Che,Yang

Mapping Targetable Sites on the Human Surfaceome for the Design of Novel Binders 1. A groundbreaking study maps the human surfaceome, identifying 4,500 targetable sites across 2,886 cell-surface proteins. This resource unlocks new therapeutic opportunities for precision medicine. 2. The study leverages MaSIF, a geometric deep-learning framework, to predict protein-protein interaction sites. Nearly 3 billion docking runs were performed to generate high-quality binder "seeds" for targeted design. 3. A novel web platform, SURFACE-Bind, is introduced. It provides open access to predicted binding sites, corresponding binder seeds, and data visualization tools. This resource aids drug discovery and protein design. 4. Experimental validation highlights three critical targets—FGFR2, IFNAR2, and HER3. De novo-designed binders showed high success rates, targeting key interfaces with nanomolar to micromolar affinities. 5. The team optimized protein design pipelines by integrating ProteinMPNN and AlphaFold2, improving biophysical properties like stability and binding affinity, achieving 11-fold higher success rates in subsequent rounds. 6. A peptide design pipeline was also developed. Interface motifs from mini-protein binders were stabilized as cyclized peptides, yielding 5 target-specific peptides with demonstrated binding activity. 7. This work underscores the power of integrating deep learning and physics-based methods to advance de novo protein and peptide design for therapeutic applications, targeting underexplored surfaceome regions. 8. By combining computational innovation with experimental validation, this research sets a new benchmark for precision protein engineering and opens the door to the next generation of biologics. @befcorreia @hamed_khakzad @yangche7 @SiFulle @J_Damjanovic_ 💻Code: github.com/hamedkhakzad/SURF… 📜Paper: biorxiv.org/content/10.1101/… #ProteinDesign #DeepLearning #Surfaceome #ComputationalBiology #DrugDiscovery #MachineLearning