Journal of Chemical Information and Modeling 最新文献

{"title":"Constrained Structure Minimizations on Hyperspheres for Minimum Energy Path Following","authors":"Jorge Alberto Sanchez Alvarez*,&nbsp;Luis López-Sosa,&nbsp;Andreas M. Köster and Patrizia Calaminici*,&nbsp;","doi":"10.1021/acs.jcim.4c0235110.1021/acs.jcim.4c02351","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02351https://doi.org/10.1021/acs.jcim.4c02351","url":null,"abstract":"<p >In this work, a reliable and robust trust region method for restricted minimizations on hyperspheres is developed. The working equations of this new methodology are presented, together with their validation. The performance and characteristics of this new algorithm are discussed by a constrained minimization on a sphere using a two-dimensional Quapp model surface. The obtained results show that the proposed method for minimizations on hyperspheres guarantees convergence to constrained minima. Its enhanced numerical stability permits tight convergence criteria for constrained minimizations. The application of the new restricted minimizer in the framework of the hierarchical transition state finder and for the calculation of intrinsic reaction coordinates for 38 chemical reactions demonstrates its robustness and efficiency.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 7","pages":"3488–3501 3488–3501"},"PeriodicalIF":5.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.jcim.4c02351","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825109","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":"Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?","authors":"Eric A. Chen,&nbsp; and ,&nbsp;Yingkai Zhang*,&nbsp;","doi":"10.1021/acs.jcim.5c0033110.1021/acs.jcim.5c00331","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00331https://doi.org/10.1021/acs.jcim.5c00331","url":null,"abstract":"<p >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.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 7","pages":"3737–3748 3737–3748"},"PeriodicalIF":5.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.jcim.5c00331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825015","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":"Constrained Structure Minimizations on Hyperspheres for Minimum Energy Path Following.","authors":"Jorge Alberto Sanchez Alvarez, Luis López-Sosa, Andreas M Köster, Patrizia Calaminici","doi":"10.1021/acs.jcim.4c02351","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02351","url":null,"abstract":"<p><p>In this work, a reliable and robust trust region method for restricted minimizations on hyperspheres is developed. The working equations of this new methodology are presented, together with their validation. The performance and characteristics of this new algorithm are discussed by a constrained minimization on a sphere using a two-dimensional Quapp model surface. The obtained results show that the proposed method for minimizations on hyperspheres guarantees convergence to constrained minima. Its enhanced numerical stability permits tight convergence criteria for constrained minimizations. The application of the new restricted minimizer in the framework of the hierarchical transition state finder and for the calculation of intrinsic reaction coordinates for 38 chemical reactions demonstrates its robustness and efficiency.</p>","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":"143762501","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":"Drug-Target Affinity Prediction Based on Topological Enhanced Graph Neural Networks.","authors":"Hengliang Guo, Congxiang Zhang, Jiandong Shang, Dujuan Zhang, Yang Guo, Kang Gao, Kecheng Yang, Xu Gao, Dezhong Yao, Wanting Chen, Mengfan Yan, Gang Wu","doi":"10.1021/acs.jcim.4c01335","DOIUrl":"10.1021/acs.jcim.4c01335","url":null,"abstract":"<p><p>Graph neural networks (GNNs) have achieved remarkable success in drug-target affinity (DTA) analysis, reducing the cost of drug development. Unlike traditional one-dimensional (1D) sequence-based methods, GNNs leverage graph structures to capture richer protein and drug features, leading to improved DTA prediction performance. However, existing methods often neglect to incorporate valuable protein cavity information, a key aspect of protein physical chemistry. This study addresses this gap by proposing a novel topology-enhanced GNN for DTA prediction that integrates protein pocket data. Additionally, we optimize training and message-passing strategies to enhance the model's feature representation capabilities. Our model's effectiveness is validated on the Davis and KIBA data sets, demonstrating its ability to capture the intricate interplay between drugs and targets. The source code is publicly available on https://github.com/ZZDXgangwu/DTA.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750193","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":"Estimation of Absolute Binding Free Energies for Drugs That Bind Multiple Proteins.","authors":"Erik Lindahl, Ran Friedman","doi":"10.1021/acs.jcim.4c01555","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01555","url":null,"abstract":"<p><p>The Gibbs energy of binding (absolute binding free energy, ABFE) of a drug to proteins in the body determines the drug's affinity to its molecular target and its selectivity. ABFE is challenging to measure, and experimental values are not available for many proteins together with potential drugs and other molecules that bind them. Accurate means of calculating such values are, therefore, highly in demand. Realizing that toxicity and side effects are closely related to off-target binding, here we calculate the ABFE of two drugs, each to multiple proteins, in order to examine whether it is possible to carry out such calculations and achieve the required accuracy. The methods that were used were free energy perturbation with replica exchange molecular dynamics (FEP/REMD) and density functional theory (DFT) with a cluster approach and a simplified model. DFT calculations were supplemented with energy decomposition analysis (EDA). The accuracy of each method is discussed, and suggestions are made for the approach toward better ABFE calculations.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750258","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":"Estimation of Absolute Binding Free Energies for Drugs That Bind Multiple Proteins","authors":"Erik Lindahl,&nbsp; and ,&nbsp;Ran Friedman*,&nbsp;","doi":"10.1021/acs.jcim.4c0155510.1021/acs.jcim.4c01555","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01555https://doi.org/10.1021/acs.jcim.4c01555","url":null,"abstract":"<p >The Gibbs energy of binding (absolute binding free energy, ABFE) of a drug to proteins in the body determines the drug’s affinity to its molecular target and its selectivity. ABFE is challenging to measure, and experimental values are not available for many proteins together with potential drugs and other molecules that bind them. Accurate means of calculating such values are, therefore, highly in demand. Realizing that toxicity and side effects are closely related to off-target binding, here we calculate the ABFE of two drugs, each to multiple proteins, in order to examine whether it is possible to carry out such calculations and achieve the required accuracy. The methods that were used were free energy perturbation with replica exchange molecular dynamics (FEP/REMD) and density functional theory (DFT) with a cluster approach and a simplified model. DFT calculations were supplemented with energy decomposition analysis (EDA). The accuracy of each method is discussed, and suggestions are made for the approach toward better ABFE calculations.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 7","pages":"3431–3438 3431–3438"},"PeriodicalIF":5.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.jcim.4c01555","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825301","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":"Drug-Target Affinity Prediction Based on Topological Enhanced Graph Neural Networks","authors":"Hengliang Guo,&nbsp;Congxiang Zhang,&nbsp;Jiandong Shang,&nbsp;Dujuan Zhang,&nbsp;Yang Guo,&nbsp;Kang Gao,&nbsp;Kecheng Yang,&nbsp;Xu Gao,&nbsp;Dezhong Yao,&nbsp;Wanting Chen,&nbsp;Mengfan Yan and Gang Wu*,&nbsp;","doi":"10.1021/acs.jcim.4c0133510.1021/acs.jcim.4c01335","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01335https://doi.org/10.1021/acs.jcim.4c01335","url":null,"abstract":"<p >Graph neural networks (GNNs) have achieved remarkable success in drug-target affinity (DTA) analysis, reducing the cost of drug development. Unlike traditional one-dimensional (1D) sequence-based methods, GNNs leverage graph structures to capture richer protein and drug features, leading to improved DTA prediction performance. However, existing methods often neglect to incorporate valuable protein cavity information, a key aspect of protein physical chemistry. This study addresses this gap by proposing a novel topology-enhanced GNN for DTA prediction that integrates protein pocket data. Additionally, we optimize training and message-passing strategies to enhance the model’s feature representation capabilities. Our model’s effectiveness is validated on the Davis and KIBA data sets, demonstrating its ability to capture the intricate interplay between drugs and targets. The source code is publicly available on https://github.com/ZZDXgangwu/DTA.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 7","pages":"3749–3760 3749–3760"},"PeriodicalIF":5.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825079","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":"Critical Assessment of RNA and DNA Structure Predictions via Artificial Intelligence: The Imitation Game.","authors":"Christina Bergonzo, Alexander Grishaev","doi":"10.1021/acs.jcim.5c00245","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00245","url":null,"abstract":"<p><p>Computational predictions of biomolecular structure via artificial intelligence (AI) based approaches, as exemplified by AlphaFold software, have the potential to model of all life's biomolecules. We performed oligonucleotide structure prediction and gauged the accuracy of the AI-generated models via their agreement with experimental solution-state observables. We find parts of these models in good agreement with experimental data, and others falling short of the ground truth. The latter include internal or capping loops, noncanonical base pairings, and regions involving conformational flexibility, all essential for RNA folding, interactions, and function. We estimate root-mean-square (r.m.s.) errors in predicted nucleotide bond vector orientations ranging between 7° and 30°, with higher accuracies for simpler architectures of individual canonically paired helical stems. These mixed results highlight the necessity of experimental validation of AI-based oligonucleotide model predictions and their current tendency to mimic the training data set rather than reproduce the underlying reality.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750190","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":"Critical Assessment of RNA and DNA Structure Predictions via Artificial Intelligence: The Imitation Game","authors":"Christina Bergonzo*,&nbsp; and ,&nbsp;Alexander Grishaev*,&nbsp;","doi":"10.1021/acs.jcim.5c0024510.1021/acs.jcim.5c00245","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00245https://doi.org/10.1021/acs.jcim.5c00245","url":null,"abstract":"<p >Computational predictions of biomolecular structure via artificial intelligence (AI) based approaches, as exemplified by AlphaFold software, have the potential to model of all life’s biomolecules. We performed oligonucleotide structure prediction and gauged the accuracy of the AI-generated models via their agreement with experimental solution-state observables. We find parts of these models in good agreement with experimental data, and others falling short of the ground truth. The latter include internal or capping loops, noncanonical base pairings, and regions involving conformational flexibility, all essential for RNA folding, interactions, and function. We estimate root-mean-square (r.m.s.) errors in predicted nucleotide bond vector orientations ranging between 7° and 30°, with higher accuracies for simpler architectures of individual canonically paired helical stems. These mixed results highlight the necessity of experimental validation of AI-based oligonucleotide model predictions and their current tendency to mimic the training data set rather than reproduce the underlying reality.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 7","pages":"3544–3554 3544–3554"},"PeriodicalIF":5.6,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.jcim.5c00245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825337","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":"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":"<p><p>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.</p>","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}