De novo Rubisco design with protein language models 1 Rubisco is the dominant biological CO2-fixing enzyme, but its low CO2/O2 specificity and modest catalytic rates have made engineering hard because folding, assembly, and catalysis are tightly coupled. 2 The paper presents RubisGen: a generative–discriminative pipeline that uses a ProGen-2 generator fine-tuned on non-Form I Rubiscos (to avoid small-subunit/chaperone complexities) plus an ESM-2 discriminator to prioritize natural-like yet novel sequences. 3 Starting from sequence-only generation, the workflow sampled ~5.6 million unique Rubisco-like sequences (top-p 0.5/0.75/1.0), then applied a multi-stage funnel: discriminator p(generated), generator loss, identity binning, MMseqs2 clustering, AlphaFold-multimer, Foldseek vs PDB, and manual active-site completeness checks. 4 This funnel reduced 5.6 million sequences to 1,375 for structural evaluation and finally to 21 diverse candidates for experiments, spanning multiple Rubisco lineages and including placements on deep/sparsely sampled phylogenetic branches rather than converging on a single template. 5 Experimental screening in E. coli showed 6/21 designs were soluble and 5/21 produced quantifiable 3-phosphoglycerate (3PGA) in an endpoint LC–MS assay, demonstrating that sequence-only generation plus independent structural filtering can recover active Rubiscos outside known genomic sequence neighborhoods. 6 Among the active designs, apparent endpoint 3PGA activities fell within the distribution of a phylogenetically diverse natural panel; two designs (GEN_8 and GEN_11) reached the higher-activity region measured for the generated set. 7 The study also reports an endpoint-based apparent CO2/O2 specificity estimate (SC/O,app). One design (GEN_8) produced the highest SC/O,app in the measured panel (5.62 ± 0.37), exceeding the natural representatives assayed here, while noting that endpoint SC/O,app is not the conventional kinetic specificity factor and requires follow-up kinetics. 8 A key mechanistic insight is that no single conserved residue or simple static structural feature explained activity differences: across 127 tested sequence/structure correlates, none remained significant after FDR correction except a composition proxy linked to solubility (lower signed basic-minus-acidic residue fraction in soluble designs). 9 Structural validation: the authors solved a 1.40 Å crystal structure of a divergent, low-performing design (GEN_10). Despite low overall sequence identity (~39% across a gapped alignment), the protein formed the canonical L2 Rubisco dimer, retained carbamylated catalytic lysine and Mg2+, and matched the AlphaFold model with sub-angstrom Cα agreement—supporting that fold preservation can be separable from catalytic optimization. 📜Paper: biorxiv.org/content/10.64898… #ComputationalBiology #ProteinDesign #ProteinLanguageModels #Rubisco #EnzymeEngineering #CarbonFixation #AlphaFold #StructuralBiology #LCMS #SyntheticBiology

De novo design of allosterically controlled enzymes 🚀 New preprint from David Baker!🚀 1. The study presents a general de novo strategy to build compact enzymes whose catalytic activity is switched on or off by binding of a separately designed protein effector at a distal site, aiming to mimic natural allostery without domain fusions or large mechanical rearrangements. 2. Core idea: engineer an energy landscape with two nearby conformational states—an active state with catalytically optimal residue geometry and an inactive state with subtly distorted active-site geometry—then design an effector that selectively stabilizes one state to shift the equilibrium (activation or repression). 3. Methodological innovation: use partial RFdiffusion (add ~1–3 Å Gaussian noise then denoise) to generate alternative backbone conformations close to the starting enzyme fold, enriching for subtle but functionally disruptive active-site perturbations rather than large-scale motions. 4. Pipeline overview: start from a de novo serine hydrolase (Win1), generate inactive conformers by partial diffusion (with domain-wise perturbations to keep state energies close), design state-selective binders with RFdiffusion (30–80 aa), and perform multistate-compatible sequence design with tied ProteinMPNN across enzyme/effector and active/inactive contexts. 5. Allosteric activators: from 61 designed enzyme–effector pairs screened in E. coli lysates (4Mu-Ac substrate), two systems (Janus1, Janus2) showed effector-dependent activity increases and were validated with purified proteins; predicted state switching involved remodeling that restores catalytic geometry upon effector binding. 6. Activator kinetics and specificity: Janus1 kcat increased ~5-fold (0.0033→0.017 s⁻1) and Janus2 increased ~2.6-fold (0.00050→0.0013 s⁻1) at 1:2 enzyme:effector; cross-testing showed cognate specificity (non-cognate effectors did not enhance activity). 7. Allosteric inhibitors: designs were built on hydrolases for a bulkier substrate (4Mu-PhAc). A serine-to-cysteine substitution was found to broadly boost baseline catalytic efficiency in these designs and was used to increase dynamic range for observing inhibition. 8. Inhibitor performance: among 24 screened inhibitor pairs, Janus3 showed clear effector-dependent repression; purified-protein kinetics showed ~8-fold reduction in kcat upon effector binding (0.0024→0.0003 s⁻1) with little change in Km, consistent with modulation of catalytic geometry rather than substrate binding. 9. Structural validation and binding biophysics: a 1.7 Å crystal structure of the Janus3 enzyme–effector complex closely matched the design at the interface (Cα RMSD ~0.77 Å), confirming distal binding and conformational coupling. SPR measured strong affinities (Janus1 KD ~2.97 nM; Janus2 ~0.726 nM; Janus3 ~106 nM) and fast on-rates; regulation in spike-in experiments occurred within assay dead time (~21 s). 10. Coupling between catalysis and effector binding: for Janus2 and Janus3, substrate did not significantly affect effector affinity; Janus1 showed weaker binding at high substrate (KD shift ~3→13 nM), and experiments with a non-hydrolyzable analog suggested the effect is linked to catalytic turnover/acyl-enzyme intermediate formation rather than simple substrate occupancy. 📜Paper: biorxiv.org/content/10.64898… #ProteinDesign #Allostery #EnzymeEngineering #GenerativeAI #DiffusionModels #RFdiffusion #ProteinMPNN #SyntheticBiology #StructuralBiology #ComputationalBiology