Miniproteins against SARS-CoV-2: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is decorated with spikes, and viral entry into cells is initiated when these spikes bind to the host angiotensin-converting enzyme 2 (ACE2) receptor. Many monoclonal antibody therapies in development target the spike proteins. Cao et al. designed small, stable proteins that bind tightly to the spike and block it from binding to ACE2. The best designs bind with very high affinity and prevent SARS-CoV-2 infection of mammalian Vero E6 cells. Cryo–electron microscopy shows that the structures of the two most potent inhibitors are nearly identical to the computational models. Unlike antibodies, the miniproteins do not require expression in mammalian cells, and their small size and high stability may allow formulation for direct delivery to the nasal or respiratory system. Science , this issue p. 426

Endocytosis and lysosomal trafficking of cell surface receptors can be triggered by endogenous ligands. Therapeutic approaches such as lysosome-targeting chimaeras^1,2 (LYTACs) and cytokine receptor-targeting chimeras³ (KineTACs) have used this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. Although powerful, these approaches can be limited by competition with native ligands and requirements for chemical modification that limit genetic encodability and can complicate manufacturing, and, more generally, there may be no native ligands that stimulate endocytosis through a given receptor. Here we describe computational design approaches for endocytosis-triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for insulin-like growth factor 2 receptor (IGF2R) and asialoglycoprotein receptor (ASGPR), sortilin and transferrin receptors, and show that fusing these tags to soluble or transmembrane target protein binders leads to lysosomal trafficking and target degradation. As these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. EndoTag fusion to a PD-L1 antibody considerably increases efficacy in a mouse tumour model compared to antibody alone. The modularity and genetic encodability of EndoTags enables AND gate control for higher-specificity targeted degradation, and the localized secretion of degraders from engineered cells. By promoting endocytosis, EndoTag fusion increases signalling through an engineered ligand–receptor system by nearly 100-fold. EndoTags have considerable therapeutic potential as targeted degradation inducers, signalling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody–drug and antibody–RNA conjugates.

Designing new enzymes typically begins with idealized arrangements of catalytic functional groups around a reaction transition state, then attempts to generate protein structures that precisely position these groups. Current AI-based methods can create active enzymes but require predefined residue positions and rely on reverse-building residue backbones from side-chain placements, which limits design flexibility. Here we show that a new deep generative model, RoseTTAFold diffusion 2 (RFdiffusion2), overcomes these constraints by designing enzymes directly from functional group geometries without specifying residue order or performing inverse rotamer generation. RFdiffusion2 successfully generates scaffolds for all 41 active sites in a diverse benchmark, compared to 16 using previous methods. We further design enzymes for three distinct catalytic mechanisms and identify active candidates after experimentally testing fewer than 96 sequences in each case. These results highlight the potential of atomic-level generative modeling to create de novo enzymes directly from reaction mechanisms.

Intrinsically disordered proteins and peptides play key roles in biology, but a lack of defined structures and high variability in sequence and conformational preferences have made targeting such systems challenging. We describe a general approach for designing proteins that bind intrinsically disordered protein regions in diverse extended conformations with side chains fitting into complementary binding pockets. We used the approach to design binders for 39 highly diverse unstructured targets, including polar targets, and obtained designs with 100-picomolar to 100-nanomolar affinities in 34 cases, testing ~22 designs per target. The designs function in cells and as detection reagents and are specific for their intended targets in all-by-all binding experiments. Our approach is a major step toward a general solution to the intrinsically disordered protein and peptide recognition problem.

We describe an approach for designing high-affinity small molecule–binding proteins poised for downstream sensing. We use deep learning–generated pseudocycles with repeating structural units surrounding central binding pockets with widely varying shapes that depend on the geometry and number of the repeat units. We dock small molecules of interest into the most shape complementary of these pseudocycles, design the interaction surfaces for high binding affinity, and experimentally screen to identify designs with the highest affinity. We obtain binders to four diverse molecules, including the polar and flexible methotrexate and thyroxine. Taking advantage of the modular repeat structure and central binding pockets, we construct chemically induced dimerization systems and low-noise nanopore sensors by splitting designs into domains that reassemble upon ligand addition.

Proteins that bind to intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) with high affinity and specificity could be useful for therapeutic and diagnostic applications^1,2,3,4. However, a general methodology for targeting IDPs or IDRs has yet to be developed. Here we show that starting only from the target sequence of the input, and freely sampling both target and binding protein conformations, RFdiffusion⁵ can generate binders to IDPs and IDRs in a wide range of conformations. We used this approach to generate binders to the IDPs amylin, C-peptide, VP48 and BRCA1_ARATH in diverse conformations with a dissociation constant (K_d) ranging from 3 to 100 nM. For the IDRs G3BP1, common cytokine receptor γ-chain (IL-2RG) and prion protein, we diffused binders to β-strand conformations of the targets, obtaining K_d between 10 and 100 nM. Fluorescence imaging experiments show that the binders bind to their respective targets in cells. The G3BP1 binder disrupts stress granule formation in cells, and the amylin binder inhibits amyloid fibril formation and dissociates existing fibres, enables targeting of both monomeric and fibrillar amylin to lysosomes, and increases the sensitivity of mass spectrometry-based amylin detection. Our approach should be useful for creating binders to flexible IDPs or IDRs spanning a wide range of intrinsic conformational preferences.