Journal of Chemical Information and Modeling 最新文献

Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-04-01 DOI: 10.1021/acs.jcim.5c00331

Eric A Chen, Yingkai Zhang

{"title":"Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?","authors":"Eric A Chen, Yingkai Zhang","doi":"10.1021/acs.jcim.5c00331","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00331","url":null,"abstract":"Allosteric compounds offer an alternative mode of inhibition to orthosteric compounds with opportunities for selectivity and noncompetition. Structure-based drug design (SBDD) of allosteric compounds introduces complications compared to their orthosteric counterparts; multiple binding sites of interest are considered, and often allosteric binding is only observed in particular protein conformations. Blind docking methods show potential in virtual screening allosteric ligands, and deep learning methods, such as DiffDock, achieve state-of-the-art performance on protein-ligand complex prediction benchmarks compared to traditional docking methods such as Vina and Lin_F9. To this aim, we explore the utility of a data-driven platform called the minimum distance matrix representation (MDMR) to retrospectively predict recently discovered allosteric inhibitors complexed with Cyclin-Dependent Kinase (CDK) 2. In contrast to other protein complex representations, it uses the minimum residue-residue (or residue-ligand) distance as a feature that prioritizes the formation of interactions. Analysis of this representation highlights the variety of protein conformations and ligand binding modes, and we identify an intermediate protein conformation that other heuristic-based kinase conformation classification methods do not distinguish. Next, we design self- and cross-docking benchmarks to assess whether docking methods can predict both orthosteric and allosteric binding modes and if prospective success is conditional on the selection of the protein receptor conformation, respectively. We find that a combined method, DiffDock followed by Lin_F9 Local Re-Docking (DiffDock + LRD), can predict both orthosteric and allosteric binding modes, and the intermediate conformation must be selected to predict the allosteric pose. In summary, this work highlights the value of a data-driven method to explore protein conformations and ligand binding modes and outlines the challenges of SBDD of allosteric compounds.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750137","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

Estimation of Absolute Binding Free Energies for Drugs That Bind Multiple Proteins.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-31 DOI: 10.1021/acs.jcim.4c01555

Erik Lindahl, Ran Friedman

引用次数: 0

Drug-Target Affinity Prediction Based on Topological Enhanced Graph Neural Networks.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-30 DOI: 10.1021/acs.jcim.4c01335

Hengliang Guo, Congxiang Zhang, Jiandong Shang, Dujuan Zhang, Yang Guo, Kang Gao, Kecheng Yang, Xu Gao, Dezhong Yao, Wanting Chen, Mengfan Yan, Gang Wu

引用次数: 0

Critical Assessment of RNA and DNA Structure Predictions via Artificial Intelligence: The Imitation Game.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-30 DOI: 10.1021/acs.jcim.5c00245

Christina Bergonzo, Alexander Grishaev

引用次数: 0

Optimizing On-the-Fly Probability Enhanced Sampling for Complex RNA Systems: Sampling Free Energy Surfaces of an H-Type Pseudoknot.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-29 DOI: 10.1021/acs.jcim.4c02235

Karim Malekzadeh, Gül H Zerze

{"title":"Optimizing On-the-Fly Probability Enhanced Sampling for Complex RNA Systems: Sampling Free Energy Surfaces of an H-Type Pseudoknot.","authors":"Karim Malekzadeh, Gül H Zerze","doi":"10.1021/acs.jcim.4c02235","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02235","url":null,"abstract":"All-atom molecular dynamics (MD) simulations offer crucial insights into biomolecular dynamics, but inherent time scale constraints often limit their effectiveness. Advanced sampling techniques help overcome these limitations, enabling predictions of deeply rugged folding free energy surfaces (FES) of RNA at atomistic resolution. The Multithermal-Multiumbrella On-the-Fly Probability Enhanced Sampling (MM-OPES) method, which combines temperature and collective variables (CVs) to accelerate sampling, has shown promise and cost-effectiveness. However, the applications have so far been limited to simpler RNA systems, such as stem-loops. In this study, we optimized the MM-OPES method to explore the FES of an H-type RNA pseudoknot, a more complex fundamental RNA folding unit. Through systematic exploration of CV combinations and temperature ranges, we identified an optimal strategy for both sampling and analysis. Our findings demonstrate that treating the native-like contacts in two stems as independent CVs and using a temperature range of 300-480 K provides the most effective sampling, while projections onto native Watson-Crick-type hydrogen bond CVs yield the best resolution FES prediction. Additionally, our sampling scheme also revealed various folding/unfolding pathways. This study provides practical insights and detailed decision-making strategies for adopting the MM-OPES method, facilitating its application to complex RNA structures at atomistic resolution.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741724","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

SWEET Family Transporters Act as Water-Conducting Carrier Proteins in Plants.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-29 DOI: 10.1021/acs.jcim.5c00110

Balaji Selvam, Arnav Paul, Ya-Chi Yu, Li-Qing Chen, Diwakar Shukla

{"title":"SWEET Family Transporters Act as Water-Conducting Carrier Proteins in Plants.","authors":"Balaji Selvam, Arnav Paul, Ya-Chi Yu, Li-Qing Chen, Diwakar Shukla","doi":"10.1021/acs.jcim.5c00110","DOIUrl":"10.1021/acs.jcim.5c00110","url":null,"abstract":"Dedicated water channels are involved in the facilitated diffusion of water molecules across cell membranes in plants. Transporter proteins are also known to transport water molecules along with substrates; however, the molecular mechanism of water permeation is not well understood in plant transporters. Here, we show that plant sugar transporters from the SWEET (sugar will eventually be exported transporter) family act as water-conducting carrier proteins via a variety of passive and active mechanisms that allow the diffusion of water molecules from one side of the membrane to the other. This study provides a molecular perspective on how plant membrane transporters act as water carrier proteins, a topic that has not been extensively explored in the literature. Water permeation in membrane transporters could occur via four distinct mechanisms, which form our hypothesis for water transport in SWEETs. These hypotheses are tested using molecular dynamics simulations of the outward-facing, occluded, and inward-facing states of AtSWEET1 to identify the water permeation pathways and the flux associated with them. The hydrophobic gates at the center of the transport tunnel act as barriers that restrict water permeation. We have performed in silico single and double mutations of the hydrophobic gate residues to examine the changes in water conductivity. Surprisingly, the double mutant allows water permeation to the intracellular half of the membrane and forms a continuous water channel. These computational results are validated by experimentally examining the transport of hydrogen peroxide molecules by the AtSWEET family of transporters. We have also shown that the transport of hydrogen peroxide follows a mechanism similar to that of water transport in AtSWEET1. Finally, we conclude that similar water-conduction states are also present in other SWEETs due to the high degree of sequence and structural conservation exhibited by this transporter family.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741726","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

Fitting Atomic Structures into Cryo-EM Maps by Coupling Deep Learning-Enhanced Map Processing with Global-Local Optimization.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-28 DOI: 10.1021/acs.jcim.5c00004

Yaxian Cai, Ziying Zhang, Xiangyu Xu, Liang Xu, Yu Chen, Guijun Zhang, Xiaogen Zhou

引用次数: 0

Prediction of Chromatographic Retention Time of a Small Molecule from SMILES Representation Using a Hybrid Transformer-LSTM Model.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-28 DOI: 10.1021/acs.jcim.5c00167

Sargol Mazraedoost, Hadi Sedigh Malekroodi, Petar Žuvela, Myunggi Yi, J Jay Liu

{"title":"Prediction of Chromatographic Retention Time of a Small Molecule from SMILES Representation Using a Hybrid Transformer-LSTM Model.","authors":"Sargol Mazraedoost, Hadi Sedigh Malekroodi, Petar Žuvela, Myunggi Yi, J Jay Liu","doi":"10.1021/acs.jcim.5c00167","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00167","url":null,"abstract":"Accurate retention time (RT) prediction in liquid chromatography remains a significant consideration in molecular analysis. In this study, we explore the use of a transformer-based language model to predict RTs by treating simplified molecular input line entry system (SMILES) sequences as textual input, an approach that has not been previously utilized in this field. Our architecture combines a pretrained RoBERTa (robustly optimized BERT approach, a variant of BERT) with bidirectional long short-term memory (BiLSTM) networks to predict retention times in reversed-phase high-performance liquid chromatography (RP-HPLC). The METLIN small molecule retention time (SMRT) data set comprising 77,980 small molecules after preprocessing, was encoded using SMILES notation and processed through a tokenizer to enable molecular representation as sequential data. The proposed transformer-LSTM architecture incorporates layer fusion from multiple transformer layers and bidirectional sequence processing, achieving superior performance compared to existing methods with a mean absolute error (MAE) of 26.23 s, a mean absolute percentage error (MAPE) of 3.25%, and R-squared (R2) value of 0.91. The model's explainability was demonstrated through attention visualization, revealing its focus on key molecular features that can influence RT. Furthermore, we evaluated the model's transfer learning capabilities across ten data sets from the PredRet database, demonstrating robust performance across different chromatographic conditions with consistent improvement over previous approaches. Our results suggest that the hybrid model presents a valuable approach for predicting RT in liquid chromatography, with potential applications in metabolomics and small molecule analysis.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143727032","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

Recent Advances in the Modeling of Ionic Liquids Using Artificial Neural Networks.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-27 DOI: 10.1021/acs.jcim.4c02364

Adrian Racki, Kamil Paduszyński

{"title":"Recent Advances in the Modeling of Ionic Liquids Using Artificial Neural Networks.","authors":"Adrian Racki, Kamil Paduszyński","doi":"10.1021/acs.jcim.4c02364","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02364","url":null,"abstract":"This paper reviews the recent and most impactful advancements in the application of artificial neural networks in modeling the properties of ionic liquids. As salts that are liquid at temperatures below 100 °C, ionic liquids possess unique properties beneficial for various industrial applications such as carbon capture, catalytic solvents, and lubricant additives. The study emphasizes the challenges in selecting appropriate ILs due to the vast variability in their properties, which depend significantly on their cation and anion structures. The review discusses the advantages of using ANNs, including feed-forward, cascade-forward, convolutional, recurrent, and graph neural networks, over traditional machine learning algorithms for predicting the thermodynamic and physical properties of ILs. The paper also highlights the importance of data preparation, including data collection, feature engineering, and data cleaning, in developing accurate predictive models. Additionally, the review covers the interpretability of these models using techniques such as SHapley Additive exPlanations to understand feature importance. The authors conclude by discussing future opportunities and the potential of combining ANNs with other computational methods to design new ILs with targeted properties.","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":"143717627","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

Stacking Interactions of Druglike Heterocycles with Nucleobases.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-03-27 DOI: 10.1021/acs.jcim.4c02420

Audrey V Conner, Lauren M Kim, Patrick A Fagan, Drew P Harding, Steven E Wheeler

{"title":"Stacking Interactions of Druglike Heterocycles with Nucleobases.","authors":"Audrey V Conner, Lauren M Kim, Patrick A Fagan, Drew P Harding, Steven E Wheeler","doi":"10.1021/acs.jcim.4c02420","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02420","url":null,"abstract":"Stacking interactions contribute significantly to the interaction of small molecules with RNA, and harnessing the power of these interactions will likely prove important in the development of RNA-targeting inhibitors. To this end, we present a comprehensive computational analysis of stacking interactions between a set of 54 druglike heterocycles and the natural nucleobases. We first show that heterocycle choice can tune the strength of stacking interactions with nucleobases over a large range and that heterocycles favor stacked geometries that cluster around a discrete set of stacking loci characteristic of each nucleobase. Symmetry-adapted perturbation theory results indicate that the strengths of these interactions are modulated primarily by electrostatic and dispersion effects. Based on this, we present a multivariate predictive model of the maximum strength of stacking interactions between a given heterocycle and nucleobase that depends on molecular descriptors derived from the electrostatic potential. These descriptors can be readily computed using density functional theory or predicted directly from atom connectivity (e.g., SMILES). This model is used to predict the maximum possible stacking interactions of a set of 1854 druglike heterocycles with the natural nucleobases. Finally, we show that trivial modifications of standard (fixed-charge) molecular mechanics force fields reduce errors in predicted stacking interaction energies from around 2 kcal/mol to below 1 kcal/mol, providing a pragmatic means of predicting more reliable stacking interaction energies using existing computational workflows. We also analyze the stacking interactions between ribocil and a bacterial riboswitch, showing that two of the three aromatic heterocyclic components engage in near-optimal stacking interactions with binding site nucleobases.","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":"143727083","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