TRACER navigates rearrangement-driven sesterterpene chemical space via multimodal enzyme-product representation learning

Machine-Learning-Enabled Rapid Evolution of Photoenzymes for the Asymmetric Synthesis of gem-Difluorophosphonates.

gem-Difluorophosphonates are pivotal structural motifs in pharmaceuticals and bioactive molecules. While photoenzymatic catalysis provides a powerful platform to overcome the challenges of enantioselective synthesis, engineering enzymes for non-natural transformations remains an arduous, labor-intensive process. Although predictive methods utilizing protein language models (PLMs) offer fitness landscape guidance, they often struggle to generalize across diverse protein families or accurately map sequence to catalytic activity. Here, we report a small-sample, accelerated evolution strategy that integrates focused rational iterative site-specific mutagenesis (FRISM) with the EVOLVEpro model. This synergistic approach identifies high-activity and enantiospecific variants through structure-based hotspot identification and active learning, requiring minimal experimental throughput. By screening only 40 variants over three evolutionary rounds, we identified four beneficial mutations whose combinations enable the synthesis of diverse fluorinated products with up to > 99% yield and 98:2 enantiomeric ratio (e.r.)-a 65% reduction in workload compared to exhaustive screening. Mechanistic investigations suggest an electron donor-acceptor (EDA)-complex-free radical addition pathway, terminated by the flavin semiquinone (FMNsq) or the active-site residue Y343. This study provides a robust, "lightweight" machine learning framework for the rapid development of new-to-nature photoenzymatic transformations.

Mechanistic Origins of Enhanced PET Hydrolase Activity from Molecular Dynamics and Deep Learning-Assisted Network Analysis

Polyethylene terephthalate (PET) hydrolase-based biodegradation offers a promising route for plastic waste remediation, yet the dynamic origins of high activity and their linkage across catalytic stages still require further elucidation. Here, we focus on the Leaf-Branch Compost Cutinase (LCC) system to reveal the possible mechanistic origins underlying the elevated activity of the LCC variants. By integrating molecular dynamics simulations, enhanced sampling, and deep learning-assisted network analysis, we systematically investigate the critical prereactive stage of amorphous PET adsorption and substrate binding. We identify three mechanistic origins underlying the high activity of LCC-LANL in the prereactive state and clarify its structure–dynamics–function relationship: (i) enhanced interaction strength coupled with an active-pocket orientation that, despite not directly facing the amorphous PET surface, maintains closer proximity to it than LCC-WT, thus promoting substrate recruitment; (ii) higher occupancy of the PET ester bond near the catalytic triad, which forms the basis for catalysis, coupled with the efficient dynamic interchange between the “W” and coiled conformations near the catalytic triad; and (iii) the enhancement of prereactive organization through long-range allosteric communication by distal mutations in LCC-LANL. Additionally, we propose a region-specific cooperative optimization strategy tailored to domain-specific functional roles and distill six design principles for efficient PETases. In summary, this work elucidates the prereactive origins underlying the high activity of LCC-LANL, paving the way for future studies on actual catalytic PET hydrolysis.

QSAR-ML- and Metadynamics-Guided Design of Symmetrical Bis-Indanones to Overcome Mutational Anchor Loss in Acetylcholinesterase

Background/Objectives: Symmetrical dual-site acetylcholinesterase (AChE) inhibitors offer a compelling strategy to mitigate mutational drug resistance, yet static modeling fails to capture induced-fit dynamics under mutational stress. Methods: Here, a 100,000-compound virtual library was filtered using a machine learning-based QSAR classification pipeline. A strict, empirically calibrated Jaccard applicability domain filter (AD = 0.823) eliminated topological anomalies, yielding a robust cross-validation accuracy (ROC-AUC: 0.80 ± 0.05; independent test MCC: 0.61). Multi-parameter ADMET and shape screening prioritized unique chemotypes to probe the 20 Å enzyme gorge. All-atom explicit-solvent molecular dynamics simulations were coupled with 150 ns enhanced-sampling Metadynamics along two orthogonal collective variables (gorge depth and ligand orientation) to map out the free energy surfaces under mutational stress. Results: Symmetrical probes suffered catastrophic unbinding upon anchor loss. Conversely, the symmetrical core of Lead Compound 1631 demonstrated extraordinary structural resilience. In silico site-directed mutagenesis (W86A and W286A) triggered a thermodynamic locking effect; the W86A mutant forced the complex into a deeper energetic well (ΔGmin = 9.23 ± 1.98 kJ/mol) than the wild-type state (5.26 ± 1.69 kJ/mol). MM/GBSA decomposition confirmed an active electrostatic-solvation compensation mechanism along a “solvation see-saw” diagonal (ΔΔGtotal = +1.59 kcal/mol). Finally, Dynamic Cross-Correlation Matrix analysis quantified a mechanical inversion of the CAS-PAS axis into an anti-correlated clamping mode (−0.04) that locked the ligand bridge in place. Conclusions: These results demonstrate that symmetrical dual-site targeting, combined with dynamic thermodynamic locking, provides a resilient framework to overcome mutational resistance in AChE inhibitors.

CatESO: Differentiable Enzyme Sequence Optimization Guided by Substrate-Aware kcat Prediction

Elucidating enzyme–substrate specificity through co-folding foundation model

Enzymatic catalysis relies on precise structural and chemical complementarity, yet systematically mapping enzyme-substrate interactions remains a critical bottleneck. While structure-aware methods have advanced functional annotation, their reliance on predefined binding pockets and rigid-body docking fails to capture the ligand-induced conformational changes essential for catalytic turnover. Here we introduce Boltz2ESI, an end-to-end framework that predicts enzyme–substrate interactions by leveraging structural knowledge learned by a biomolecular foundation model. Through native co-folding, the framework inherently captures active-site plasticity without requiring predefined pocket annotations. Integrating these learned biophysical priors with global evolutionary context and geometric molecular descriptors, Boltz2ESI consistently outperforms state-of-the-art sequence-based and rigid-docking approaches. Extensive validation demonstrates that the framework accurately discriminates tight sub-family specificities, enabling effective candidate prioritization for biosynthetic pathway elucidation, as demonstrated on the withanolide pathway. Ultimately, this structure-dynamic approach establishes an actionable foundation for accelerating rational biocatalyst discovery and large-scale pathway de-orphaning.

Diffusion-Based Protein Structure Design: Geometric Modelling, Validation Strategies, and Thermodynamic Challenges

This review focuses on coordinate- and residue-frame-based diffusion approaches for generating protein structures, paying particular attention to geometric equivariance, conditioning strategies, all-atom modelling and interaction-aware design.