Journal of Chemical Information and Modeling 最新文献

{"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}

{"title":"Some Open Mathematical Problems on Fullerenes.","authors":"Artur Bille, Victor Buchstaber, Evgeny Spodarev","doi":"10.1021/acs.jcim.4c01997","DOIUrl":"10.1021/acs.jcim.4c01997","url":null,"abstract":"<p><p>Fullerenes are hollow carbon molecules where each atom is connected to exactly three other atoms, arranged in pentagonal and hexagonal rings. Mathematically, they can be combinatorially modeled as planar, 3-regular graphs with facets composed only of pentagons and hexagons. In this work, we outline a few of the many open questions about fullerenes, beginning with the problem of generating fullerenes randomly. We then introduce an infinite family of fullerenes on which the generalized Stone-Wales operation is inapplicable. Furthermore, we present numerical insights into a graph invariant, called the <i>character</i> of a fullerene, derived from its adjacency and degree matrices. As supported by numerical results, this descriptor may lead to a new method for linear enumeration of all fullerenes.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2911-2923"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595805","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}

{"title":"Optimized Protein-Excipient Interactions in the Martini 3 Force Field.","authors":"Tobias M Prass, Kresten Lindorff-Larsen, Patrick Garidel, Michaela Blech, Lars V Schäfer","doi":"10.1021/acs.jcim.4c02338","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02338","url":null,"abstract":"<p><p>The high doses of drugs required for biotherapeutics, such as monoclonal antibodies (mAbs), and the small volumes that can be administered to patients by subcutaneous injections pose challenges due to high-concentration formulations. The addition of excipients, such as arginine and glutamate, to high-concentration protein formulations can increase solubility and reduce the tendency of protein particle formation. Molecular dynamics (MD) simulations can provide microscopic insights into the mode of action of excipients in mAb formulations but require large system sizes and long time scales that are currently beyond reach at the fully atomistic level. Computationally efficient coarse-grained models such as the Martini 3 force field can tackle this challenge but require careful parametrization, testing, and validation. This study extends the popular Martini 3 force field toward realistic protein-excipient interactions of arginine and glutamate excipients, using the Fab domains of the therapeutic mAbs trastuzumab and omalizumab as model systems. A novel all-atom to coarse-grained mapping of the amino acid excipients is introduced, which explicitly captures the zwitterionic character of the backbone. The Fab-excipient interactions of arginine and glutamate are characterized concerning molecular contacts with the Fabs at the single-residue level. The Martini 3 simulations are compared with results from all-atom simulations as a reference. Our findings reveal an overestimation of Fab-excipient contacts with the default interaction parameters of Martini 3, suggesting a too strong attraction between protein residues and excipients. Therefore, we reparametrized the protein-excipient interaction parameters in Martini 3 against all-atom simulations. The excipient interactions obtained with the new Martini 3 mapping and Lennard-Jones (LJ) interaction parameters, coined Martini 3-exc, agree closely with the all-atom reference data. This work presents an improved parameter set for mAb-arginine and mAb-glutamate interactions in the Martini 3 coarse-grained force field, a key step toward large-scale coarse-grained MD simulations of high-concentration mAb formulations and the stabilizing effects of excipients.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699097","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":"CMDmpnn: Combining Comparative Molecular Dynamics and ProteinMPNN to Rapidly Expand Enzyme Substrate Spectrum.","authors":"Chuan-Qi Sun, Zhi-Min Li, Yu Ji, Ulrich Schwaneberg, Zong-Lin Li","doi":"10.1021/acs.jcim.5c00117","DOIUrl":"10.1021/acs.jcim.5c00117","url":null,"abstract":"<p><p>Expanding enzyme substrate spectra enhances industrial applications and drives sustainable biocatalysis. Despite advances, challenges in modification efficiency and high-throughput screening persist. Here, we developed a virtual screening method called CMDmpnn that combines comparative molecular dynamics (MD) simulations and ProteinMPNN to broaden enzyme substrate spectra without compromising other industrially important properties of enzymes, such as thermostability. Using glycosyltransferase as a model, we first established a dynamic model library of the wild-type enzyme through MD simulations and performed clustering. Subsequently, we utilized ProteinMPNN to generate a comprehensive set of new sequences for the entire library, enabling rapid identification of all possible enzyme variants. Short MD simulations were then conducted on variant-substrate complex models, with results compared to those of the wild-type enzyme. By analyzing catalytically relevant information such as substrate binding modes and key atomic distances, we identified multiple variants capable of catalyzing a broad spectrum of phenolic compounds, all within a timeframe of less than 2 weeks. The CMDmpnn method offers a powerful and efficient tool for rapidly expanding enzyme substrate spectra.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2741-2747"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603034","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":"LLM-Driven Synthesis Planning for Quantum Dot Materials Development.","authors":"So Eun Choi, MiYoung Jang, SoHee Yoon, SangHyun Yoo, Jooyeon Ahn, Minho Kim, Ho-Gyeong Kim, Yebin Jung, Seongeon Park, Young-Seok Kim, Taekhoon Kim","doi":"10.1021/acs.jcim.4c01529","DOIUrl":"10.1021/acs.jcim.4c01529","url":null,"abstract":"<p><p>The application of large language models in materials science has opened new avenues for accelerating materials development. Building on this advancement, we propose a novel framework leveraging large language models to optimize experimental procedures for synthesizing quantum dot materials with multiple desired properties. Our framework integrates the synthesis protocol generation model and the property prediction model, both fine-tuned on open-source large language models using parameter-efficient training techniques with in-house synthesis protocol data. Once the synthesis protocol with target properties and a masked reference protocol is generated, it undergoes validation through the property prediction models, followed by assessments of its novelty and human evaluation. Our synthesis experiments demonstrate that among the six synthesis protocols derived from the entire framework, three successfully update the Pareto front, and all six improve at least one property. Through empirical validation, we confirm the effectiveness of our fine-tuned large language model-driven framework for synthesis planning, showcasing strong performance under multitarget optimization.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2748-2758"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603039","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}