Journal of Chemical Theory and Computation最新文献

{"title":"Beyond Deshielding: NMR Evidence of Shielding in Hydridic and Protonic Hydrogen Bonds.","authors":"Debashree Manna,Rabindranath Lo,Maximilián Lamanec,Jana Pavlišová,Ondřej Socha,Martin Dračínský,Pavel Hobza","doi":"10.1021/acs.jctc.5c00870","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00870","url":null,"abstract":"The red shift of the X-H stretching frequency, with a significant increase in intensity of the corresponding spectral band and a downfield chemical shift of hydrogen (deshielding) in nuclear magnetic resonance (NMR) spectroscopy, has traditionally been used as a criterion for identifying X-H···Y hydrogen bonds (HBs) where X is the hydrogen donor and Y is the acceptor. However, over the past two decades, it has become evident that certain HBs can exhibit a blue shift in the X-H stretching frequency and may also show a decrease in IR intensity, diverging from classical expectations. In this study, we investigate a wide array of HBs, encompassing both red-shifted and blue-shifted systems, as well as protonic and hydridic HB systems. We focus on understanding the underlying electronic conditions behind the reverse chemical shift effects─upfield shifts (shielding) upon HB formation, challenging the view that hydrogen bonding (H-bonding) typically leads to deshielding. We employ state-of-the-art quantum chemical methods, integrating computed NMR shielding tensors and electron deformation density, in combination with experimental NMR, to probe that phenomenon. The computational findings are thoroughly validated against experimental results. Our research confirms that shielding is also possible upon HB formation, thereby broadening the conceptual framework of H-bonding.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"55 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preparing Superposition States to Modify the Spectra and to Achieve Complete Selectivity in Photodissociation Reactions.","authors":"Ignacio R Sola,Alberto García-Vela","doi":"10.1021/acs.jctc.5c00655","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00655","url":null,"abstract":"We derive and apply the geometric optimization methodology to modify the photodissociation spectra of CH3I in the A band. For this purpose, we prepare optimized initial wave functions that maximally exploit interference-induced coherent control to drive a reaction mediated by nonadiabatic couplings in a polyatomic molecule essentially from the beginning. By designing functionals that maximize the output of the products, or that imply competition between the products, or discrimination of one of them, we test the performance of the methods and the effect of preparing initial vibrational coherences among CH3-I stretching vibrational states, CH3 vibrational states, or both. Our results show that using weak ultrashort pulses, one can easily increase the efficiency of the reaction toward any of the products by 100-200% using vibrational states related to the reaction coordinate; that one can increase the efficiency by more than 100% and at the same time almost completely quench the output of products in the other channels. Finally, if one demands high selectivity in the reaction, we show that it is possible to suppress even the most dominant channel to less than one part in a million by preparing superpositions of all available vibrational states optimized with the proper functional.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"13 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144737374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Binding Affinity Calculations: A Non-Equilibrium Simulations Approach for Calculation of Relative Binding Free Energies in Systems with Trapped Waters.","authors":"Swapnil Wagle, Christopher I Bayly, David L Mobley","doi":"10.1021/acs.jctc.5c00758","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00758","url":null,"abstract":"<p><p>The formation of protein-ligand complexes involves displacement of water molecules that were previously occupying the protein's binding site. In some cases, however, some water molecules may not be displaced by the ligand's binding, and they can stabilize the complex by mediating the interactions between the ligand and the protein. A relative binding free energy (RBFE) calculation between two ligands, one of which binds to the protein with an intermediate water while the other displaces the water, can yield wrong results if the water fails to rearrange itself within the simulation timescale. Enhanced sampling methods have previously been used to address the sampling of such \"trapped\" waters, inserting or deleting waters in the protein's binding site during ligand transformation. While sometimes effective, the enhanced sampling methods typically require long simulation times to converge and may lead to differences in RBFE estimates (i.e., hysteresis) based on initial water placement. In this study, we present a non-equilibrium switching (NES) method to calculate RBFEs in systems with trapped waters. Our approach requires the knowledge of the positions of the trapped waters prior to performing the free energy calculation for ligand transformation and then uses this information to efficiently calculate the RBFE between the ligands. In our simulation protocol, we perform ligand transformation in the binding site of the target protein by using three consecutive NES switches. The three NES switches implement restraints, transform the ligand, and then remove the restraints. We demonstrate that our NES simulation-based method results in RBFE estimates within 1.1 kcal mol^-1 of experimental RBFEs, with associated statistical errors under 0.4 kcal mol^-1, for eight systems involving trapped water displacement. Our method provides a computationally inexpensive alternative for estimating RBFEs for systems involving trapped waters by leveraging distributed computational resources.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mutual Correlation.","authors":"Francesco A Evangelista","doi":"10.1021/acs.jctc.5c00766","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00766","url":null,"abstract":"<p><p>Quantifying correlation and complexity in quantum many-body states is central to advancing theoretical and computational chemistry, physics, and quantum information science. This work introduces a novel framework, <i>mutual correlation</i>, based on the Frobenius norm squared of the two-body reduced density matrix cumulant. Through systematic partitioning of the cumulant norm, mutual correlation quantifies nonadditive correlations among interacting subsystems. To assess the ability of mutual correlation to identify orbital interactions, we performed benchmark studies on model systems, including H₁₀, N₂, and <i>p</i>-benzyne, and performed a formal and numerical comparison with orbital mutual information. Maximally correlated orbitals, obtained by maximizing a nonlinear cost function of the mutual correlation, are also considered to identify a basis-independent partitioning of correlation. This study suggests that mutual correlation is a broadly applicable metric, useful in active space selection and the interpretation of electronic states.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performing Path Integral Molecular Dynamics Using an Artificial Intelligence-Enhanced Molecular Simulation Framework.","authors":"Cheng Fan, Maodong Li, Sihao Yuan, Zhaoxin Xie, Dechin Chen, Yi Isaac Yang, Yi Qin Gao","doi":"10.1021/acs.jctc.5c00666","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00666","url":null,"abstract":"<p><p>This study employed an artificial intelligence-enhanced molecular simulation framework to enable efficient path integral molecular dynamics (PIMD) simulations. Owing to its modular architecture and high-throughput capabilities, the framework effectively mitigates the computational complexity and resource-intensive limitations associated with conventional PIMD approaches. By integrating machine learning force fields (MLFFs) into the framework, we rigorously tested its performance through two representative cases: a small-molecule reaction system (double-proton transfer in the formic acid dimer) and a bulk-phase transition system (water-ice phase transformation). Computational results demonstrate that the proposed framework achieves accelerated PIMD simulations while preserving the quantum mechanical accuracy. These findings show that nuclear quantum effects can be captured for complex molecular systems using relatively low computational cost.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Quantum Embedding by Machine Learning for Large Molecular Assemblies.","authors":"Moritz Bensberg, Marco Eckhoff, Raphael T Husistein, Matthew S Teynor, Valentina Sora, William Bro-Jørgensen, F Emil Thomasen, Anders Krogh, Kresten Lindorff-Larsen, Gemma C Solomon, Thomas Weymuth, Markus Reiher","doi":"10.1021/acs.jctc.5c00389","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00389","url":null,"abstract":"<p><p>We present a quantum-in-quantum embedding strategy coupled to machine learning potentials to improve on the accuracy of quantum-classical hybrid models for the description of large molecules. In such hybrid models, relevant structural regions (such as those around reaction centers or pockets for binding of host molecules) can be described by a quantum model that is then embedded into a classical molecular-mechanics environment. However, this quantum region may become so large that only approximate electronic structure models are applicable. To then restore accuracy in the quantum description, we here introduce the concept of quantum cores within the quantum region that are amenable to accurate electronic structure models due to their limited size. Huzinaga-type projection-based embedding, for example, can deliver accurate electronic energies obtained with advanced electronic structure methods. The resulting total electronic energies are then fed into a transfer learning approach that efficiently exploits the higher-accuracy data to improve on a machine learning potential obtained for the original quantum-classical hybrid approach. We explore the potential of this approach in the context of a well-studied protein-ligand complex for which we calculate the free energy of binding using alchemical free energy and nonequilibrium switching simulations.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cmem Builder: An Automated Tool for Curved Membrane Construction in Molecular Dynamics Simulations.","authors":"Jiabin Guo,Kin Lei,Jixing Liu,Henry Hy Tong,Yun Lyna Luo,Wei Han,Shu Li","doi":"10.1021/acs.jctc.5c00467","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00467","url":null,"abstract":"Membrane curvature is a fundamental property of biological membranes, driving essential processes such as endocytosis, vesicle formation, and mechanotransduction. Molecular dynamics (MD) simulations have become a powerful approach for studying curved membrane systems, providing atomistic insights into curvature-driven phenomena and protein-membrane interactions. However, online platforms like CHARMM-GUI and CGMD focus on constructing flat bilayers or vesicles and lack support for generating curved membranes with defined geometries. Local tools, while more flexible, often do not incorporate protein-specific curvature features, such as those from the Orientations of Proteins in Membranes (OPM) database, which are critical for accurately modeling protein-lipid interactions in curved environments. To address these limitations, we developed Cmem Builder, a novel and user-friendly web server for automating the generation of curved lipid membranes and membrane-protein complexes for coarse-grained (CG) MD simulations using the MARTINI force field. Cmem Builder specializes in generating Z-axis symmetric curved membrane shapes, supports curvature profiles derived from OPM database or custom geometries, allows extensive control over lipid composition, and ensures lipid placement through geometric sampling. The tool has been successfully applied to classical curved membrane systems, including Piezo1 and BAR proteins, as well as plasma membranes with asymmetric lipid compositions, demonstrating its accuracy and efficiency. In total, Cmem Builder provides a robust and accessible platform for exploring the complex dynamics of curved membrane systems. The tool is freely available at https://cmembuilder.com.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"26 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FLAMES─Fast, Low-Storage, Accurate, and Memory-Efficient Adaptive Sampling─Approach to Resolve Spatially Dependent Dynamics of Molecular Liquids.","authors":"Guang Chen,Suresh Narayanan,Gregory Brian Stephenson,Michael J Servis,Subramanian K R S Sankaranarayanan","doi":"10.1021/acs.jctc.5c00553","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00553","url":null,"abstract":"Many critical phenomena in soft matter occur at large length scales, necessitating the resolution of their structure and dynamics at low wavenumbers. However, resolving wavenumber-dependent dynamics computationally via molecular dynamics simulations presents significant challenges, as these phenomena span several orders of magnitude in both time and length scales, resulting in high computational costs and memory demands. This work highlights the computational and memory challenges associated with analyzing molecular trajectories in reciprocal space and demonstrates a method to address them. We introduce FLAMES─Fast, Low-storage, Accurate, and Memory-Efficient adaptive Sampling, which is a direct method for calculation of structure factors, allowing us to select only the required number of wavevectors for binning. We also use wavenumber-dependent time steps to extract dynamics. Our FLAMES approach effectively mitigates computational and memory/storage bottlenecks. We demonstrate the method using simulations of a model system, liquid octane, at various temperatures. Comparisons with experimental data and real space computation show that the FLAMES technique achieves high accuracy in resolving temperature- and spatially dependent dynamics while being significantly more computationally efficient and requiring less memory and storage than methods based on a uniform wavevector grid and fixed temporal spacing.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"26 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Sampling and Tailored Collective Variables Yield Reproducible Free Energy Landscapes of Beta-1 Adrenergic Receptor Activation.","authors":"Simone Aureli,Valerio Rizzi,Nicola Piasentin,Francesco Luigi Gervasio","doi":"10.1021/acs.jctc.5c00600","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00600","url":null,"abstract":"The beta-1 adrenergic receptor (ADRB1) is a critical target for cardiovascular drugs, yet our understanding of how it is activated remains incomplete. Capturing the concerted interplay of agonists, solvent, ions, and protein microswitches is a significant challenge for conventional simulation methods and is essential for unraveling this process. Here, we address this challenge by implementing a powerful enhanced sampling framework that integrates the OneOPES enhanced sampling algorithm with a set of biologically motivated collective variables (CVs). These CVs are designed to track several key features of the activation process simultaneously, including rearrangement of conserved microswitches, the state of the sodium ion binding pocket, and dynamics of critical water molecules. Using this framework, we mapped the multidimensional free energy landscapes of the ADRB1 receptor in both its apo- and adrenaline-bound holo states. Our analysis reveals a detailed, stepwise activation pathway that quantifies the known modulatory roles of sodium ions and protonation states and identifies essential water-mediated networks that stabilize the active conformation. This work provides a detailed overview of ADRB1 activation and establishes the robustness of our OneOPES approach for investigating complex activation mechanisms with the potential for application to other Class A GPCRs.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"710 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules.","authors":"Paul Katzberger,Felix Pultar,Sereina Riniker","doi":"10.1021/acs.jctc.5c00728","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00728","url":null,"abstract":"The conformational ensemble of a molecule is strongly influenced by the surrounding environment. Correctly modeling the effect of any given environment is, hence, of pivotal importance in computational studies. Machine learning (ML) has been shown to be able to model these interactions probabilistically, with successful applications demonstrated for classical molecular dynamics. While first instances of ML implicit solvents for quantum-mechanical (QM) calculations exist, the high computational cost of QM reference calculations hinders the development of a generally applicable ML implicit solvent model for QM calculations. Here, we present a novel way of developing such a general machine-learned QM implicit solvent model by transferring knowledge obtained from classical interactions to QM, emulating a QM/MM setup with electrostatic embedding and a nonpolarizable MM solvent. This has the profound advantages that neither QM/MM reference calculations nor experimental data are required for training and that the obtained graph neural network (GNN)-based implicit solvent model (termed QM-GNNIS) is compatible with any functional and basis set. QM-GNNIS is currently applicable to small organic molecules and describes 39 different organic solvents. The performance of QM-GNNIS is validated on NMR and IR experiments, demonstrating that the approach can reproduce experimentally observed trends unattainable by state-of-the-art implicit-solvent models paired with static QM calculations.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"130 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}