Andreas Walter Goetz

Nucleic acid enzymes (NAzymes), including ribozymes, DNAzymes, and XNAzymes, are programmable biocatalysts that catalyze diverse chemical reactions and provide unique platforms for investigating catalysis and engineering molecular function. Their central roles in biology and evolution, together with emerging applications in biotechnology, synthetic biology, and medicine, have created an urgent need for predictive computational design frameworks. Although multiscale computational enzymology integrating ab initio quantum mechanics can, in principle, provide predictive insight into these systems, current approaches remain computationally prohibitive for large, complex NAzymes and high-throughput design workflows. Machine learning potentials have the potential to enhance the accuracy and efficiency of multiscale modeling approaches, but QM/MM contexts in which the MM environment is highly polarizing remain challenging. Nucleic acid enzymes present a particularly demanding case: experimentally relevant predictions require an accuracy that semiempirical QM/MM methods alone cannot reliably deliver in their often large, highly charged, metal-dependent active sites. Building on the range-corrected QM/MM Δ-machine learning potential (ΔMLP) framework, we develop ΔNAzyme, a foundational ΔMLP for nucleic acid enzyme reactions. We generate a training set of QM/MM simulations spanning diverse nucleic acid enzyme classes, with energies and forces computed at both the reference (DFTB3) and target (PBE0/6–31G*) levels of theory, yielding a correction on top of DFTB3 that recovers PBE0 quality results. We train models using both the Deep Potential and MACE architectures. The MACE model exhibits enhanced transferability, which we attribute to the equivariant, many-body message-passing framework. Free energy profiles from case-study reactions validate ΔNAzyme against target-level results and show that the model supports efficient fine-tuning to new nucleic acid enzyme systems with minimal additional data.

The self-splicing group I intron ribozyme from Tetrahymena thermophillia (THr) is a prototypical example of how RNA overcomes its limited chemical repertoire to form complex, negatively charged junctions and catalyze chemical reactions. Despite over 30 years of bio-chemical and structural characterization, questions still remain regarding the requirements for THr catalysis that may inform structure based drug design against group I introns, as well as the use of THr as an RNA editing tool. Recent cryo-EM structures have provided an unprecedented window into the first and second steps of splicing, enabling detailed mechanistic investigation at the atomistic level. In this work, we leverage solvation theory, molecular dynamics, alchemical free energy simulations, and machine learning accelerated QM/MM to probe outstanding questions related to 1) the role of a possible third Mg ²⁺ binding in the active site, 2) differences in catalytic requirements between G _exo cofactors and between the first and second steps of splicing, 3) the potential role of an acid or base, and 4) restrictions on the identity of the 5 ^′ exon splice site nucleotide, U(−1). We propose that a transient, cofactor dependent, interaction with a third metal participates in the first step, while a higher affinity interaction with this third ion is required in the second step. We also propose that the presence of a phosphate on the G _exo in the first step, and analogously the G _ω in the second step, forms a metal ion binding site not present when guanosine is bound. Finally, we propose and computationally test design modifications of G22 (isofunctional substitutions of 2,6-diaminopurine that tune the p K _a at the N1 position) that we predict will enable the accommodation of C(–1) in a crucial wobble-like pair at physiological pH. This design would provide a potential strategy for expanding the sequence space accessible to programmable trans-splicing ribozymes in RNA editing applications. TOC Graphic:

General force fields such as General Amber Force Field (GAFF) have been designed for broad applicability and are widely used in protein-ligand binding simulations in structure-based drug discovery. However, the force field parameters are not always transferable across ligand molecules, and custom reparameterization is sometimes necessary for accurate binding free energy simulations. This is especially true for torsion parameters, which are highly dependent on stereoelectronic and steric effects. Here, we report a novel, flexible, and user-friendly computational tool called the Automated Force Field Developer and Optimizer (AFFDO) platform that allows generating accurate, tailored GAFF2 torsion parameters for drug-like molecules. For a given ligand, AFFDO selects the most important torsions, carries out GPU-accelerated density functional theory calculations to collect reference data and fits torsion terms using a fast gradient-based optimizer that leverages automated differentiation. We benchmark AFFDO by parametrizing a series of drug-like molecules and carrying out protein-ligand relative binding free energy (RBFE) simulations. The results show that AFFDO can significantly improve GAFF2 torsion parameters against QM reference data, which in some cases translates into better agreement with experimental RBFE values within a reasonable computational time.

We describe the implementation details of highly efficient ab initio electrostatic potential (ESP) calculations on graphics processing units (GPUs) and introduce a novel scheme for partial charges that are robust against molecular orientation. Performance analyses are discussed, and we highlight that in our new implementation, a single data center GPU can outperform 128 corresponding data center CPU cores in time to solution. This implementation in the open-source Quantum Interaction Computational Kernel code (QUICK) enables ESP computations on highly dense grids that surpass what is reported in the literature, on the order of N_grid points ∼ 20000 points/atom. We demonstrate that, in this dense-grid limit, ESP charges become independent of molecular orientation. We denote such ESP charges as being robust against molecular orientation and validate this desirable attribute against standard charge schemes. Our proposed charge scheme, called reweighted RESP (rwRESP), is designed to significantly overcome the sensitivity to N_grid points that limits the reliability of canonical RESP charges. By effectively amending this N_grid points-sensitivity, we demonstrate that rwRESP charges also achieve robustness against molecular orientation. Ultradense-grid ESP computations and rwRESP fits can be readily performed via the seamless integration of QUICK with AmberTools, enabling highly efficient and reliable parametrization of the general AMBER force field (GAFF) for nonstandard residues. In this spirit, we believe that our fully fledged GPU protocol for obtaining robust molecular charges will facilitate a wide range of applications, such as high-throughput parametrization of molecular interaction potentials, while also serving as a foundational step toward GPU-accelerated on-the-fly polarizable QM/MM simulations with QUICK.

We describe the implementation details of highly efficient ab initio electrostatic potential (ESP) calculations on graphics processing units (GPUs), and introduce a novel scheme for partial charges that are robust against molecular orientation. Performance analyses are discussed, and we highlight that in our new implementation, a single data center GPU can outperform 128 corresponding data center CPU cores in time to solution. This implementation in the open-source Quantum Interaction Computational Kernel code (QUICK) enables ESP computations on highly dense grids that surpass what is reported in the literature, on the order of N(grid points) ~ 20000 points/atom. We demonstrate that, in this dense-grid limit, ESP charges become independent of molecular orientation. We denote such ESP charges as being robust against molecular orientation and validate this desirable attribute against standard charge schemes. Our proposed charge scheme, called reweighted RESP (rwRESP), is designed to significantly overcome the sensitivity to N(grid points) that limits the reliability of canonical RESP charges. By effectively amending this N(grid points)-sensitivity, we demonstrate that rwRESP charges also achieve robustness against molecular orientation. Ultra-dense-grid ESP compu- tations and rwRESP fits can be readily performed via the seamless integration of QUICK with Ambertools, enabling highly efficient and reliable parameterization of the general AMBER force field (GAFF) for nonstandard residues. In this spirit, we believe that our fully-fledged GPU protocol for obtaining robust molecular charges will facilitate a wide range of applications, such as high-throughput parameterization of molecular interaction potentials, while also serving as a foundational step toward GPU-accelerated on-the-fly polarizable QM/MM simulations with QUICK.

Amber is a molecular dynamics (MD) software package first conceived by Peter Kollman, his lab and collaborators to simulate biomolecular systems. The pmemd module is available as a serial version for central processing units (CPUs), NVIDIA and Advanced Micro Devices (AMD) graphics processing unit (GPU) versions as well as Message Passing Interface (MPI) parallel versions. Advanced capabilities include thermodynamic integration, replica exchange MD and accelerated MD methods. A brief update to the software and recently added capabilities is described in this Application Note.

The development of universal machine learning potentials (MLP) for small organic and drug-like molecules requires large, accurate datasets that span diverse chemical spaces. In this study, we introduce the QDπ dataset which incorporates data taken from several datasets. We use a query—by—committee active learning strategy to extract data from large datasets to maximize the diversity and avoid redundancy as relevant for neural network training to construct the QDπ dataset. The QDπ dataset requires only 1.6 million structures to express the chemical diversity of 13 elements from the various source datasets at the ωB97M-D3(BJ)/def2-TZVPPD level of theory. The QDπ dataset enables creation of flexible target loss functions for neural network training relevant to drug discovery, including information-dense data sets of relative conformational energies and barriers, intermolecular interactions, tautomers and relative protonation energies of drug-like compounds and biomolecular fragments. It is the hope that the high chemical information density and diversity contained in the QDπ dataset will provide a valuable resource for the development of new universal MLPs for drug discovery.

Generic force fields such as Generalized Amber Force Field (GAFF) are widely used in protein-ligand binding simulations in structure-based drug discovery. However, the force field parameters are not always transferable across ligand molecules, and reparameterization is necessary for accurate binding free energy simulations. This is especially true for torsion parameters which are highly dependent on stereoelectronic and steric effects. Here we report a novel, flexible, and user-friendly computational tool called the Automated Force Field Developer and Optimizer (AFFDO) platform that allows generating accurate GAFF2 torsion parameters for drug-like molecules. For a given ligand, AFFDO selects the most important torsions, carries out GPU-accelerated density functional theory calculations to collect reference data and fits torsion terms using a fast gradient-based optimizer that leverages automated differentiation. We benchmark AFFDO by parameterizing a series of drug-like molecules and carrying out protein-ligand relative binding free energy (RBFE) simulations. The results show that our tool is capable of significantly improving GAFF2 torsion parameters and RBFE values within a reasonable amount of time.

Atmospheric aerosols facilitate reactions between ambient gases and dissolved species. Here, we review our efforts to interrogate the uptake of these gases and the mechanisms of their reactions both theoretically and experimentally. We highlight the fascinating behavior of N₂O₅ in solutions ranging from pure water to complex mixtures, chosen because its aerosol-mediated reactions significantly impact global ozone, hydroxyl, and methane concentrations. As a hydrophobic, weakly soluble, and highly reactive species, N₂O₅ is a sensitive probe of the chemical and physical properties of aerosol interfaces. We employ contemporary theory to disentangle the fate of N₂O₅ as it approaches pure and salty water, starting with adsorption and ending with hydrolysis to HNO₃, chlorination to ClNO₂, or evaporation. Flow reactor and gas-liquid scattering experiments probe even greater complexity as added ions, organic molecules, and surfactants alter the interfacial composition and reaction rates. Together, we reveal a new perspective on multiphase chemistry in the atmosphere.

We report the development and testing of new integrated cyberinfrastructure for performing free energy simulations with generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and machine learning potentials (MLPs) in Amber. The Sander molecular dynamics program has been extended to leverage fast, density-functional tight-binding models implemented in the DFTB+ and xTB packages, and an interface to the DeePMD-kit software enables the use of MLPs. The software is integrated through application program interfaces that circumvent the need to perform “system calls” and enable the incorporation of long-range Ewald electrostatics into the external software’s self-consistent field procedure. The infrastructure provides access to QM/MM models that may serve as the foundation for QM/MM–ΔMLP potentials, which supplement the semiempirical QM/MM model with a MLP correction trained to reproduce ab initio QM/MM energies and forces. Efficient optimization of minimum free energy pathways is enabled through a new surface-accelerated finite-temperature string method implemented in the FE-ToolKit package. Furthermore, we interfaced Sander with the i-PI software by implementing the socket communication protocol used in the i-PI client–server model. The new interface with i-PI allows for the treatment of nuclear quantum effects with semiempirical QM/MM–ΔMLP models. The modular interoperable software is demonstrated on proton transfer reactions in guanine-thymine mispairs in a B-form deoxyribonucleic acid helix. The current work represents a considerable advance in the development of modular software for performing free energy simulations of chemical reactions that are important in a wide range of applications.