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.

A small-sample, accelerated evolution strategy that integrates focused rational iterative site-specific mutagenesis (FRISM) with the EVOLVEpro model is reported, providing 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

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

Boltz2ESI is introduced, an end-to-end framework that predicts enzyme–substrate interactions by leveraging structural knowledge learned by a biomolecular foundation model and consistently outperforms state-of-the-art sequence-based and rigid-docking approaches.

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.