Journal of Chemical Theory and Computation最新文献

Temperature-Dependent Coarse-Grained Model for Simulations of Intrinsically Disordered Protein LCST and UCST Liquid-Liquid Phase Separations. 本征无序蛋白LCST和UCST液-液相分离的温度依赖粗粒度模型。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-25 DOI: 10.1021/acs.jctc.5c00212

Yingmin Jiang,Tâp Ha-Duong

{"title":"Temperature-Dependent Coarse-Grained Model for Simulations of Intrinsically Disordered Protein LCST and UCST Liquid-Liquid Phase Separations.","authors":"Yingmin Jiang,Tâp Ha-Duong","doi":"10.1021/acs.jctc.5c00212","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00212","url":null,"abstract":"Many intrinsically disordered proteins (IDPs) can undergo a liquid-liquid phase separation (LLPS) in water, depending on solution conditions (temperature, pH, and ionic strength). There are two types of LLPS that are controlled by temperature: those occurring above a lower critical solution temperature (LCST) and those occurring below an upper critical solution temperature (UCST). IDP coarse-grained (CG) models are particularly appropriate for investigating the physical and chemical factors that govern their LLPS and supramolecular organization. However, the development of CG models allowing simulations of both LCST and UCST behavior of temperature-sensitive IDPs is still in its infancy. In this context, we present here a novel temperature-dependent (TD) CG model for IDP simulations based on the MARTINI 3 force field. The model was developed by modifying the Lennard-Jones potentials between apolar or charged solute beads and water with a TD rescaling factor. It was parametrized to fit the TD potentials of mean force (PMF) between two apolar or two charged molecules computed using all-atom (AA) simulations. We show that the TD CG model is able to reproduce the experimentally known LLPS of both LCST and UCST low-complexity sequences and to estimate phase transition temperatures comparable to experimental measurements.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"64 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876472","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}

引用次数: 0

Stability of the Long-Range Corrected Exchange-Correlation Functional and the Proca Procedural Functional in Time-Dependent Density-Functional Theory. 时变密度泛函理论中远距校正交换-相关泛函和进程泛函的稳定性。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-24 DOI: 10.1021/acs.jctc.5c00122

Jared R Williams,Carsten A Ullrich

引用次数: 0

Revisiting Machine Learning Potentials for Silicate Glasses: The Missing Role of Dispersion Interactions. 重新审视硅酸盐玻璃的机器学习潜力：色散相互作用的缺失作用。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-24 DOI: 10.1021/acs.jctc.5c00218

Alfonso Pedone,Marco Bertani,Matilde Benassi

{"title":"Revisiting Machine Learning Potentials for Silicate Glasses: The Missing Role of Dispersion Interactions.","authors":"Alfonso Pedone,Marco Bertani,Matilde Benassi","doi":"10.1021/acs.jctc.5c00218","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00218","url":null,"abstract":"Machine learning interatomic potentials (MLIPs) offer a promising alternative to traditional force fields and ab initio methods for simulating complex materials such as oxide glasses. In this work, we present the first evaluation of the pretrained MACE (Multi-ACE) model [D.P. Kovács et al., J. Chem. Phys. 159(2023), 044118] for silicate glasses, using sodium silicates as a test case. We compare its performance with a DeePMD-based MLIP specifically trained on sodium silicate compositions [M. Bertani et al., J. Chem. Theory Comput. 20(2024), 1358-1370] and assess their accuracy in reproducing structural and dynamical properties. Additionally, we investigate the role of dispersion interactions by incorporating the D3(BJ) correction in both models. Our results show that while MACE accurately reproduces neutron structure factors, pair distribution functions, and Si[Qn] speciation, it performs slightly worst for elastic properties calculations. However, it is suitable for the simulations of sodium silicate glasses. The inclusion of dispersion interactions significantly improves the reproduction of density and elastic properties for both MLIPs, highlighting their critical role in glass modeling. These findings provide insight into the transferability of general MLIPs to disordered systems and emphasize the need for dispersion-aware training data sets in developing accurate force fields for oxide glasses.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"35 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143872106","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}

引用次数: 0

ANI-1xBB: An ANI-Based Reactive Potential for Small Organic Molecules. ANI-1xBB：一种基于ani的有机小分子反应电位。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-24 DOI: 10.1021/acs.jctc.5c00347

Shuhao Zhang,Roman Zubatyuk,Yinuo Yang,Adrian Roitberg,Olexandr Isayev

{"title":"ANI-1xBB: An ANI-Based Reactive Potential for Small Organic Molecules.","authors":"Shuhao Zhang,Roman Zubatyuk,Yinuo Yang,Adrian Roitberg,Olexandr Isayev","doi":"10.1021/acs.jctc.5c00347","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00347","url":null,"abstract":"Reactive potentials serve as essential tools for investigating chemical reactions with moderate computational costs. However, traditional reactive potentials often depend on fixed, semiempirical parameters, which limits their accuracy and transferability. Overcoming these limitations can significantly expand the applicability of reactive potentials, enabling the simulation of a broader range of reactions under diverse conditions and the prediction of reaction properties, such as barrier heights. This work introduces ANI-1xBB, a novel ANI-based reactive ML potential trained on off-equilibrium molecular conformers generated through an automated bond-breaking workflow. ANI-1xBB significantly enhances the prediction of reaction energetics, barrier heights, and bond dissociation energies, surpassing those of conventional ANI models. Our results show that ANI-1xBB improves transition state modeling and reaction pathway prediction while generalizing effectively to pericyclic reactions and radical-driven processes. Furthermore, the automated data generation strategy supports the efficient construction of large-scale, high-quality reactive data sets, reducing reliance on expensive QM calculations. This work highlights ANI-1xBB as a practical model for accelerating the development of reactive machine learning potentials, offering new opportunities for modeling reaction phenomena.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"7 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143872023","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}

引用次数: 0

Modeling Enzyme Reaction and Mutation by Direct Machine Learning/Molecular Mechanics Simulations. 通过直接机器学习/分子力学模拟模拟酶反应和突变。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-24 DOI: 10.1021/acs.jctc.5c00149

Xinhu Sha,Zhuo Chen,Daiqian Xie,Yanzi Zhou

{"title":"Modeling Enzyme Reaction and Mutation by Direct Machine Learning/Molecular Mechanics Simulations.","authors":"Xinhu Sha,Zhuo Chen,Daiqian Xie,Yanzi Zhou","doi":"10.1021/acs.jctc.5c00149","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00149","url":null,"abstract":"Accurately modeling enzyme reactions through direct machine learning/molecular mechanics simulations remains challenging in describing the electrostatic coupling between the QM and MM subsystems. In this work, we proposed a reweighting ME (mechanic embedding) REANN (recursively embedded atom neural network) method that trains the potential and point charges of the QM subsystem in vacuo. The charge equilibration approach has been encoded into REANN to ensure conservation of the total charge of the QM subsystem. Electrostatic coupling is measured by point charges, and the polarization of the MM subsystem on the coupling can be corrected by thermodynamic perturbation after molecular dynamics simulations. We first constructed the REANN surfaces of potential energy and charges for the acylation of cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2) by aspirin. These surfaces allowed us to reproduce the free energy curves of B3LYP/MM-MD with a chemical accuracy. Subsequently, they were successfully applied to R513A of COX-2, reproducing the free energy barrier simulated by B3LYP/MM MD with a difference of less than 0.5 kcal mol-1 and a speedup of 80-fold, revealing our method can predict the activity of mutants accurately and rapidly. This method is expected to be applied in virtual screening in the future.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"136 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876474","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}

引用次数: 0

Allosteric Regulation of Enzymatic Catalysis through Mechanical Force. 机械力作用下酶催化的变构调节。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-22 DOI: 10.1021/acs.jctc.5c00250

Zeyu Zhang,Yangyang Zhang,Weitong Ren,Weiwei Zhang,Wenfei Li,Wei Wang

{"title":"Allosteric Regulation of Enzymatic Catalysis through Mechanical Force.","authors":"Zeyu Zhang,Yangyang Zhang,Weitong Ren,Weiwei Zhang,Wenfei Li,Wei Wang","doi":"10.1021/acs.jctc.5c00250","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00250","url":null,"abstract":"Mechanical force has been increasingly recognized to play crucial roles in regulating various cellular processes, which has inspired wide interest in elucidating the biophysical mechanism underlying these mechanobiological processes. In this work, we investigate the mechanical regulation of enzyme catalysis by developing a residue-resolved computational model capable of describing the full catalytic cycle of enzymes under mechanical force. Intriguingly, for a model enzyme, adenylate kinase, we showed that applying tensile forces with biologically relevant strength can increase the enzymatic activity. Further analysis showed that mechanical tensile force allosterically modifies the global free energy landscape and conformational dynamics of the protein, which then promotes the rate-limiting product release step of the enzymatic cycle. The effect of mechanical allostery on enzyme catalysis depends on the intrinsic conformational propensity of the enzymes. The crucial role of mechanical allostery in enzymatic catalysis elucidated in this work sheds important insights into the biophysical principle of enzymatic regulation and suggests a possible strategy for fine-tuning the functioning dynamics of biological enzymes.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"128 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866261","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}

引用次数: 0

Spatial and Sequential Topological Analysis of Molecular Dynamics Simulations of IgG1 Fc Domains. IgG1 Fc结构域分子动力学模拟的空间和顺序拓扑分析。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-22 DOI: 10.1021/acs.jctc.5c00161

Melinda Kleczynski,Christina Bergonzo,Anthony J Kearsley

引用次数: 0

Unitary Block-Correlated Coupled Cluster Ansatz Based on the Generalized Valence Bond Wave Function for Quantum Simulation. 基于广义价键波函数的量子模拟酉块相关耦合簇Ansatz。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-22 DOI: 10.1021/acs.jctc.5c00239

Jiaqi Hu,Qingchun Wang,Shuhua Li

{"title":"Unitary Block-Correlated Coupled Cluster Ansatz Based on the Generalized Valence Bond Wave Function for Quantum Simulation.","authors":"Jiaqi Hu,Qingchun Wang,Shuhua Li","doi":"10.1021/acs.jctc.5c00239","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00239","url":null,"abstract":"Strongly correlated (SC) systems present significant challenges for classical quantum chemistry methods. Quantum computing, particularly the variational quantum eigensolver (VQE), offers a promising framework to address these challenges by inherently supporting exponentially large configuration spaces. However, its application to SC systems remains limited due to the single-reference nature of the widely used ansatzes such as unitary coupled cluster (UCC). To address this challenge, we propose the generalized valence bond-based unitary block correlated coupled cluster (GVB-UBCCC) method. This novel ansatz incorporates the multiconfigurational nature of generalized valence bond (GVB) and the accuracy of block correlated coupled cluster (BCCC) methods, making it well-suited for SC systems. We have implemented the GVB-UBCCC method with up to two-block correlation (GVB-UBCCC2) and applied it to investigate ground-state energies for several SC systems, including H4, the water dimer, N2H2, and S6, at most described by 24 qubits. Our approach demonstrates that for these systems, GVB-UBCCC2 can achieve more accurate ground-state energies than UCCSD in most cases while requiring only O(N2) quantum gates and parameters, as opposed to the O(N4) scaling of UCCSD. The results highlight the effectiveness and potential advantages of GVB-UBCCC in SC systems.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866195","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}

引用次数: 0

Ensemble Adaptive Sampling Scheme: Identifying an Optimal Sampling Strategy via Policy Ranking. 集成自适应抽样方案：通过策略排序确定最优抽样策略。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-22 DOI: 10.1021/acs.jctc.4c01488

Hassan Nadeem,Diwakar Shukla

{"title":"Ensemble Adaptive Sampling Scheme: Identifying an Optimal Sampling Strategy via Policy Ranking.","authors":"Hassan Nadeem,Diwakar Shukla","doi":"10.1021/acs.jctc.4c01488","DOIUrl":"https://doi.org/10.1021/acs.jctc.4c01488","url":null,"abstract":"Efficient sampling in biomolecular simulations is critical for accurately capturing the complex dynamic behaviors of biological systems. Adaptive sampling techniques aim to improve efficiency by focusing computational resources on the most relevant regions of the phase space. In this work, we present a framework for identifying the optimal sampling policy through metric-driven ranking. Our approach systematically evaluates the policy ensemble and ranks the policies based on their ability to explore the conformational space effectively. Through a series of biomolecular simulation case studies, we demonstrate that the choice of a different adaptive sampling policy at each round significantly outperforms single policy sampling, leading to faster convergence and improved sampling performance. This approach takes an ensemble of adaptive sampling policies and identifies the optimal policy for the next round based on current data. Beyond presenting this ensemble view of adaptive sampling, we also propose two sampling algorithms that approximate this ranking framework on the fly. The modularity of this framework allows incorporation of any adaptive sampling policy, making it versatile and suitable as a comprehensive adaptive sampling scheme.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"5 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866104","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}

引用次数: 0

Extraction of Double Photoionization Amplitudes from Full-Scattered Wave Functions. 从全散射波函数中提取双光离振幅。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-20 DOI: 10.1021/acs.jctc.5c00197

Alexander A Sadamune,Robert R Lucchese,C William McCurdy,Frank L Yip

引用次数: 0