De novo protein design in an expanded chemical space 1 CheMoDesign is a generative framework that treats chemistry (PTMs, genetically encoded noncanonical amino acids, and post-expression chemical modifications) as a user-specified design variable, enabling de novo proteins whose binding and reactivity causally depend on the chosen chemical group. 2 Method core: it repurposes a pretrained all-atom structure-prediction model (Boltz-2) without retraining by weakening Pairformer pair-representation conditioning (zij → (1 − α)zij) to increase structural exploration, while preserving “chemical/functional anchors” that must remain fixed in the interface. 3 The workflow separates “dreaming” and “waking”: dreaming samples diverse target-associated backbones under weakened conditioning; waking performs sequence-conditioned truncated denoising to repair local geometry and reconcile the backbone with realizable sequences, improving sequence–structure compatibility across multiple targets. 4 Chemical anchors are implemented at atom-level: a parent residue representation is retained while an atom-level moiety is appended with correct covalent topology; all pairs involving the chemical moiety are protected during scaling/optimization, letting the model organize interfaces around the specified chemistry. 5 Retrospective validation: on native PTM-dependent complexes (acetyllysine, trimethyllysine, phosphotyrosine), CheMoDesign could recover native-like functional-group geometry (RMSD < 2 Å among passing designs), and “chemical ablation” back to canonical residues reduced predicted interface quality (ipTM drops of ~0.36–0.64), supporting modification dependence. 6 Prospective PTM design: sulfotyrosine (TYS) binders to thrombin were generated at multiple epitopes; 5/11 expressed designs all bound, with KD values spanning ~165–1193 nM, and Tyr substitution weakened binding across epitopes, indicating the designed sulfate group contributed to recognition. 7 Another PTM case: 3-nitrotyrosine (3-NT) binders to HER2 were designed without relying on an existing HER2–3-NT structural template; tested pairs showed 3-NT variants bound ~2.5–3.3× tighter than Tyr variants (e.g., KD ~236 nM vs ~780 nM), consistent with chemistry-guided interface formation. 8 Genetically encoded ncAAs enabled programmable behaviors beyond affinity: a tetrazine-bearing residue dominated IL-7Rα binding (KD ~8.4 nM; Ala/Phe substitutions >2 μM) and allowed post-production “rewriting” via tetrazine–TCO ligation, yielding payload-dependent affinity switching (including near-blocking by PEG-20K). 9 Reactivity as a designed output: pBpa-containing binders supported light-triggered covalent capture (rapid crosslinking upon 365 nm irradiation, including on-cell retention), while FSY-containing binders enabled proximity-driven SuFEx covalency to Trop-2 with crosslinking MS evidence for the intended linkage. 10 Post-expression chemistry broadened the accessible chemical space: installing a benzenesulfonamide modification on a designed Cys produced an isoform-selective CA IX binder/inhibitor (KD ~45 nM; weak CA XII binding; ~98% CA IX inhibition at 1 μM), and a photolabile 2-nitrobenzyl alcohol-derived modification enabled light-activated covalent PD-L1 capture with competitor-resistant cellular retention dependent on PD-L1 Lys75. 💻Code: github.com/Yuxuan-Lee/CheMoD… 📜Paper: biorxiv.org/content/10.64898… #ProteinDesign #GenerativeAI #ComputationalBiology #ChemicalBiology #NoncanonicalAminoAcids #PostTranslationalModifications #CovalentInhibitors #BioorthogonalChemistry #Boltz2 #AlphaFold3

Towards Accurate Prediction of Mutation-Induced Changes in Protein Structure 1. The paper builds a quantitative framework to measure how single amino-acid mutations deform protein structures, using matched wild-type and mutant X-ray crystal structures from the PDB and explicitly controlling for “native” structural variability. 2. Key dataset contribution: 6,967 wild-type–mutant sequence pairs, leveraging 27,200 wild-type and 16,176 mutant X-ray structures, with each wild-type sequence required to have at least five experimental “duplicate” structures to estimate intrinsic fluctuations. 3. Core metric: for each residue i, local deformation Di is defined from RMS changes in Cα–Cα distances to its local Voronoi neighbors (rather than global RMSD), reducing sensitivity to rigid-body motion and focusing on local rearrangements around mutations. 4. Innovation that removes thermal/experimental noise: normalized mutation-induced deformation eDi = (average deformation between wild-type duplicates vs mutant structures) divided by (average deformation among wild-type duplicates). This isolates mutation-specific effects from baseline structural variability. 5. Main structural finding: mutation-induced changes are strongly localized. Averaged across proteins, deformation peaks at the mutation site and decays rapidly with distance, approaching a near-baseline plateau for spatial distances r > ~12–15 Å (and similarly within ~±10 residues along sequence). 6. Distributional result: eDi at mutation sites is roughly exponential—many mutations look “neutral” structurally (eDi ≈ 1), while a smaller fraction induce substantially larger local deformations. 7. AlphaFold3 evaluation: predicted mutant structures were generated (AF3 v3.0.1; up to 20 seeds per mutant, selecting top-ranked per seed). Correlation between predicted and experimental normalized deformation at the mutation site is moderate overall (Pearson ρ ≈ 0.55) but collapses for strongly perturbative mutations (down to ρ ≈ 0.2 at high deformation Z-score cutoffs). 8. Important nuance: unnormalized comparisons can look deceptively good because mutant deformation correlates with wild-type duplicate fluctuations. After normalization, AF3 appears biased toward wild-type-like ensembles and struggles specifically where mutations cause large structural responses. 9. Distance dependence is weaker than magnitude dependence: the correlation between predicted and experimental normalized deformation decreases only modestly with distance (from ~0.55 at r = 0 to ~0.35 far away), while the dominant failure mode is large mutation-induced deformation. 10. Physical interpretability: a single feature—normalized local change in relative solvent accessibility (^ΔrSASA) computed from experimental structures—correlates strongly with deformation (ρ ≈ 0.6) and, unlike AlphaFold3, the correlation does not degrade for strongly perturbative mutations; however, it currently cannot be used directly for prediction because it requires the mutant structure. 💻Code: github.com/lzyttxs/ 📜Paper: arxiv.org/abs/2609.24842 #ProteinStructure #Mutations #AlphaFold3 #ComputationalBiology #StructuralBiology #Bioinformatics #PDB #SASA #ProteinEngineering

EnZight: A Structure-Guided Algorithm to Identify and Prioritize Substitution Hotspots for Enzyme Engineering 1. EnZight is a structure-guided workflow that proposes “substitution hotspots” in protein cores: positions where the local 3D environment is conserved across homologs, but the amino acid identity varies—suggesting mutations likely to preserve fold integrity while tuning activity and thermostability. 2. The central idea is to translate tolerated natural variation from homologous structures into practical, small mutagenesis sets enriched for functional variants, addressing a common bottleneck in enzyme engineering where many random mutations break folding or solubility. 3. Method overview: starting from a query structure (experimental or predicted), EnZight retrieves homologous structures via Foldseek (or accepts user-provided homologs), performs structural alignments, filters poor alignments (default RMSD cutoff 5 Å), and computes per-residue conservation scores (0–100). 4. A key technical component is the structure-based multiple sequence alignment (SB-MSA): EnZight refines a standard sequence MSA using gaps implied by the structural alignment, improving correspondence in regions with low sequence similarity but conserved structure. 5. Hotspot calling: EnZight scans mainly the protein core and flags positions where the query differs from at least one homolog while maintaining an equivalent local neighborhood; it also supports coupled double substitutions when the combined neighborhood matches a homolog. 6. Outputs are designed for hands-on engineering: (i) an interactive PyMOL-ready view with synchronized sequence/structure/selection panels for rapid inspection of local packing and contacts, and (ii) a ranked table of single and double substitutions scored by an “EnZight score” reflecting variability of the position plus its environment (higher score = less conserved environment, prioritized as more tolerant). 7. Experimental validation used u10, a polyurethane-urethane-bond cleaving amidase. From 36 predicted single-site hotspots, 34 variants were cloned and 33 expressed (97%), consistent with the goal of proposing fold-tolerant substitutions that avoid the usual high fraction of nonfunctional mutants. 8. Several single substitutions improved activity and/or stability, and beneficial substitutions combined additively (minimal epistasis): combinations of V44I, H90T, S151C, S151P showed observed ΔTm,app matching sums of single-mutant effects. The best triple mutant achieved ~6-fold higher catalytic efficiency (kcat/Km) and +2 °C apparent melting temperature. 9. Mechanistic/structural support: X-ray structure of the best variant (ITP) showed the global fold is preserved (Cα RMSD ~0.32 Å vs WT) and substitutions act via local interactions—e.g., V44I improves hydrophobic packing; H90T stabilizes a hydrogen-bond network near catalytic K79; S151P likely stabilizes a loop segment containing cis-S154 linked to catalysis. 10. Generality signals: activity gains were largely substrate-independent across ester/amide/carbamate model substrates and extended to the pharmaceutical micropollutant flutamide; EnZight score correlated with activity improvements (Spearman ρ ~0.56 for DUE–MDA; ~0.39 for flutamide), suggesting the ranking can help design smaller, more focused libraries. 📜Paper: biorxiv.org/content/10.64898… #EnzymeEngineering #ProteinDesign #StructuralBioinformatics #DirectedEvolution #ComputationalBiology #Biocatalysis #PlasticRecycling #Amidase #ProteinStructure #BioinformaticsTools