Marc Expòsit

This paper introduces scalable experimental and computational workflows to accelerate protein design by improving throughput, reducing costs, and enabling rapid validation of thousands of protein designs.

This paper demonstrates that AI-designed receptor modulators can drive efficient human fibroblast-to-muscle transdifferentiation by rewiring extracellular signaling.

The paper designs novel cytokines by fusing computationally generated receptor-binding domains, identifying several that activate signaling pathways and have specific biological effects.

This paper designs synthetic cytokine agonists with controlled receptor geometry to modulate signaling outcomes and functional effects.

This paper designs a synthetic ligand, H2F, that induces HER2-FGFR proximity to reprogram fibroblasts into myogenic cells and promote myogenic regeneration.

The paper designs overlapping genes using deep generative models of protein sequences and shows high success rates in creating functional proteins in alternate reading frames.

This paper designs de novo peptide masks to reversibly inactivate miniprotein binders, demonstrating their effectiveness in reducing EGFR antagonist binding and enabling conditional activation via stimuli like proteases or light.