Journal of Chemical Theory and Computation最新文献_第7页

In Silico Study of Ionizable Lipid Nanoparticles Using the SPICA Force Field. 利用SPICA力场对可电离脂质纳米颗粒的硅研究。

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-06-10 DOI: 10.1021/acs.jctc.5c00498

Akhil Pratap Singh, Hiroki Tanaka, Yusuke Miyazaki, Shusaku Nagano, Wataru Shinoda

{"title":"In Silico Study of Ionizable Lipid Nanoparticles Using the SPICA Force Field.","authors":"Akhil Pratap Singh, Hiroki Tanaka, Yusuke Miyazaki, Shusaku Nagano, Wataru Shinoda","doi":"10.1021/acs.jctc.5c00498","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00498","url":null,"abstract":"Lipid nanoparticles (LNPs), composed of ionizable amino lipids, phosphatidylcholines (PC) lipids, and cholesterol, have shown promise as delivery vehicles for therapeutic oligonucleotides in various applications, including cancer immunotherapies, cellular reprogramming, genome editing, and viral vaccines (e.g., COVID-19 vaccines). However, the molecular characterization of ionizable amino lipids and their assemblies, such as LNPs, both in silico and in vitro, remains in its early stages. In particular, in silico studies on LNPs to understand their nanostructure have been limited due to the need for accurate coarse-grained (CG) models. In this study, we expand the SPICA force field to develop a more reliable and accurate explicit CG model for investigating the structure and properties of model LNPs through in silico experiments. Using this CG model, we performed molecular dynamics simulations on LNP systems with varying helper lipids and pH conditions. Our results reveal bilayer structures with double-stranded DNA (dsDNA) sandwiched between closely apposed monolayers in LNPs at pH 4, while at pH 7, dsDNA molecules are embedded within amorphous domains inside the LNPs. These in silico-optimized microstructures align well with the experimental observations obtained from small-angle X-ray scattering and cryogenic transmission electron microscopy (cryo-TEM). Additionally, a detailed analysis of LNPs containing different helper lipids explains why replacing saturated PC lipids with unsaturated PC lipids enhances the DNA transfection activity. Overall, this study provides a robust CG model for in silico studies of LNPs and offers in-depth molecular-level insights to advance their design for improved stability and efficacy.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256708","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

Locality-Sensitive Hashing-Based Data Set Reduction for Deep Potential Training. 基于位置敏感哈希的深度潜能训练数据集约简。

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-06-10 DOI: 10.1021/acs.jctc.5c00366

Anmol, Anuj Kumar Sirohi, Neha, Jayadeva, Sandeep Kumar, Tarak Karmakar

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IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-06-10

Chung Bin Park, and , Bong June Sung*,

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Multiproperty Deep Learning of the Correlation Energy of Electrons and the Physicochemical Properties of Molecules. 电子相关能与分子物理化学性质的多性质深度学习。

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-06-10 DOI: 10.1021/acs.jctc.5c00414

Yan Yuan, Yilin Zhao, Linling Lu, Junjie Wang, Jingbo Chen, Shubin Liu, Paul W Ayers, Dongbo Zhao

{"title":"Multiproperty Deep Learning of the Correlation Energy of Electrons and the Physicochemical Properties of Molecules.","authors":"Yan Yuan, Yilin Zhao, Linling Lu, Junjie Wang, Jingbo Chen, Shubin Liu, Paul W Ayers, Dongbo Zhao","doi":"10.1021/acs.jctc.5c00414","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00414","url":null,"abstract":"The density-based descriptors from the information-theoretic approach (ITA) are used as features for multiproperty deep learning (DL), predicting the correlation energy and physicochemical properties of molecules. In addition to response properties (molecular polarizability αiso and NMR shielding constant σiso) where ITA has been shown to work well before, we consider four conceptually distinct but practically related concepts: electron correlation, redox potential, octanol-water partition coefficient (logKow), and the wavelength of maximum absorption (λmax). The DL-predicted results are in good agreement with either the calculated or experimental counterparts, indicative of the model's robustness. We verified the transferability of redox potentials of phenazine derivatives. Generalizability is observed for the λmax data: small chromophores are used for training/validation but the test set has sizable molecules. The trained DL model outperforms the conventional TD-DFT method in terms of accuracy and efficiency. We also showcase that the isotropic quadrupole moment (Θiso) is a good predictor of logKow. This establishes that versatile density-based ITA quantities can be used to make accurate, low-cost predictions of both extensive and intensive properties, suggesting that this ITA-DL protocol has the potential for closed-loop chemistry automation. Implication of this work is straightforward, that a universal framework should be possible based on the ITA-based DL models.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256712","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

Exact Mo̷ller-Plesset Adiabatic Connection Correlation Energy Densities. 精确的Mo´ller-Plesset绝热连接相关能量密度。

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-06-10 Epub Date: 2025-05-21 DOI: 10.1021/acs.jctc.5c00348

Kimberly J Daas, Heng Zhao, Elias Polak, Stefan Vuckovic

{"title":"Exact Mo̷ller-Plesset Adiabatic Connection Correlation Energy Densities.","authors":"Kimberly J Daas, Heng Zhao, Elias Polak, Stefan Vuckovic","doi":"10.1021/acs.jctc.5c00348","DOIUrl":"10.1021/acs.jctc.5c00348","url":null,"abstract":"The Mo̷ller-Plesset adiabatic connection (MPAC) provides a powerful tool for developing density functional theory (DFT)-like approximations that map Hartree-Fock densities to the wave function-based correlation energy, thereby leveraging both wave function and DFT concepts for electronic structure approximations. A key object in this context is the correlation energy density, which represents the local (pointwise) contribution to the total correlation energy. While well-studied in DFT, it remains largely unexplored in the wave function framework. Here, we introduce a rigorous formulation of the wave function-based correlation energy density within MPAC, implement it via full configuration interaction calculations, and analyze its behavior and physically meaningful contributions for representative small (di)atomic systems. We define this quantity by employing a general gauge strategy, from which the conventional DFT correlation energy density gauge also arises. We then discuss the resulting commonalities and differences between correlation energy densities in the DFT and wave function frameworks and derive the small-interaction (MP2) limit of the latter in terms of Hartree-Fock orbitals. Finally, we show how these newly introduced energy densities can serve as new approximation targets in both machine-learning-assisted and traditional electronic structure methods for mapping HF-density-based features to correlation energy within the wave function framework.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":"5501-5513"},"PeriodicalIF":5.7,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118400","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

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-06-10

Rose K. Cersonsky*, Bingqing Cheng, Marco De Vivo and Pratyush Tiwary,

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IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-06-10

Kyeong Su Min, and , Jae Woo Park*,

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IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-06-10

Tong Guan, Xin-Tian Xie, Xiao-Jie Zhang, Cheng Shang* and Zhi-Pan Liu*,

引用次数: 0

Relativistic Extended-Coupled-Cluster Calculations of P,T-Odd Sensitivity Parameters for Diatomic Molecules. 双原子分子P、t奇灵敏度参数的相对论扩展耦合簇计算。

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-06-10 Epub Date: 2025-05-19 DOI: 10.1021/acs.jctc.5c00329

Kaushik Talukdar, Malaya K Nayak, Nayana Vaval, Sourav Pal

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Relativistic Extended-Coupled-Cluster Calculations of <ns0:math><ns0:mi>P</ns0:mi><ns0:mo>,</ns0:mo><ns0:mi>T</ns0:mi></ns0:math>-Odd Sensitivity Parameters for Diatomic Molecules.","authors":"Kaushik Talukdar, Malaya K Nayak, Nayana Vaval, Sourav Pal","doi":"10.1021/acs.jctc.5c00329","DOIUrl":"10.1021/acs.jctc.5c00329","url":null,"abstract":"We demonstrate the first application of the four-component relativistic extended-coupled-cluster (ECC) method, a variational approach, to determine the <math><mi>P</mi><mo>,</mo><mi>T</mi></math>-odd molecular electronic structure parameters necessary for high-precision spectroscopic measurements on ultracold molecules. We calculate the <math><mi>P</mi><mo>,</mo><mi>T</mi></math>-odd sensitivity parameters for two moderately heavy, laser-coolable diatomic molecules (SrH and SrF), as well as two relatively lighter open-shell molecules (CaH and CaF) in their ground electronic state, 2Σ1/2. The hyperfine structure constants of 87Sr in the SrF and SrH molecules are also computed and compared with available experimental results to assess the accuracy of our calculations. Furthermore, we examine the roles of electron correlation, relativity, basis set, and virtual spinors in ECC calculations of the sensitivity parameters. Our study reveals that the ECC method is a reliable technique for studying <math><mi>P</mi><mo>,</mo><mi>T</mi></math>-odd properties of diatomic molecules such as CaH, CaF, SrH and SrF, yielding results in very good agreement with those obtained using two widely employed analytic approaches: the Z-vector technique and the linear expectation-value method within the relativistic single-reference coupled-cluster singles and doubles framework.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":"5481-5489"},"PeriodicalIF":5.7,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144092134","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}

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Trajectory Retracing of the Packaging and Ejection Processes of Coaxially Spooled DNA. 同轴缠绕DNA封装和抛射过程的轨迹回溯。

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-06-10 Epub Date: 2025-05-27 DOI: 10.1021/acs.jctc.5c00137

Chung Bin Park, Bong June Sung

{"title":"Trajectory Retracing of the Packaging and Ejection Processes of Coaxially Spooled DNA.","authors":"Chung Bin Park, Bong June Sung","doi":"10.1021/acs.jctc.5c00137","DOIUrl":"10.1021/acs.jctc.5c00137","url":null,"abstract":"The coaxial spool structure of DNA has been regarded as an equilibrium conformation inside of a viral capsid. It has also been accepted that the DNA conformation inside the viral capsid should correlate strongly with the ejection of DNA out of the viral capsid. However, how the coaxial spool structure of DNA would affect the ejection kinetics remains elusive at the molecular level. In this study, we perform extensive Langevin dynamics simulations for a single polymer chain packaged within a small confinement to mimic the packaging and ejection processes of viral DNA and investigate the effects of its conformation on the ejection kinetics. We show that when the polymer chain within a small rectangular confinement is coaxially spooled, its ejection kinetics is facilitated significantly due to the trajectory retracing. We tune the conformation of the polymer chain inside the confinement by changing both the chain rigidity and the aspect ratio (γ) of the confinement. As either the aspect ratio (γ) decreases or the rigidity increases, the chain packaged inside the confinement is more likely to have a coaxial spool structure. And the polymer chain of the coaxial spool structure ejects quickly than chains of other conformations. We find that the coaxial spool structure enables the single chain to follow the reverse pathway of the packaging during ejection without significant structural rearrangement, thus enhancing the ejection kinetics.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":"5736-5745"},"PeriodicalIF":5.7,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144155244","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