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.

Protein engineering of cutinases is a promising strategy for the biocatalytic degradation of non-natural polyesters. We report a mechanistic study addressing the hydrolysis of the aliphatic polyester poly(butylene succinate, or PBS) by the fungal Apergillus oryzae cutinase enzyme. Through atomistic molecular dynamics simulations and advanced alchemical transformations, we reveal how three units of a model PBS substrate fit the active site cleft of the enzyme, interacting with hydrophobic side chains. The substrate ester moiety approaches the Asp-His-Ser catalytic triad, displaying catalytically competent conformations. Acylation and deacylation hydrolytic reactions were modeled according to a canonical esterase mechanism using umbrella sampling simulations at the quantum mechanical/molecular mechanical DFT(B3LYP)/6-31G**/AMBERff level. The free energy profiles of both steps show a high-energy tetrahedral intermediate resulting from the nucleophilic attack on the ester's carboxylic carbon. The free energy barrier of the acylation step is higher (20.2 ± 0.6 kcal mol-1) than that of the deacylation step (13.6 ± 0.6 kcal mol-1). This is likely due to the interaction of the ester's carboxylic oxygen with the oxyanion hole in the reactive conformation of the deacylation step. In contrast, these interactions form as the reaction proceeds during the acylation step. The formation of an additional hydrogen bond interaction with the side chain of Ser48 is crucial to stabilizing the developing charge at the carboxylic oxygen, thus lowering the activation free energy barrier. These mechanistic insights will inform the design of enzyme variants with improved activity for plastic degradation.

We present the first quantum-centric simulations of noncovalent interactions using a supramolecular approach. We simulate the potential energy surfaces (PES) of the water and methane dimers, featuring hydrophilic and hydrophobic interactions, respectively, with a sample-based quantum diagonalization (SQD) approach. Our simulations on quantum processors, using 27- and 36-qubit circuits, are in remarkable agreement with classical methods, deviating from complete active space configuration interaction (CASCI) and coupled-cluster singles, doubles, and perturbative triples (CCSD(T)) within 1 kcal/mol in the equilibrium regions of the PES. Finally, we test the capacity limits of the quantum methods for capturing hydrophobic interactions with an experiment on 54 qubits. These results mark significant progress in the application of quantum computing to chemical problems, paving the way for more accurate modeling of noncovalent interactions in complex systems critical to the biological, chemical and pharmaceutical sciences.

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.

A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated structure. In the gas phase, the site of protonation is determined using proton affinity (PA) measurements. Currently, mass spectrometry and ab initio computation methods are widely used to evaluate PA; however, both methods are resource-intensive and time-consuming. Therefore, there is a critical need for efficient methods to estimate PA, enabling the rapid identification of the most favorable protonation site in complex organic molecules with multiple proton binding sites. In this work, we developed a fast and accurate method for PA prediction by using multiple descriptors in combination with machine learning (ML) models. Using a comprehensive set of 186 descriptors, our model demonstrated strong predictive performance, with an R2 of 0.96 and a MAE of 2.47 kcal/mol, comparable to experimental uncertainty. Furthermore, we designed quantum circuits as feature encoders for a classical neural network. To evaluate the effectiveness of this hybrid quantum-classical model, we compared its performance with traditional ML models using a reduced feature set derived from the full set. A correlation analysis showed that the quantum-encoded representations have a stronger positive correlation with the target values than the original features do. As a result, the hybrid model outperformed its classical counterpart and achieved consistent performance comparable to traditional ML models with the same reduced feature set on both a noiseless simulator and real quantum hardware, highlighting the potential of quantum machine learning for accurate and efficient PA predictions.

Metal transporters play crucial roles in the homeostasis and detoxification of beneficial and toxic metals in the human body. Due to experimental limitations in studying some metal transporters, numerous simulation studies have been conducted to understand the mechanisms of metal transport. However, studying the transport of divalent metal ions across the plasma membrane by metal transporters has been challenging with traditional molecular dynamics (MD) simulations. The metal ions often become trapped inside the transporter due to encountering high energy barriers during the transport process. In this study, we combined a recently developed metadynamics setup, known as well-tempered (WT) volume-based MTD, with the 12-6-4 Lennard-Jones (LJ) model representing transition metal-His/Asp/Glu side chain interactions. We used this approach to investigate the mechanism of action of a Zrt-/Irt-like protein (ZIP) transporter and compared the results with simulations using standard 12-6 LJ parameters for the transition metal-His/Asp/Glu side chain interactions. Our results show that the 12-6-4 LJ model for transition metal-His/Asp/Glu side chain interactions samples conformational space more broadly than the standard 12-6 LJ model for the same interactions in MTD simulations, facilitating the sampling of states that are hard to reach with the standard 12-6 model within the same time scale. This is even more remarkable given the fact that the model is dominated by 12-6 LJ interactions for the majority of the system, while the transition metal-His/Asp/Glu side chain interactions are the only interactions using the 12-6-4 LJ model. Hence, a small subset of interactions significantly modifies the states sampled by the entire protein leading to a more frequent observation of the transport of the transition metal ion. Overall, using 12-6-4 LJ to model the transition metal-His/Asp/Glu side chain interactions increases the potential for discovering additional metastable states by enabling metal ions to traverse more freely along the defined transport pathways.

The sample-based quantum diagonalization (SQD) method shows great promise in quantum-centric simulations of ground state energies in molecular systems. Inclusion of solute-solvent interactions in simulations of electronic structure is critical for biochemical and medical applications. However, all of the previous applications of the SQD method were shown for gas-phase simulations of the electronic structure. The present work aims to bridge this gap by introducing the integral equation formalism polarizable continuum model (IEF-PCM) of solvent into the SQD calculations. We perform SQD/cc-pVDZ IEF-PCM simulations of methanol, methylamine, ethanol, and water in aqueous solution using quantum hardware and compare our results to CASCI/cc-pVDZ IEF-PCM simulations. Our simulations on ibm_cleveland, ibm_kyiv, and ibm_marrakesh quantum devices are performed with 27, 30, 41, and 52 qubits demonstrating the scalability of SQD IEF-PCM simulations.

Accurately resolving a three-dimensional structure that corresponds to an experimental mass spectrometry (MS) result is valuable for outcomes such as improved analyte identification, determination of physiochemical properties relating to conformation, analyte impurity testing, and drug chemical integrity analysis. Computational approaches utilizing charge state modeling, conformational sampling, quantum mechanical optimizations, relative energy scoring, and computed ion-neutral collision cross sections (CCS) have historically achieved success at assigning equilibrium structures to ion-mobility MS-derived CCS values. Despite this positive status, there remains a lack of new computational software to achieve higher throughput when modeling large systems. A major adverse impact on computational cost is the general increase in titratable sites with molecular size, which then warrants additional protonation/deprotonation models in order to ensure that the correct charge state is captured. Here, we introduce a user-friendly machine learning program called SEER (State Ensemble Energy Recognition) to accurately and efficiently predict the equilibrium charge states of MS-relevant ions. We report that for all systems within the test set, SEER successfully captured the lowest relative energy minimum charge states within its top two predicted candidates from an overall average number of ∼ seven titratable sites. Furthermore, the density functional theory optimized geometries for SEER assigned charge states produced CCS experimental errors that are within the acceptable threshold (i.e., ≤3% error) set for this work. The benchmark study compared SEER to two well-established charge state prediction software packages CREST and Epik classic and found that SEER is either on par or better at consistently locating the correct charge states for the test set with competitive efficiency. SEER requires no additional user programming and is readily accessible through the Google Colab platform at https://github.com/mitkeng/SEER.

In the Big Data era, a change of paradigm in the use of molecular dynamics is required. Trajectories should be stored under FAIR (findable, accessible, interoperable and reusable) requirements to favor its reuse by the community under an open science paradigm.

Ion mobility mass spectrometry (IM-MS) can assist in the identification of isobaric chemical analytes by exploiting the difference in their gas phase collision cross-section (CCS) property. In glycomics, reliable glycan characterization remains challenging, even with IM-MS, because of closely related isomeric species and the available binding arrangements of substituted monosaccharides, allowing for the formation of complex structures. Here, we present a computational procedure to obtain gas-phase structural information from the experimental IM-MS CCS data of carbohydrates. The workflow proceeds with high throughput charge modeling of glycan seed structures to determine the precise protonation or deprotonation site. The charge models were then screened by using density functional theory (DFT) to produce candidate charge states for conformation generation. An extensive conformational scoring of the glycan ions was performed quantum mechanically at the DFT D3-B3LYP/6-31G+(d,p) level for the negative mode, [M - H]-, and at the D3-B3LYP/6-31G(d,p) level for the positive mode, [M + H]+. For most of our test set, the computed CCS values from the final geometry optimized structures showed good agreement with experiment. We also demonstrated the capability of characterizing configurational and constitutional isomeric species. Altogether, we believe that the method we used in this work can be used to build a reliable theoretical reference database for glycans that can be used for experimental quality control and for assigning molecular structure to experimental IM-MS CCS information.

Computing ground-state properties of molecules is a promising application for quantum computers operating in concert with classical high-performance computing resources. Quantum embedding methods are a family of algorithms particularly suited to these computational platforms: they combine high-level calculations on active regions of a molecule with low-level calculations on the surrounding environment, thereby avoiding expensive high-level full-molecule calculations and allowing the distribution of computational cost across multiple and heterogeneous computing units. Here, we present the first density matrix embedding theory (DMET) simulations performed in combination with a sample-based quantum diagonalization (SQD) method. We employ the DMET-SQD formalism to compute the ground-state energy of a ring of 18 hydrogen atoms and the relative energies of the chair, half-chair, twist-boat, and boat conformers of cyclohexane. The full-molecule 41- and 89-qubit simulations are decomposed into 27- and 32-qubit active-region simulations, which we carry out on the ibm_cleveland device, obtaining results in agreement with reference classical methods. Our DMET-SQD calculations mark tangible progress in the size of active regions that can be accurately tackled by near-term quantum computers and are an early demonstration of the potential for quantum-centric simulations to accurately treat the electronic structure of large molecules, with the ultimate goal of tackling systems such as peptides and proteins.

The ion mobility (IM) technique coupled with traditional mass spectrometry (IM-MS) has introduced a practical tool for the characterization of peptide analyte ions by exploiting the difference in their collision cross section (CCS) values. CCS holds molecular level information that can be used to computationally assign the conformation(s) of a molecular system. However, a reliable and accurate method for peptide structure prediction remains a challenge because peptides exhibit dynamic gas-phase intramolecular interactions, and hence, the methods used will need to account for this. In this work, we systematically assessed the performance of a computational workflow involving both classical and density functional theory (DFT) steps to elucidate the peptide structure. Not unexpectedly, extensive enumeration of available peptide conformations was critical to obtain high-quality results. Due to the size of the systems studied and the large numbers of conformers that needed to be optimized, we initially chose the D3-B3LYP/6-31G(d) level of theory and obtained good agreement between experimental and computed CCS values. However, in several cases, suboptimal accuracies were observed, but we found that increasing the basis set used to 6-31G(d,p) was able to improve our agreement with experiment. Altogether, we demonstrated that accurate peptide structure assignment is achievable with adequate sampling of the conformational space and using the appropriate quantum mechanical level of theory to account for intramolecular interactions in the gas phase.

Metal coordination is ubiquitous in Nature and central in many applications, ranging from nanotechnology to catalysis and environmental chemistry. Complex formation results from the subtle interplay between different thermodynamic, kinetic, and mechanistic contributions, which remain largely elusive to standard experimental methodologies and challenging for typical modeling approaches. Here, considering some prototypical metal complexes between Cd(II) and Ni(II) with various amine ligands, we present a comprehensive atomistic-level description of their chemical equilibrium, complex formation, and ligand exchange dynamics in aqueous solution, providing an excellent agreement with available association constants and formation rates spanning several orders of magnitude. This is achieved through an effective molecular simulation approach that combines finely tuned interatomic potentials with state-of-the-art enhanced sampling and kinetics techniques. Worthy of note, the nature of the chelate effect, a fundamental concept in coordination chemistry, is fully unravelled through the comparative analysis of the ligand binding reactions of monodentate and bidentate ligands in octahedral complexes. Results provide a complete picture illustrating all the concurrent contributions to this phenomenon, such as entropy, dissociation rates, and ligand binding mechanisms, in some cases contradicting previously held beliefs. This study represents a step forward for the in silico design and applications of coordination complex systems.

Computational methods have revolutionized NMR spectroscopy, driving significant advancements in structural biology and related fields. This review focuses on recent developments in quantum chemical and machine learning approaches for computational NMR, emphasizing their role in enhancing accuracy, efficiency, and scalability. QM methods provide precise predictions of NMR parameters, enabling detailed structural characterization of diverse systems. ML techniques, leveraging extensive data sets and advanced algorithms, complement QM by efficiently automating spectral assignments, predicting chemical shifts, and analyzing complex data. Together, these approaches have transformed NMR workflows, addressing challenges in metabolomics, protein structure determination, and drug discovery. This review highlights recent progress, emerging tools, and future directions in computational NMR, underscoring its critical role in modern structural science.

Significant success has been achieved in fabricating angstrom-scale artificial solid ionic channels aiming to replicate the biological ion channels (BICs). Besides high selectivity, BICs also exhibit sophisticated ion gating and interplay. However, such behavior and functionality are seldomly recreated in the artificial counterparts due to the insufficient understanding of the molecular origin. Here we report cooperative and inhibitory ion transport in angstrom-scale acetate functionalized MoS2 two-dimensional channels. For cooperative ion transport, the permeability of K+ is doubled in the presence of only 1% Pb2+ (versus K+ by molarity), while the permeability of Pb2+ is independent of K+. Molecular dynamics simulations reveal complex interplay among K+, Pb2+, and the anions in governing the cooperativity, such that Pb2+ ions capture and slow down the anions via long-range interaction, which leads to the synchronization of anions with K+ to transport as ion pairs with reduced interaction with the channel surface. For inhibitory ion transport, divalent Co2+ (or Ba2+) and Pb2+ can replace each other in the confined channel and compete for the limited transport cross section. Our work reveals ion transport phenomena in extreme confinement and highlights the potential of manipulating ion interplay in confinement for achieving advanced functionalities.

Transition metal complexes are a class of compounds with varied and versatile properties, making them of great technological importance. Their applications cover a wide range of fields, either as metallodrugs in medicine or as materials, catalysts, batteries, solar cells, etc. The demand for the novel design of transition metal complexes with new properties remains of great interest. However, the traditional high-throughput screening approach is inherently expensive and laborious since it depends on human expertise. Here, we present LigandDiff, a generative model for the de novo design of novel transition metal complexes. Unlike the existing methods that simply extract and combine ligands with the metal to get new complexes, LigandDiff aims at designing configurationally novel ligands from scratch, which opens new pathways for the discovery of organometallic complexes. Moreover, it overcomes the limitations of current methods, where the diversity of new complexes highly relies on the diversity of available ligands, while LigandDiff can design numerous novel ligands without human intervention. Our results indicate that LigandDiff designs unique and novel ligands under different contexts, and these generated ligands are synthetically accessible. Moreover, LigandDiff shows good transferability by generating successful ligands for any transition metal complex.