an immune-cell signaling protein implicated in blood cancers and autoimmune diseases.

The Baylor College of Medicine-led study, “Leveraging high-throughput proteomics and AI-based protein folding to accelerate VAV1 molecular glue discovery,” was published in Nature Communications. The team was led by senior and co-corresponding author Jin Wang, PhD, director of Baylor’s Center for NextGen Therapeutics, and first and co-corresponding author Hanfeng Lin, PhD, a postdoctoral researcher in Wang’s laboratory.

“Many scientists are increasingly exploring a new way to treat disease: instead of blocking harmful proteins, they aim at eliminating them entirely,” Wang said. Because degradation via molecular glue removes the whole protein rather than inhibiting one of its functions, the strategy could produce a more complete therapeutic effect.

“To find compounds capable of degrading VAV1, we screened a library of molecules using high-throughput proteomics, a technology that can assess thousands of proteins simultaneously. This unbiased analysis revealed a series of compounds, including NGT-201-12, that caused VAV1 levels to drop while affecting relatively few other proteins,” added Lin. Follow-up studies showed that degradation depended on the proteasome and cereblon (CRBN), a component of the cell’s protein-degradation pathway.

The team then developed GluePlex, a computational workflow that integrates AI-based protein-structure prediction with physics-based modeling. Without relying on an experimental structure of the ternary complex, GluePlex modeled how VAV1, CRBN, and the molecular glue assemble. “The model identified a specific region of VAV1, known as the SH3-2 domain, as being essential for degradation. Experimental tests confirmed the prediction and pinpointed the exact spot the glue uses: a small surface loop on VAV1 that acts as a degradation signal, or ‘degron.’ This loop is different from degradation signals commonly associated with cereblon-targeting molecular glues,” Lin said.

“Applying Free Energy Perturbation (FEP) to predicted ternary structures yields cooperativity metrics that correlate with degradation potency, overcoming limitations of standard docking and enabling prospective ranking of analogs—even from weak initial binders,” the authors wrote. Adding halogen substitutions restricted molecular flexibility and improved degradation efficiency, producing NGT-201-18, a more potent degrader that formed a stronger degradation complex. In primary human T cells, NGT-201-18 reduced VAV1 levels and suppressed T-cell activation. Dose-response proteomics identified VAV1 as the principal target but also revealed degradation of LIMD1, an off-target carrying a canonical G-loop degron. The finding shows that one molecular glue can engage structurally distinct degrons and underscores the importance of proteome-wide profiling.

The compounds remain preclinical, and additional studies will be needed to assess their pharmacology, safety, selectivity, and activity in disease models. Still, the work offers both a starting point for therapies aimed at VAV1-driven autoimmune disorders and hematologic malignancies and a broader discovery strategy. “This work introduces a series of VAV1-targeting molecular glues and, just as importantly, shows how artificial intelligence, structural modeling and proteomics can work together at the earliest stage of a project,” Wang said.

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