Journal of Chemical Information and Modeling 最新文献

{"title":"DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials.","authors":"Jinzhe Zeng, Timothy J Giese, Duo Zhang, Han Wang, Darrin M York","doi":"10.1021/acs.jcim.4c02441","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02441","url":null,"abstract":"<p><p>Machine learning potentials (MLPs) have revolutionized molecular simulation by providing efficient and accurate models for predicting atomic interactions. MLPs continue to advance and have had profound impact in applications that include drug discovery, enzyme catalysis, and materials design. The current landscape of MLP software presents challenges due to the limited interoperability between packages, which can lead to inconsistent benchmarking practices and necessitates separate interfaces with molecular dynamics (MD) software. To address these issues, we present DeePMD-GNN, a plugin for the DeePMD-kit framework that extends its capabilities to support external graph neural network (GNN) potentials.DeePMD-GNN enables the seamless integration of popular GNN-based models, such as NequIP and MACE, within the DeePMD-kit ecosystem. Furthermore, the new software infrastructure allows GNN models to be used within combined quantum mechanical/molecular mechanical (QM/MM) applications using the range corrected ΔMLP formalism.We demonstrate the application of DeePMD-GNN by performing benchmark calculations of NequIP, MACE, and DPA-2 models developed under consistent training conditions to ensure fair comparison.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143727026","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}

{"title":"MartiniGlass: a Tool for Enabling Visualization of Coarse-Grained Martini Topologies.","authors":"Christopher Brasnett, Siewert J Marrink","doi":"10.1021/acs.jcim.4c02277","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02277","url":null,"abstract":"<p><p>As molecular modeling gains ever more prominence in understanding cellular processes, high quality visualization of models and dynamics has never been more important. Naturally, much molecular visualization software is written to enable the visualization of atomic level details in structures. While necessary, this means that visualization of increasingly popular coarse-grained (CG) models remains a challenge. Here, we present a Python package, MartiniGlass, that facilitates the visualization of systems simulated with the widely used CG Martini force field using the popular visualization package VMD. MartiniGlass rapidly processes molecular topologies and accounts for important topological features at CG resolution, such as secondary structure restraints, preparing them for easy visualization of simulated trajectories.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143727030","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}

{"title":"Knowledge-Based Artificial Intelligence System for Drug Prioritization.","authors":"Yinchun Su, Jiashuo Wu, Xilong Zhao, Yue Hao, Ziyi Wang, Yongbao Zhang, Yujie Tang, Bingyue Pan, Guangyou Wang, Qingfei Kong, Junwei Han","doi":"10.1021/acs.jcim.5c00027","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00027","url":null,"abstract":"<p><p><i>In silico</i> drug prioritization may be a promising and time-saving strategy to identify potential drugs, standing as a faster and more cost-effective approach than <i>de novo</i> approaches. In recent years, artificial intelligence has greatly evolved the drug development process. Here, we present a novel computational framework for drug prioritization, <i>labyrinth</i>, designed to simulate human knowledge retrieval and inference to identify potential drug candidates for each disease. With the integration of up-to-date clinical trials, literature co-occurrences, drug-target interactions, and disease similarities, our framework achieves over 90% predictive accuracy across clinical trial phases and strong alignment with clinical practice in TCGA cohorts. We have demonstrated effectiveness across 20 different disease categories with robust ROC-AUC metrics and the balance between predictive accuracy and model interpretability. We further demonstrate its effectiveness at both the population and the individual levels. This study not only demonstrates the capacity for its drug prioritization but underscores the importance of aligning computational models with intuitive human reasoning. We have wrapped the core function into an R package named <i>labyrinth</i>, which is freely available on GitHub under the GPL-v2 license (https://github.com/hanjunwei-lab/labyrinth).</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143727029","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}

{"title":"Investigation of Electrostatic Effects on Enyzme Catalysis: Insights from Computational Simulations of Monoamine Oxidase A Pathological Variants Leading to the Brunner Syndrome.","authors":"Martina Rajić, Jernej Stare","doi":"10.1021/acs.jcim.4c01698","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01698","url":null,"abstract":"<p><p>Brunner syndrome is a rare genetic disorder characterized by impulsive aggressiveness and intellectual disability, which is linked to impaired function of the monoamine oxidase A (MAO-A) enzyme. Patients with specific point mutations in the <i>MAOA</i> gene have been reported to exhibit these symptoms, along with notably elevated serotonin levels, which suggest a decreased catalytic performance of the mutated MAO-A enzymes. In this study, we present multiscale molecular simulations focusing on the rate-limiting step of MAO-A-catalyzed serotonin degradation for the C266F and V244I variants that are reportedly associated with pathologies characteristic of the Brunner syndrome. We found that the C266F mutation causes an approximately 18,000-fold slowdown of enzymatic function, which is equivalent to a <i>MAOA</i> gene knockout. For the V244I mutant, a somewhat smaller, yet still significant 300-fold slowdown has been estimated. Furthermore, we conducted a comprehensive comparison of the impact of enzyme electrostatics on the catalytic function of the wild-type (WT) MAO-A and both aforementioned mutants (C266F and V244I), as well as on the E446K mutant investigated in one of our earlier studies. The results have shown that the mutation induces a noteworthy change in electrostatic interactions between the reacting moiety and its enzymatic surroundings, leading to a decreased catalytic performance in all of the considered MAO-A variants. An analysis of mutation effects supported by geometry comparison of mutants and the wild-type enzyme at a residue level suggests that a principal driving force behind the altered catalytic performance of the mutants is subtle structural changes scattered along the entire enzyme. These shifts in geometry also affect domains most relevant to catalysis, where structural offsets of few tenths of an Å can significantly change contribution to the barrier of the involved residues. These results are in full agreement with the reasoning derived from clinical observations and biochemical data. Our research represents a step forward in the attempts of using fundamental principles of chemical physics in order to explain genetically driven pathologies. In addition, our results support the view that the catalytic function of enzymes is crucially driven by electrostatic interactions.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707738","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}

{"title":"Relative Binding Free Energy Estimation of Congeneric Ligands and Macromolecular Mutants with the Alchemical Transfer Method with Coordinate Swapping.","authors":"Emilio Gallicchio","doi":"10.1021/acs.jcim.5c00207","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00207","url":null,"abstract":"<p><p>We present the Alchemical Transfer with Coordinate Swapping (ATS) method to enable the calculation of the relative binding free energies between large congeneric ligands and single-point mutant peptides to protein receptors with the Alchemical Transfer Method (ATM) framework. Similarly to ATM, the new method implements the alchemical transformation as a coordinate transformation and works with any unmodified force fields and standard chemical topologies. Unlike ATM, which transfers whole ligands in and out of the receptor binding site, ATS limits the magnitude of the alchemical perturbation by transferring only the portion of the molecules that differ between the bound and unbound ligands. The common region of the two ligands, which can be arbitrarily large, is unchanged and does not contribute to the magnitude and statistical fluctuations of the perturbation energy. Internally, the coordinates of the atoms of the common regions are swapped to maintain the integrity of the covalent bonding data structures of the OpenMM molecular dynamics engine. The work successfully validates the method on protein-ligand and protein-peptide RBFE benchmarks. This advance paves the road for the application of the relative binding free energy Alchemical Transfer Method protocol to study the effect of protein and nucleic acid mutations on the binding affinity and specificity of macromolecular complexes.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707742","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}

{"title":"<i>UANanoDock</i>: A Web-Based <i>UnitedAtom</i> Multiscale Nanodocking Tool for Predicting Protein Adsorption onto Nanoparticles.","authors":"Julia Subbotina, Panagiotis D Kolokathis, Andreas Tsoumanis, Nikolaos K Sidiropoulos, Ian Rouse, Iseult Lynch, Vladimir Lobaskin, Antreas Afantitis","doi":"10.1021/acs.jcim.4c02292","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02292","url":null,"abstract":"<p><p><i>UANanoDock</i> is a web-based application with a graphical user interface designed for modeling protein-nanomaterial interactions, accessible via the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.com/compsafenano/uananodock/). The application's foundation lies in the UnitedAtom multiscale model, previously reported for predicting the adsorption energies of biopolymers and small molecules onto nanoparticles (NPs). <i>UANanoDock</i> offers insights into optimal protein orientations when bound to spherical NP surfaces, considering factors such as material type, NP radius, surface potential, and amino acid (AA) ionization states at specific pH levels. The tool's computational time is determined solely by the protein's AA count, regardless of NP size. With its efficiency (e.g., approximately 60 s processing time for a 1331 AA protein) and versatility (accommodating any protein with a standard AA sequence in PDB format), <i>UANanoDock</i> serves as a prescreening tool for identifying proteins likely to adsorb onto NP surfaces. An illustration of <i>UANanoDock</i>'s utility is provided, demonstrating its application in the rational design of immunoassays by determining the preferred orientation of the immunoglobulin G (IgG) antibody adsorbed on Ag NPs.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699093","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}

{"title":"Identification of Small-Molecule Inhibitors for Enterovirus A71 IRES by Structure-Based Virtual Screening.","authors":"Kaichen Wang, Sean Xian Yu Lee, Chaitanya K Jaladanki, Wei Shen Ho, Justin Jang Hann Chu, Hao Fan, Christina Li Lin Chai","doi":"10.1021/acs.jcim.4c01903","DOIUrl":"10.1021/acs.jcim.4c01903","url":null,"abstract":"<p><p>Structured RNAs play a crucial role in regulating gene expression, which includes both protein synthesis and RNA processing. Dysregulation of these processes is associated with various conditions, including viral and bacterial infections, as well as cancer. The unique tertiary structures of structured RNAs provide an opportunity for small molecules to directly modulate such processes, making them promising targets for drug discovery. Although small-molecule inhibitors targeting RNA have shown early success, <i>in silico</i> strategies like structure-based virtual screening remain underutilized for RNA-targeted drug discovery. In this study, we developed a virtual screening scheme targeting the structural ensemble of EV-A71 IRES SL II, a noncoding viral RNA element essential for viral replication. We subsequently optimized the experimentally validated hit compound IRE-03 from virtual screening through an \"analog-by-catalog\" search. This led to the identification of a more potent IRES inhibitor, IRE-03-3, validated through biochemical and functional assays with an EC₅₀ value of 11.96 μM against viral proliferation. Our findings demonstrate that structure-based virtual screening can be effectively applied to RNA targets, providing exciting new opportunities for future antiviral drug discovery.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"3010-3021"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143530861","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}

{"title":"DeepMVD: A Novel Multiview Dynamic Feature Fusion Model for Accurate Protein Function Prediction.","authors":"Chaolin Song, Shiwen He, Yurong Qian, Xinhui Li, Yue Hu, Jiaying Chen, Jingfu Wang, Lei Deng","doi":"10.1021/acs.jcim.4c02216","DOIUrl":"10.1021/acs.jcim.4c02216","url":null,"abstract":"<p><p>Proteins, as the fundamental macromolecules of life, play critical roles in various biological processes. Recent advancements in intelligent protein function prediction methods leverage sequences, structures, and biomedical literature data. Among them, function prediction methods for protein sequences remain an enduring and popular research direction. Existing studies have failed to effectively utilize the multilevel attribute features reflected in protein sequences. This limitation hinders the enrichment of protein descriptions needed for high-precision prediction of protein functions. To address this, we propose DeepMVD, a novel deep learning model that enhances prediction accuracy by dynamically fusing multiview features. DeepMVD employs specialized modules to extract unique features from each view and utilizes an adaptive fusion mechanism for optimal integration. Evaluation of the CAFA4 data set shows that DeepMVD significantly outperforms existing state-of-the-art models in terms of BP, MF, and CC terminology, all obtaining the highest Fmax (0.523, 0.712, 0.740). Ablation studies confirm the model's robustness. Source code and data sets are available at http://swanhub.co/scl/DeepMVD.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"3077-3089"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575564","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}

{"title":"Cocry-pred: A Dynamic Resource Propagation Method for Cocrystal Prediction.","authors":"Wenxiang Song, Ren Peng, Hongbo Yu, Meiling Zhan, Guixia Liu, Weihua Li, Guobin Ren, Bin Zhu, Yun Tang","doi":"10.1021/acs.jcim.5c00179","DOIUrl":"10.1021/acs.jcim.5c00179","url":null,"abstract":"<p><p>Drug cocrystallization is a powerful strategy to enhance drug properties by modifying their physicochemical characteristics without altering their chemical structure. However, the identification of suitable coformers remains a challenging and resource-intensive task. To streamline this process, we developed a novel cocrystal prediction model, Cocry-pred, which utilizes the Network-Based Inference (NBI) algorithm─a dynamic resource propagation method─to recommend coformers for target molecules based on topological data from cocrystal network and molecular substructure information. We evaluated the impact of 13 types of molecular fingerprints and different numbers of propagation rounds on model performance. Additionally, to achieve optimal performance, we introduced three key hyperparameters─α (node weights), β (edge weights) and γ (penalty for high-degree nodes)─to balance the influence of various factors within the composite network. The best performance of Cocry-pred achieved an impressive AUC of 0.885 and an RS of 0.108. To validate the reliability of the model, we employed it to predict potential coformers for Apatinib. Subsequently, seven Apatinib cocrystals were then synthesized experimentally, among which single-crystal structures were obtained for two cocrystals. This advancement highlights the potential of Cocry-pred as a powerful tool, offering significant improvements in efficiency and providing valuable insights for cocrystal screening and design.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2868-2881"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603035","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}

{"title":"Extending the PARCH Scale: Assessing Hydropathy of Proteins across Multiple Water Models.","authors":"Xuyang Qin, Jingjing Ji, Somya Chakraborty, Shikha Nangia","doi":"10.1021/acs.jcim.4c02415","DOIUrl":"10.1021/acs.jcim.4c02415","url":null,"abstract":"<p><p>Quantitative assessment of amino acid hydropathy can be done using the protocol for assigning a residue's character on a hydropathy (PARCH) scale, which assigns values from 0 to 10, with lower values indicating greater hydrophobicity. The merit of the PARCH scale lies in its ability to integrate both the nanoscale topographical features and the chemical properties of amino acid residues when determining hydropathy. In its initial application, we employed the TIP3P water model, optimized for CHARMM36m proteins, to simulate the water behavior around the protein surface. Due to the growing use of the PARCH scale, we have extended its application to three additional all-atom water models: TIP4P, TIP4P-Ew, and TIP5P. Our findings reveal that although PARCH values vary across these water models, the relative hydropathy trends remain consistent. All models successfully distinguished hydrophobic from hydrophilic regions in nanoscale topography, although charged residues showed greater sensitivity to model choice, leading to more significant value variances. Additionally, we evaluated the influence of two other parameters─the force constant used to constrain proteins and the time step of the evaporation process─on the PARCH scale. Overall, the PARCH scale has demonstrated robustness in capturing protein hydropathy across various water models, suggesting its potential applicability with other protein-water force field combinations and even molecular systems beyond proteins.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2999-3009"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}