Journal of Chemical Theory and Computation最新文献

{"title":"AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.","authors":"Siqin Cao,Feliks Nüske,Bojun Liu,Micheline B Soley,Xuhui Huang","doi":"10.1021/acs.jctc.5c00076","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00076","url":null,"abstract":"Elucidating collective variables (CVs) for biomolecular dynamics is crucial for understanding numerous biological processes. By leveraging the tensor-train data structure, a multilinear version of the AMUSE (Algorithm for Multiple Unknown Signals) algorithm for Koopman approximation (AMUSEt) was recently developed to identify CVs for biomolecular dynamics. To find slow CVs, AMUSEt transforms input features (e.g., pairwise atomic distances) into nonlinear basis functions (e.g., Gaussian functions) and encodes these nonlinear basis functions within a tensor-train structure via time-lagged correlation functions. Due to the need to fit these tensor-train data structures into computer memory, AMUSEt can handle only a limited number of input features. Consequently, AMUSEt relies on manually selecting and ranking features based on physical intuition to fully capture the slow dynamics. However, when applied to complex biological systems with numerous features, this selection and ranking process becomes increasingly challenging. To address this challenge, here we present AMUSET-TICA (AMUSEt-based Time-lagged Independent Component Analysis), a CV-identification method using time-structure-independent components (tICs) as the input features for AMUSEt. The key insight of AMUSET-TICA lies in its highly effective embedding of high-dimensional atomistic protein conformations, achieved by expanding orthogonal tICs into overlapping Gaussian basis functions through a tensor-product data structure. This eliminates the need for manually selecting and ranking input features for a wide range of biomolecular systems. We demonstrate that AMUSET-TICA consistently and significantly outperforms AMUSEt and tICA in identifying slow CVs for three different biomolecular systems: alanine dipeptide, the N-terminal domain of L9 (NTL9), and the FIP35 WW domain. For all these systems, the CVs generated by AMUSET-TICA accurately describe the slowest dynamical modes underlying these biological conformational changes. Furthermore, we show that AMUSET-TICA achieves performance comparable to deep-learning approaches like VAMPnets in identifying the slowest dynamical modes, while being significantly more computationally efficient in terms of CPU time. In addition, the CVs yielded by AMUSET-TICA provide insights into the folding mechanisms of NTL9 and the FIP35 WW domain, including CV3 and CV4 of the WW domain, which capture its two parallel folding pathways. We expect AMUSET-TICA can be widely applied to facilitate the investigation of biomolecular dynamics.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"91 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857332","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":"Exact Two-Component Relativistic Polarizable Density Embedding.","authors":"Ernst Dennis Larsson,Peter Reinholdt,Jacob Kongsted,Erik Donovan Hedegård","doi":"10.1021/acs.jctc.5c00014","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00014","url":null,"abstract":"We have implemented the fragment-based polarizable density embedding (PDE) model within a relativistic framework building on the eXact 2-Component (X2C) relativistic Hamiltonian, thereby taking the PDE method to a relativistic framework. The PDE model provides a robust solution to the electron-leakage problem, and we show that this newly implemented model offers an accurate way to model solvated systems possessing significant relativistic effects. To demonstrate the model's performance, we perform comparative calculations of the K- and L2,3-edge spectra of water-solvated cysteine (both protonated and deprotonated) with the X2C Hamiltonian. Particularly, with counterions such as Na+ in the solvent, electron leakage clearly shows in the older polarizable embedding model through spurious peaks in the spectra. However, when the PDE model is employed, these spurious peaks disappear.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"37 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849383","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":"Simulating Magnetic Field-Driven Real-Time Quantum Dynamics Using London Nuclear-Electronic Orbital Approach.","authors":"Diandong Tang,Aodong Liu,Tanner Culpitt,Sharon Hammes-Schiffer,Xiaosong Li","doi":"10.1021/acs.jctc.5c00273","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00273","url":null,"abstract":"Harnessing a static magnetic field to drive molecular vibrations presents a promising avenue for controlling chemical processes. However, the coupling of nuclear dynamics with an external magnetic field has largely been explored only through classical approximations. In this work, we introduce a time-dependent quantum dynamics formalism based on London nuclear-electronic orbitals, enabling the simulation of magnetic field-driven quantum dynamics. Through simulations of HCN and H2CO molecules, we provide a detailed analysis of how the relative orientation of the magnetic field and vibrational symmetry influence the resulting quantum dynamics. Our findings reveal field-induced mode couplings and symmetry-dependent effects, offering new insights into the role of magnetic fields in vibrational control. This work establishes a quantum mechanical framework for understanding and manipulating vibrational dynamics using external magnetic fields, paving the way for novel applications in spectroscopy, reaction dynamics, and quantum control.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"5 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851013","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":"Active Learning FEP: Impact on Performance of AL Protocol and Chemical Diversity.","authors":"Richard Lonsdale,Jack Glancy,Leen Kalash,David Marcus,Ian D Wall","doi":"10.1021/acs.jctc.5c00128","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00128","url":null,"abstract":"Active learning using models built on binding potency predictions from free energy perturbation (AL-FEP) has been proposed as a method for generating machine learning models capable of predicting biochemical potency for early-stage lead optimization where limited measured data are available. Two applications of AL-FEP are described here for different bromodomain inhibitor series that were developed in historic GSK projects: one where the core is kept constant and the other where core changes are included in the pool of compound ideas. Measured biochemical potency data have been used to assess the performance of the final models and demonstrate that well-performing models can be generated within several rounds of active learning, especially when the core is kept constant. To apply this method routinely to drug discovery projects, a retrospective evaluation of the AL-FEP workflow has been conducted covering parameters including the compound selection strategy, explore-exploit ratios, and number of compounds selected per cycle. Significant differences in performance in terms of model enrichment and R2 are observed and rationalized. Recommendations are made as to when specific parameters should be employed for AL-FEP depending on the context (maximizing potency or broad-range prediction accuracy) in which the final model is to be deployed.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"3 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846326","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":"Role of Noise-Modulated Self-Propulsion in Driving Spatiotemporal Orders in Active Systems.","authors":"Kaustav Mondal,Tarpan Maiti,Pushpita Ghosh","doi":"10.1021/acs.jctc.5c00093","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00093","url":null,"abstract":"Fluctuations play a pivotal role in driving spatiotemporal order in active matter systems. In this study, we employ a novel analytical framework to investigate the impact of dichotomous noise on the self-propelling velocity of active particle systems such as polymerizing actin filaments or reproducing elongated bacteria. By incorporating dichotomous fluctuations with Ornstein-Zernike correlations into a continuum-based model, we derive a bifurcation condition in the noise parameter space, revealing a noise-induced instability that drives the emergence of traveling waves. This approach demonstrates how specific noise strengths and correlation times expand the instability region by introducing effective new degrees of freedom that alter the system's stability matrix. Advance numerical simulations, meticulously designed to handle the properties of dichotomous noise, validate these theoretical predictions and reveal excellent agreement. A key finding is the observation of wave-reversal behavior, driven by the sign alternation of the noise-modulated advection term and modulated by the relaxation time. Remarkably, we identify a finite parameter range where this reversal is suppressed, offering new insights into noise-induced bifurcations and spatiotemporal dynamics. Our combined analytical and numerical approach provides a deeper understanding of the role of noise in shaping self-organization and pattern formation in biological and synthetic active systems.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"75 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846329","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":"Exploring the Linear Energy Relationships between Activation Energy and Reaction Energy under an Electric Field.","authors":"Supin Zhao,Ke Gong,Zhexuan Song,Giuseppe Cassone,Jing Xie","doi":"10.1021/acs.jctc.5c00225","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00225","url":null,"abstract":"Electric-field (EF)-mediated chemistry has recently garnered increasing attention partly owing to its capability to catalyze a broad range of chemical reactions. How the EF affects the kinetics and thermodynamics of target reactions is a critical question. Herein, both density functional theory (DFT) and MP2 calculations suggest that the change of activation energy ΔΔE‡ and the change of reaction energy ΔΔErxn under an EF display a linear energy relationship (LER) ΔΔE‡ = mΔΔErxn. This has been tested against several reactions such as SN2 and proton transfer reactions, including neutral and charged systems and endothermic and exothermic processes. The linear coefficient m approximates to the ratio of the dipole moment change, i.e., Δμ‡/Δμrxn, of the studied reactions. The LER holds well at EF strengths up to ≈1 V/nm but deviates from the DFT-calculated results at larger EFs. Such deviations are mainly caused by the molecular geometry changes under an EF. Systems with larger polarizability experience greater geometry changes under an EF, thus leading to larger deviations. In addition, we propose that the reaction barrier can be predicted by -Δμ‡F - 0.5Δα‡F2, while it is well approximated by -Δμ‡F for small EF strengths. The proposed LER and the field-dependent barrier estimation promise broad applicability in EF-mediated chemical reactions.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"9 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846327","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":"A Hybrid Bottom-Up and Data-Driven Machine Learning Approach for Accurate Coarse-Graining of Large Molecular Complexes.","authors":"Korbinian Liebl,Gregory A Voth","doi":"10.1021/acs.jctc.5c00063","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00063","url":null,"abstract":"Bottom-up coarse-graining refers to the development of low-resolution simulation models that are thermodynamically consistent with certain distributions from fully atomistic simulations. Force-matching and relative entropy minimization represent two major, frequently applied methods that allow to develop such bottom-up models. Nevertheless, atomistic simulations can often provide only limited sampling of the phase space. For bottom-up coarse-graining, these limitations may result in overfitting of the atomistic reference data, especially for large molecular complexes, where the learning may be agnostic of the actual affinities between binding partners. As a solution to this problem, we devise a data-driven machine learning hybrid coarse-graining concept that represents a regularized version of the relative entropy minimization approach. We demonstrate that this new approach allows one to develop coarse-grained models for molecular complexes that reproduce the targeted binding affinity but also describe the underlying complex structure accurately. The trained models therefore show diverse behavior as they can undergo frequent unbinding and binding events and are also transferable for simulating entire protein lattices, e.g., for a virus capsid.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"29 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846358","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":"Improving the Hit Rates of Virtual Screening by Active Learning from Bioactivity Feedback.","authors":"Xun Deng,Junlong Liu,Zhike Liu,Jiansheng Wu,Fuli Feng,Jieping Ye,Zheng Wang","doi":"10.1021/acs.jctc.4c01618","DOIUrl":"https://doi.org/10.1021/acs.jctc.4c01618","url":null,"abstract":"Virtual screening has been widely used to identify potential hit candidates that can bind to the target protein in drug discovery. Contemporary screening methods typically rely on oversimplified scoring functions, frequently yielding one-digit hit rates (or even zero) among top-ranked candidates. The substantial cost of laboratory validation further constrains the exploration of candidate molecules. We find that test-time prediction refinement is almost blank in this area, which means bioactivity feedback in the wet-lab experiments is neglected. Here, we introduce an Active Learning from Bioactivity Feedback (ALBF) framework to enhance the weak hit rate of current virtual screening methods. ALBF spends the budget of wet-lab experiments iteratively and leverages the target-specific bioactivity insights from current wet-lab tests to refine the score results (i.e., rankings). Our framework consists of two components: a novel query strategy that considers the evaluation quality and its overall influence on other top-scored molecules; and an efficient score optimization strategy that propagates the bioactivity feedback to structurally similar molecules. We evaluated ALBF on diverse subsets of the well-known DUD-E and LIT-PCBA benchmarks. Our active learning protocol averagely enhances top-100 hit rates by 60% and 30% on DUD-E and LIT-PCBA with 50 to 200 bioactivity queries on the selected molecules that are deployed in ten rounds. The consistently superior performance demonstrates ALBF's potential to enhance both the accuracy and cost-effectiveness of active learning-based laboratory testing.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"26 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846328","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":"Small Molecules Targeting the Structural Dynamics of AR-V7 Partially Disordered Proteins Using Deep Ensemble Docking.","authors":"Pantelis Karatzas,Z Faidon Brotzakis,Haralambos Sarimveis","doi":"10.1021/acs.jctc.5c00171","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00171","url":null,"abstract":"The extensive conformational dynamics of partially disordered proteins hinders the efficiency of traditional in-silico structure-based drug discovery approaches due to the challenge of screening large chemical spaces of compounds, albeit with an excessive number of transient binding sites, quickly making this problem intractable. In this study, using the monomer of the AR-V7 transcription factor splicing variant related to prostate cancer as a test case, we present a deep ensemble docking pipeline that accelerates the screening of small molecule binders targeting partially disordered proteins at functional regions. By swiftly identifying the conformational ensemble of AR-V7 and reducing the dimension of binding sites by a factor of 90, we identify functionally relevant binding sites along the AR-V7 structural ensemble at phase separation-prone regions that have been experimentally shown to contribute to enhanced transcription activity and the onset of tumor growth. Following this, we combine physics-based molecular docking and multiobjective classification machine learning models to speed up the screening for binders in a larger chemical space able to target these functional multiple binding sites of AR-V7. This step increases the multibinding site hit rate of small molecules by a factor of 17 compared to naive molecular docking. Finally, assessing in atomistic molecular dynamics the effect of a selected binder on AR-V7 dynamics, we find that in the presence of the ChEMBL22003 compound, AR-V7 exhibits less conformational entropy, smaller solvent exposure of phase separation-prone regions, and higher solvent exposure of other protein regions, promoting this compound as a potential AR-V7 phase separation modulator.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"5 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836613","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":"Calculating Bond Capacities by Linear Response Methods.","authors":"Jonas E S Mikkelsen,Frank Jensen","doi":"10.1021/acs.jctc.5c00233","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00233","url":null,"abstract":"Bond capacities can be considered as atom-atom condensed versions of the density response function. They quantify the ease with which the electron density can be transferred between atoms due to differences in potential and are thus central quantities for modeling charge flow in force fields. We describe an implementation of calculating bond capacities by linear response methods with the minimal basis iterative stockholder definition of atoms in molecules. The calculated bond capacities are moderately sensitive to the level of theory at Hartree-Fock, density functional theory, and multiconfigurational self-consistent field and are insensitive to basis set quality beyond a polarized double-ζ quality. The dependence of bond capacities on chemical structure displays a high degree of transferability and conforms to the concept of functional groups. Bond capacities connect all atom pairs in a molecule; however, the magnitude rapidly diminishes as a function of the number of connecting bonds for the nonconjugated system, while a less rapid decay and oscillating pattern is observed for conjugated systems.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"40 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836611","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}