Wayne

High-throughput physics-based enzyme engineering 1. The study presents a scalable, physics-based enzyme-engineering workflow that reaches quantum-mechanical accuracy for enzyme electrostatics while staying fast enough for high-throughput, atomistic free-energy calculations of multistep catalytic mechanisms. 2. A central contribution is MACEPOL-EF, an electric field-aware machine-learning interatomic potential (MLIP) designed for the unusually strong and heterogeneous electric fields found in protein active sites (roughly 0.49–1.60 V Å−1, consistent with Stark-effect measurements). 3. To train for that regime, the authors build BioPol-EF: a DFT-labeled dataset (~280k entries) where external electrostatic fields are systematically reconstructed around molecules to match protein-like, element-dependent field statistics, addressing a known failure mode of field-agnostic ML potentials in ML/MM. 4. Architecturally, MACEPOL-EF extends MACEPOLAR-1 with (i) per-atom charge–potential (qV) coupling so atoms can sense anisotropic external potentials/fields, and (ii) an adaptive polarization head that captures linear polarization plus higher-order response, while retaining physically meaningful atomic charges. 5. The paper implements a bidirectional electrostatic-embedding ML/MM scheme in AMBER: the ML region responds to MM-derived potentials/fields, while the MM region responds to on-the-fly ML electrostatics via analytic force propagation, yielding stable energy conservation and a physically consistent hybrid potential. 6. The practical target is an engineered oxidative amidase (OxiAm) derived from aldehyde dehydrogenase (ALDH) that forms amides from aldehydes + amines, competing against native hydrolysis (acid formation). Using enhanced sampling (OPES-MetaD), they compute full reaction free-energy landscapes and extract minimum-energy pathways and transition-state ensembles. 7. Mechanistically, they resolve a multistep pathway: Cys293 thiolate attack → tetrahedral intermediate → hydride transfer to NAD(P)+ → metastable thioester → branch point where either water (hydrolysis) or amine (aminolysis) attacks, with proton transfer mediated by a preorganized water-assisted H-bond network. Ensemble analysis shows early steps are rigid/preorganized, while hydrolysis/aminolysis have broad, heterogeneous TS ensembles. 8. A key modeling advance is microkinetic integration of multiple barriers (thioester formation, aminolysis, hydrolysis) rather than assuming a single rate-determining step. This improves kcat agreement with experiment (|Δlog| ~0.67 vs ~2.28 when approximating with aminolysis alone) and enables quantitative prediction of selectivity (amide/acid), including a saturation-mutagenesis panel at position 259. 9. The framework generalizes across substrate scope and homologous scaffolds: calculated log10(kcat) correlates with experimental yields across 25 aldehyde–amine combinations (r = 0.48), and the same four mutations (L163G, R166M, T234H, E259L) shift selectivity toward amidation across multiple ALDHs via scaffold-dependent atomistic mechanisms (sometimes improving selectivity at the cost of turnover, sometimes improving both). 10. In a pharma-oriented case study (ABBV-318 route), the authors use transition-state interaction energy decomposition to nominate redesign positions (including a non-obvious site F454), run virtual saturation mutagenesis with ML/MM free-energy barriers, and experimentally validate improved variants (e.g., G163C ~80% yield at pH 11; F454H ~60% yield at pH 7), illustrating how TS-ensemble physics can guide mutations beyond previously explored sites. 💻Code: github.com/ClickFF/MLMM4AMBE… ; github.com/Hsuchein/AutoNACC ; github.com/ClickFF/MACEPOL-E… 📜Paper: biorxiv.org/content/10.64898… #ComputationalBiology #EnzymeEngineering #Biocatalysis #MolecularSimulation #FreeEnergy #MLMM #QMMM #AMBER #MachineLearning #Microkinetics

UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems 1. The UBio-MolFM framework bridges the fundamental gap between quantum-mechanical accuracy and biological scale in molecular simulation, achieving ab initio-level fidelity on biomolecular systems up to ~1,500 atoms. 2. The model introduces three synergistic innovations: UBio-Mol26 dataset, E2Former-V2 architecture, and a Three-Stage Curriculum Learning protocol, each addressing critical limitations in existing approaches. 3. UBio-Mol26 is a large bio-specific dataset with 17 million configurations up to 1,200 atoms, constructed via a "Two-Pronged Strategy" combining bottom-up enumeration of biochemical building blocks with top-down sampling of native protein environments. 4. E2Former-V2 achieves linear-scaling equivariant transformation through Equivariant Axis-Aligned Sparsification (EAAS) and Long-Short Range (LSR) modeling, delivering up to ~4× higher inference throughput compared to strong equivariant baselines. 5. The Three-Stage Curriculum Learning protocol progressively transitions from energy initialization to energy-force consistency with force-focused supervision, effectively mitigating energy offsets across heterogeneous data sources. 6. Rigorous benchmarking demonstrates superior performance on out-of-distribution systems: substantial reductions in force and relative-energy errors compared to MACE-OMol and UMA-S-1p1, particularly for protein optimization and dynamics. 7. The model accurately reproduces key macroscopic observables in downstream MD simulations, including liquid water structure, ionic solvation with correct coordination numbers, solvent-dependent peptide conformations, and precise metal-ion coordination in RNA. 8. UBio-MolFM maintains the environmentally sensitive open and closed conformations of Cyclosporine A in water and vacuum respectively, capturing the competition between intramolecular hydrogen bonds and solvent stabilization. 9. For RNA dynamics, the model provides the most accurate structural description of Mg2+ coordination compared to Amber99 and UMA-S-1p1, correctly reproducing Mg-O distances and flexible angle distributions. 10. The framework represents a significant step toward "executable biology" where quantum-accurate simulations become routine tools for dissecting molecular mechanisms of life, with planned open-science release of pretrained weights and inference engine. 💻Code: github.com/IQuestLab/E2Forme… 📜Paper: arxiv.org/abs/2602.17709 #MolecularSimulation #MachineLearning #ComputationalBiology #ForceFields #QuantumChemistry #Bioinformatics #DeepLearning #MolecularDynamics #FoundationModel #EquivariantNetworks

Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Model 1. A new study titled "Apo2Mol" introduces a groundbreaking approach to structure-based drug design, leveraging a dynamic pocket-aware diffusion model to generate 3D molecules that account for protein flexibility. This innovation addresses a critical limitation in current methods that assume rigid protein binding pockets. 2. The core innovation of Apo2Mol lies in its ability to jointly generate ligands and their corresponding holo pocket conformations from apo states. This is achieved through a full-atom hierarchical graph-based diffusion model, which captures realistic protein-ligand interactions and conformational changes. 3. The researchers curated a high-quality dataset of over 24,000 experimentally resolved apo-holo structure pairs from the Protein Data Bank. This dataset provides a robust foundation for training the model, ensuring that it learns from real-world protein dynamics rather than relying on simulated data. 4. Empirical results demonstrate that Apo2Mol achieves state-of-the-art performance in generating high-affinity ligands. The model not only produces molecules with favorable binding affinities but also generates realistic pocket conformations, advancing the capabilities of dynamic structure-based drug design. 5. The study highlights the potential of Apo2Mol to generate novel ligands for emerging drug targets, even when only apo structures are available. This capability is crucial for practical drug discovery scenarios where holo structures are often unknown. 📜Paper: arxiv.org/abs/2511.14559v1 #DrugDesign #DiffusionModels #ProteinFlexibility #StructureBasedDesign #AIinBiology

ForceFM: Enhancing Protein-Ligand Predictions through Force-Guided Flow Matching 1. ForceFM introduces a novel force-guided flow matching model that integrates physical principles into deep learning for molecular docking. This hybrid approach significantly improves the accuracy and physical plausibility of predicted protein-ligand binding poses. 2. The model incorporates a force-guided network that steers ligand conformations towards low-energy states, ensuring physically realistic docking results. This not only enhances docking accuracy but also reduces computational costs compared to traditional unguided methods. 3. ForceFM demonstrates robust generalization across multiple energy functions, including Vina, Glide, Gnina, and Confscore. This flexibility allows it to adapt to various docking scenarios and outperform existing methods in both blind docking and cross-domain evaluations. 4. Extensive experiments on the PDBBind dataset show that ForceFM achieves superior performance in terms of RMSD and physical validity. The model also shows strong generalization to unseen proteins, making it a versatile tool for drug discovery applications. 5. The incorporation of force guidance significantly accelerates sampling efficiency, reducing the number of required steps to achieve optimal docking results. This advancement addresses a key limitation of current deep learning-based docking methods. 6. Despite its promising results, ForceFM currently focuses on rigid docking and does not fully account for protein flexibility or dynamic conformational changes during binding. Future work aims to address these limitations by incorporating molecular dynamics simulations and expanding training on more diverse datasets. 📜Paper: openreview.net/pdf/ae92f0c9f… #ForceFM #MolecularDocking #DeepLearning #DrugDiscovery #ProteinLigand #AIinBiology

KIMMDY: a biomolecular reaction emulator １．The authors present KIMMDY, a biomolecular reaction emulator that enables large-scale simulations of biochemical reactions in complex, dynamic molecular systems, addressing a major bottleneck in computational biology. ２．Unlike traditional molecular simulations limited to structural dynamics, KIMMDY explicitly models chemical reactivity by combining kinetic Monte Carlo (kMC) with machine learning and graph neural networks (GNNs) to predict reaction rates across conformational ensembles. ３．A key innovation is that KIMMDY leverages ensemble-based reaction rate predictions, capturing how molecular conformations influence reactivity, a feature missing from conventional kMC-MD approaches. ４．The modular framework of KIMMDY allows integration of both machine-learned, physics-based, or heuristic reaction models, making it highly extensible to new reaction types and biomolecular systems. ５．In validation studies, KIMMDY accurately reproduced hydrogen atom transfer (HAT) dynamics in alkyl radicals, correctly predicting reaction probabilities and trends based on experimental data, even though absolute rates were slightly underestimated due to ignored tunneling effects. ６．Applied to collagen fibrils, KIMMDY uncovered new radical transfer pathways under mechanical stress, identifying DOPA and an overlooked PYD moiety as efficient radical scavengers, providing a novel interpretation of experimental EPR spectra. ７．The method further demonstrated its ability to simulate competing reactions, showing that in dense collagen networks, homolytic bond cleavage can outcompete hydrolysis under mechanical load, an insight unattainable by QM/MM or MD alone. ８．KIMMDY was also applied to DNA origami, predicting unexpectedly low quantum yields for photodimerization in common structural motifs, challenging assumptions in DNA nanotechnology and highlighting the need for experimental reassessment. ９．Overall, KIMMDY enables realistic simulations of reactive biomolecular systems on timescales and system sizes previously inaccessible, providing mechanistic insights into processes like radical migration, competing reaction pathways, and photochemical stability. １０．The work highlights how coupling ensemble-aware machine learning models with adaptive kMC expands the scope of molecular simulations beyond structural dynamics, opening avenues for studying complex biochemical reactivity in health, aging, and biomaterials design. 💻Code: github.com/graeter-group/kim… 📜Paper: biorxiv.org/content/10.1101/… #MolecularDynamics #ReactionEmulation #GraphNeuralNetworks #ProteinBiochemistry #ComputationalBiology #DNAOrigami #Mechanochemistry #RadicalChemistry