Design Of Intrinsically Disordered Region Binding Proteins @ScienceMagazine 🚀 New paper published from David Baker!🚀 A groundbreaking computational approach has been developed to design proteins that can specifically bind to intrinsically disordered regions (IDRs), which are highly flexible and lack a defined 3D structure. This innovation addresses a significant challenge in protein engineering where traditional methods often struggle with such dynamic targets. The key breakthrough lies in leveraging the inherent flexibility of IDRs. Instead of being a hurdle, the designed binding proteins actually guide the disordered target sequences into specific, binding-competent conformations, enabling high-affinity and highly specific interactions. This is a paradigm shift from targeting rigid structures. This novel pipeline integrates physics-based methods with advanced deep learning techniques, specifically RFdiffusion, to create a versatile library of protein scaffolds with specialized binding pockets. This library allows for the precise recognition of diverse extended peptide conformations. The researchers demonstrated the effectiveness of their method by designing binders for 39 out of 43 diverse synthetic and therapeutically relevant IDRs, including those related to GPCR signaling and cancer. These binders consistently achieved picomolar to nanomolar affinities. The designed binders exhibited remarkable specificity, binding tightly to their intended targets even when faced with highly similar sequences, as confirmed by comprehensive all-by-all binding experiments. They also proved functional in cellular contexts for applications like enriching low-abundance proteins for proteomics and influencing protein localization. Structural validation through co-crystal structures and nuclear magnetic resonance (NMR) data corroborated the computational design, showing that the designed binders successfully induce the disordered targets into specific bound conformations. 💻Code: zenodo.org/records/14829674 📜Paper: science.org/doi/10.1126/scie… #ComputationalBiology #ProteinDesign #IDPs #DrugDiscovery #DeepLearning #StructuralBiology #Science