Journal of Chemical Information and Modeling 最新文献

Segmentation for Learning Adsorption Patterns and Residence-Time Kinetics on Amorphous Surfaces. 学习非晶表面吸附模式和停留时间动力学的分割。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-10-03 DOI: 10.1021/acs.jcim.5c01463

Mattia Turchi,Ivan Lunati

{"title":"Segmentation for Learning Adsorption Patterns and Residence-Time Kinetics on Amorphous Surfaces.","authors":"Mattia Turchi,Ivan Lunati","doi":"10.1021/acs.jcim.5c01463","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01463","url":null,"abstract":"Heterogeneous surfaces such as amorphous silica are characterized by highly heterogeneous local atomic environments that govern the adsorption of gas molecules through spatial arrangements. These surfaces exhibit properties that are particularly relevant for adsorption and catalytic applications. Here, we investigate CO2 adsorption landscapes, captured by CO2 density maps, which display complex patterns requiring machine learning (ML) segmentation for systematic analysis. We present an optimized segmentation protocol based on a modified Random Forest (RF) classifier designed to control the morphology and spatial extent of the segmented regions via feature smoothing and standardized training parameters. While broadly applicable for specific modeling goals and properties of interest, here, the method is tailored to identify high-density regions that dominate heterogeneous adsorption dynamics. For these regions, we extract residence-time statistics that deviate from exponential behavior, revealing multiple time scales associated with distinct surface defects on amorphous surfaces. The extracted kinetics provide essential information for coarse-grained models of adsorption on disordered surfaces. Such models, parametrized using atomistic simulations, enable the prediction of macroscopically measurable adsorption and desorption rates, which can be directly compared with experiments also under conditions not limited by mass transfer.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"27 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

ML-PLA: Enhancing Protein-Ligand Binding Affinity Prediction with Microenvironment and Long-Range Interaction-Aware Graph Neural Networks. ML-PLA：利用微环境和远程相互作用感知图神经网络增强蛋白质配体结合亲和力预测。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-10-03 DOI: 10.1021/acs.jcim.5c01974

Yajie Meng,Zhuang Zhang,Jincan Li,Xianfang Tang,Changcheng Lu,Zilong Zhang,Feifei Cui,Pan Zeng,Bo Li,Junlin Xu

{"title":"ML-PLA: Enhancing Protein-Ligand Binding Affinity Prediction with Microenvironment and Long-Range Interaction-Aware Graph Neural Networks.","authors":"Yajie Meng,Zhuang Zhang,Jincan Li,Xianfang Tang,Changcheng Lu,Zilong Zhang,Feifei Cui,Pan Zeng,Bo Li,Junlin Xu","doi":"10.1021/acs.jcim.5c01974","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01974","url":null,"abstract":"Accurately predicting protein-ligand binding affinity (PLA) is essential in drug discovery for identifying lead compounds. The sequence and structural contexts of an amino acid residue (i.e., microenvironment) describe the surrounding chemical properties and geometric features. While recent graph-based models have shown considerable promise, they often construct microenvironment representations using a shallow fusion of sequence and structural features, potentially failing to capture their full synergistic effects. Furthermore, the common reliance on a fixed distance threshold to define interaction space, while computationally efficient, inherently limits the ability to model key nonlocal biological phenomena. To address these issues, we propose a novel method named ML-PLA. Specifically, ML-PLA employs a heterogeneous graph neural network to model protein microenvironments by aggregating both sequence and structure information from neighboring nodes. Furthermore, we incorporate a vector quantized-variational autoencoder to capture the diversity and complexity of microenvironments, producing chemically meaningful, fine-grained representations. To effectively exploit long-range interaction information, ML-PLA projects protein-ligand complex atoms into multiple virtual atoms using a multihead attention mechanism, rather than simply increasing the number of graph neural network layers. This approach effectively embeds the interaction information into the complex atom features while simultaneously avoiding oversmoothing. Extensive experiments on the CASF-2016 and CASF-2013 benchmark data sets demonstrate the significant effectiveness and robust generalization capabilities of ML-PLA compared with state-of-the-art methods.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"98 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

VeGA: A Versatile Generative Architecture for Bioactive Molecules across Multiple Therapeutic Targets. VeGA：跨多种治疗靶点的生物活性分子的多功能生成结构。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-10-02 DOI: 10.1021/acs.jcim.5c01606

Pietro Delre,Antonio Lavecchia

{"title":"VeGA: A Versatile Generative Architecture for Bioactive Molecules across Multiple Therapeutic Targets.","authors":"Pietro Delre,Antonio Lavecchia","doi":"10.1021/acs.jcim.5c01606","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01606","url":null,"abstract":"In this paper, we present VeGA, a lightweight, decoder-only Transformer model for de novo molecular design. VeGA balances a streamlined architecture with robust generative performance, making it highly efficient and well-suited for resource-limited environments. Pretrained on ChEMBL, the model demonstrates strong performance against cutting-edge approaches, achieving high validity (96.6%) and novelty (93.6%), ranking among the top performers in the MOSES benchmark. The model's main strength lies in target-specific fine-tuning under challenging, data-scarce conditions. In a rigorous, leakage-safe evaluation across five pharmacological targets against state-of-the-art models (S4, R4), VeGA proved to be a powerful \"explorer\" that consistently generated the most novel molecules while maintaining a strong balance between discovery performance and chemical realism. This capability is particularly evident in the extremely low-data scenario of mTORC1, where VeGA achieved top-tier results. As a case study, VeGA was applied to the Farnesoid X receptor (FXR), generating novel compounds with validated binding potential through molecular docking. The model is available as an open-access platform to support medicinal chemists in designing novel, target-specific chemotypes (https://github.com/piedelre93/VeGA-for-de-novo-design). Future developments will focus on incorporating conditioning strategies for multiobjective optimization and integrating experimental in vitro validation workflows.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"76 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Exploring Peptide's Antifreeze Activity Using a Semi-Automated Molecular Dynamics-Enabled Screening Framework. 利用半自动分子动力学筛选框架探索肽的抗冻活性。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-10-02 DOI: 10.1021/acs.jcim.5c01112

Yuan Yuan,Micholas Dean Smith,Vermont P Dia,Tong Wang

{"title":"Exploring Peptide's Antifreeze Activity Using a Semi-Automated Molecular Dynamics-Enabled Screening Framework.","authors":"Yuan Yuan,Micholas Dean Smith,Vermont P Dia,Tong Wang","doi":"10.1021/acs.jcim.5c01112","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01112","url":null,"abstract":"The generation of molecular dynamics input files for the study of protein and peptide antifreeze behavior is time-consuming and tedious. This study presents the use of a nonequilibrium molecular dynamics simulations pipeline to infer relative rankings of the ice refreezing inhibition or antifreeze activities of peptides. By leveraging a combination of existing tools, the pipeline developed here allows researchers, using only amino acid sequences and requested ice-water ratios, to quickly generate molecular dynamics-ready input files of proteins and peptides at ice-water interfaces using an amino acid sequence alone. Using this pipeline, this work examines potential relationships between the secondary structure and chain length of plant-derived peptides and their antifreeze activity. Using nine different peptides, in groups of three with different peptide chain lengths, namely, short, intermediate, and long (20-25, 35-40, and 55-60 amino acids, respectively), and distinct secondary structural motifs (α-helix, β-sheet, and random coil), potential relationships between antifreeze activity and peptide structural properties were examined. Our results indicate that peptides with stable and rigid secondary structures, especially those rich in α-helix content, exhibit higher antifreeze activity, regardless of the chain lengths tested. Additional analysis of the simulations also reveals that the peptides demonstrating extensive interactions with water molecules display enhanced antifreeze properties, even those with relatively flexible conformations. The existing findings improve the understanding of structure-function relationships in antifreeze peptides and provide practical insights for designing novel and potentially cost-effective peptides for applications in the cryopreservation of food and biological materials.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"6 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

multiSMD - A Python Toolset for Multidirectional Steered Molecular Dynamics. multiSMD -一个用于多向操纵分子动力学的Python工具集。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-10-02 DOI: 10.1021/acs.jcim.5c01742

Katarzyna Walczewska-Szewc,Beata Niklas,Kamil Szewc,Wiesław Nowak

{"title":"multiSMD - A Python Toolset for Multidirectional Steered Molecular Dynamics.","authors":"Katarzyna Walczewska-Szewc,Beata Niklas,Kamil Szewc,Wiesław Nowak","doi":"10.1021/acs.jcim.5c01742","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01742","url":null,"abstract":"Molecular forces govern all biological processes from cellular mechanics to molecular recognition events. Understanding the direction-dependence of these forces is particularly critical for elucidating fundamental interactions, such as protein-protein binding, ligand dissociation, and signal mechanotransduction. While steered molecular dynamics (SMD) simulations enable the study of force-induced transitions, conventional single-direction approaches may overlook anisotropic mechanical responses inherent to biomolecular systems. Therefore, probing the mechanical stability of molecular systems with respect to a director of an external force may provide critical information. Here, we present multiSMD, a Python-based tool that automates the setup and analysis of multidirectional SMD simulations in NAMD and GROMACS. By systematically probing forces along multiple spatial vectors, multiSMD captures direction-dependent phenomena, such as changing energy barriers or structural resilience, that remain hidden in standard SMD. We demonstrate the utility of our approach through three distinct applications: (i) anisotropic unbinding in a protein-protein complex, (ii) search for ligand dissociation pathways dependent on the pulling direction, and (iii) force-induced remodeling of intrinsically disordered regions in proteins. multiSMD streamlines the exploration of nanomechanical anisotropy in biomolecules, offering a computational framework to guide experiments (e.g., atomic force microscopy - AFM or optical tweezers) and uncover mechanistic properties inaccessible to single-axis methods.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"199 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Correction to "Sequence-Dependent Shape and Stiffness of DNA and RNA Double Helices: Hexanucleotide Scale and Beyond". 修正“DNA和RNA双螺旋的序列依赖形状和硬度：六核苷酸尺度及以上”。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-10-01 DOI: 10.1021/acs.jcim.5c02218

Pavlína Slavníková,Marek Cuker,Eva Matoušková,Ivan Čmelo,Marie Zgarbová,Petr Jurečka,Filip Lankaš

引用次数: 0

Targeting SARS-CoV-2 Receptor Binding Domain and Main Protease with D-Peptides. 利用d肽靶向SARS-CoV-2受体结合域和主要蛋白酶

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-10-01 DOI: 10.1021/acs.jcim.5c01839

Laiyi Feng,Jingjia Liu,Chunmei Li,Qian Wang,Luhua Lai,Changsheng Zhang

{"title":"Targeting SARS-CoV-2 Receptor Binding Domain and Main Protease with D-Peptides.","authors":"Laiyi Feng,Jingjia Liu,Chunmei Li,Qian Wang,Luhua Lai,Changsheng Zhang","doi":"10.1021/acs.jcim.5c01839","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01839","url":null,"abstract":"D-peptide binders are promising drug candidates that may offer better binding specificity and improved metabolic stability than canonical peptide drugs. However, there is a lack of efficient computational methods for the de novo design of D-peptide binders based on target protein structure. We developed a general framework for de novo design of D-helical peptide binders for the target protein, which consists of curved helical scaffold generation, scaffold docking to the target surface, Rosetta based sequence design, and in silico selection. For the convenience of conformation sampling, the targeted protein is mirrored to D-type, while the peptide ligands are presented in L-type during the sequence design step. We have applied this workflow to design D-helical peptides targeting the two major targets for inhibiting SARS-CoV-2, the receptor binding domain (RBD) of the spike protein and the main protease (3CLpro), to alter its oligomeric state and inhibit its activity. We found that both the receptor binding surface of RBD and the groove between the catalytic and regulation domains of 3CLpro are favorable for the binding of 28-mer D-helical peptides. We designed and tested 8 D-peptides for RBD and found 4 of them bound to RBD with the best one demonstrating submicromolar dissociation constant and the ability to block the binding of full-length spike protein toward its receptor, human angiotensin-converting enzyme 2. For 3CLpro, 3 of the 12 designed D-peptides could inhibit its catalytic activity. And the best peptide LY09 binds 3CLpro with submicromolar dissociation constant and disrupts the dimerization of 3CLpro. The D-peptide binder docking and design tools are publicly available at https://github.com/laiyii/D-peptide-binder-design.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"24 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145195127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

In Silico Structural Modeling of the HuR-mRNA Complex: Insights into Structural and Functional Regulation. HuR-mRNA复合体的计算机结构建模：对结构和功能调控的见解。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-10-01 DOI: 10.1021/acs.jcim.5c01028

Davide Pietrafesa,Alice Romeo,Fabio Giovanni Tucci,Paola Fiorani,Federico Iacovelli,Mattia Falconi

{"title":"In Silico Structural Modeling of the HuR-mRNA Complex: Insights into Structural and Functional Regulation.","authors":"Davide Pietrafesa,Alice Romeo,Fabio Giovanni Tucci,Paola Fiorani,Federico Iacovelli,Mattia Falconi","doi":"10.1021/acs.jcim.5c01028","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01028","url":null,"abstract":"The RNA-binding protein HuR (embryonic lethal abnormal vision-like protein 1) regulates mRNA stability and translation. HuR contains three RNA-recognition motifs (RRMs): the RRM1 and RRM2 confer high-affinity mRNA binding, while RRM3 mediates protein oligomerization. Although HuR is predominantly nuclear, cellular stimuli trigger its cytoplasmic translocation via a nucleocytoplasmic shuttling sequence between the RRM2 and RRM3 domains. Despite HuR's critical role in post-transcriptional gene regulation, its full-length three-dimensional (3D) structure remains uncharacterized. In this study, we employed an in silico approach, combining molecular modeling, atomistic, and coarse-grained molecular dynamics simulations to build and validate a 3D model of the full-length HuR in complex with an mRNA fragment. Structural analysis of the model identified a tyrosine residue as a key mediator of HuR-RNA interaction stability and provided novel structural insights into HuR's RNA-binding mechanism, contributing to a deeper understanding of its regulatory functions.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"25 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 Temperature Stability from Sequence Segment Perspective. 从序列片段的角度模拟酶的温度稳定性。

IF 5.3 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-10-01 DOI: 10.1021/acs.jcim.5c01674

Ziqi Zhang, Shiheng Chen, Runze Yang, Zhisheng Wei, Wei Zhang, Lei Wang, Zhanzhi Liu, Fengshan Zhang, Jing Wu, Xiaoyong Pan, Hongbin Shen, Longbing Cao, Zhaohong Deng

{"title":"Modeling Enzyme Temperature Stability from Sequence Segment Perspective.","authors":"Ziqi Zhang, Shiheng Chen, Runze Yang, Zhisheng Wei, Wei Zhang, Lei Wang, Zhanzhi Liu, Fengshan Zhang, Jing Wu, Xiaoyong Pan, Hongbin Shen, Longbing Cao, Zhaohong Deng","doi":"10.1021/acs.jcim.5c01674","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01674","url":null,"abstract":"Developing enzymes with desired thermal properties is crucial for a wide range of industrial and research applications, and determining temperature stability is an essential step in this process. Experimental determination of thermal parameters is labor-intensive, time-consuming, and costly. Moreover, existing computational approaches are often hindered by limited data availability and imbalanced distributions. To address these challenges, we introduce a curated temperature stability data set designed for model development and benchmarking in enzyme thermal modeling. Leveraging this data set, we present the Segment Transformer, a novel deep learning framework that enables efficient and accurate prediction of enzyme temperature stability. The model achieves state-of-the-art performance with RMSE of 23.29, MAE of 17.37, Pearson correlation of 0.35, and Spearman correlation of 0.34, respectively. These results highlight the effectiveness of incorporating segment-level representations, grounded in the biological observation that different regions of a protein sequence contribute unequally to thermal behavior. As a proof of concept, we applied the Segment Transformer to guide the engineering of a cutinase enzyme. Experimental validation demonstrated a 1.64-fold improvement in relative activity following heat treatment, achieved through only 17 mutations and without compromising catalytic function.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

MDPath: Unraveling Allosteric Communication Paths of Drug Targets through Molecular Dynamics Simulations. MDPath：通过分子动力学模拟揭示药物靶点的变构通信路径。

IF 5.3 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-10-01 DOI: 10.1021/acs.jcim.5c01590

Niklas Piet Doering, Marvin Taterra, Marcel Bermúdez, Gerhard Wolber

引用次数: 0