Computational design of serine hydrolases.

Enzymes that proceed through multistep reaction mechanisms often utilize complex, polar active sites positioned with sub-angstrom precision to mediate distinct chemical steps, which makes their de novo construction extremely challenging. We sought to overcome this challenge using the classic catalytic triad and oxyanion hole of serine hydrolases as a model system. We used RFdiffusion1 to generate proteins housing catalytic sites of increasing complexity and varying geometry, and a newly developed ensemble generation method called ChemNet to assess active site geometry and preorganization at each step of the reaction. Experimental characterization revealed novel serine hydrolases that catalyze ester hydrolysis with catalytic efficiencies (k cat /K m ) up to 3.8 x 103 M-1 s-1, closely match the design models (Cα RMSDs < 1 Å), and have folds distinct from natural serine hydrolases. In silico selection of designs based on active site preorganization across the reaction coordinate considerably increased success rates, enabling identification of new catalysts in screens of as few as 20 designs. Our de novo buildup approach provides insight into the geometric determinants of catalysis that complements what can be obtained from structural and mutational studies of native enzymes (in which catalytic group geometry and active site makeup cannot be so systematically varied), and provides a roadmap for the design of industrially relevant serine hydrolases and, more generally, for designing complex enzymes that catalyze multi-step transformations.

Diffusing protein binders to intrinsically disordered proteins.

Proteins which bind intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) with high affinity and specificity could have considerable utility for therapeutic and diagnostic applications. However, a general methodology for targeting IDPs/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 conformation, RFdiffusion can generate binders to IDPs and IDRs in a wide range of conformations. We use this approach to generate binders to the IDPs Amylin, C-peptide and VP48 in a range of conformations with Kds in the 3 -100nM range. The Amylin binder inhibits amyloid fibril formation and dissociates existing fibers, and enables enrichment of amylin for mass spectrometry-based detection. For the IDRs G3bp1, common gamma chain (IL2RG) and prion, we diffused binders to beta strand conformations of the targets, obtaining 10 to 100 nM affinity. The IL2RG binder colocalizes with the receptor in cells, enabling new approaches to modulating IL2 signaling. Our approach should be widely useful for creating binders to flexible IDPs/IDRs spanning a wide range of intrinsic conformational preferences.

De novo designed proteins neutralize lethal snake venom toxins.

Snakebite envenoming remains a devastating and neglected tropical disease, claiming over 100,000 lives annually and causing severe complications and long-lasting disabilities for many more1,2. Three-finger toxins (3FTx) are highly toxic components of elapid snake venoms that can cause diverse pathologies, including severe tissue damage3 and inhibition of nicotinic acetylcholine receptors (nAChRs) resulting in life-threatening neurotoxicity4. Currently, the only available treatments for snakebite consist of polyclonal antibodies derived from the plasma of immunized animals, which have high cost and limited efficacy against 3FTxs5,6,7. Here, we use deep learning methods to de novo design proteins to bind short- and long-chain α-neurotoxins and cytotoxins from the 3FTx family. With limited experimental screening, we obtain protein designs with remarkable thermal stability, high binding affinity, and near-atomic level agreement with the computational models. The designed proteins effectively neutralize all three 3FTx sub-families in vitro and protect mice from a lethal neurotoxin challenge. Such potent, stable, and readily manufacturable toxin-neutralizing proteins could provide the basis for safer, cost-effective, and widely accessible next-generation antivenom therapeutics. Beyond snakebite, our computational design methodology should help democratize therapeutic discovery, particularly in resource-limited settings, by substantially reducing costs and resource requirements for development of therapies to neglected tropical diseases.

Generalized biomolecular modeling and design with RoseTTAFold All-Atom.

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.