Journal of Chemical Information and Modeling 最新文献

{"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":"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}

{"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":"<p><p>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 (<b>s</b>ugar <b>w</b>ill <b>e</b>ventually be <b>e</b>xported <b>t</b>ransporter) 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.</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":"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}

{"title":"Fitting Atomic Structures into Cryo-EM Maps by Coupling Deep Learning-Enhanced Map Processing with Global-Local Optimization.","authors":"Yaxian Cai, Ziying Zhang, Xiangyu Xu, Liang Xu, Yu Chen, Guijun Zhang, Xiaogen Zhou","doi":"10.1021/acs.jcim.5c00004","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00004","url":null,"abstract":"<p><p>With the breakthroughs in protein structure prediction technology, constructing atomic structures from cryo-electron microscopy (cryo-EM) density maps through structural fitting has become increasingly critical. However, the accuracy of the constructed models heavily relies on the precision of the structure-to-map fitting. In this study, we introduce DEMO-EMfit, a progressive method that integrates deep learning-based backbone map extraction with a global-local structural pose search to fit atomic structures into density maps. DEMO-EMfit was extensively evaluated on a benchmark data set comprising both cryo-electron tomography (cryo-ET) and cryo-EM maps of protein and nucleic acid complexes. The results demonstrate that DEMO-EMfit outperforms state-of-the-art approaches, offering an efficient and accurate tool for fitting atomic structures into density maps.</p>","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":"143727027","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":"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":"<p><p>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 <i>R</i>-squared (<i>R</i>²) 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.</p>","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}

{"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":"<p><p>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.</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":"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}

{"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":"<p><p>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.</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":"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}

{"title":"MultiCTox: Empowering Accurate Cardiotoxicity Prediction through Adaptive Multimodal Learning.","authors":"Lin Feng, Xiangzheng Fu, Zhenya Du, Yuting Guo, Linlin Zhuo, Yan Yang, Dongsheng Cao, Xiaojun Yao","doi":"10.1021/acs.jcim.5c00022","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00022","url":null,"abstract":"<p><p>Cardiotoxicity refers to the inhibitory effects of drugs on cardiac ion channels. Accurate prediction of cardiotoxicity is crucial yet challenging, as it directly impacts the evaluation of cardiac drug efficacy and safety. Numerous methods have been developed to predict cardiotoxicity, yet their performance remains limited. A key limitation is that these methods often rely solely on single-modal data, making multimodal data integration challenging. As a result, we present a multimodal method integrating molecular SMILES, structure, and fingerprint to enhance cardiotoxicity prediction. First, we designed a fusion layer to unify representations from different modalities. During training, the model maximizes intramodal similarity for the same molecule while minimizing intermolecular similarity, ensuring consistent cross-modal representations. This study evaluates the inhibitory effects of candidate drugs on voltage-gated potassium (hERG), sodium (Nav1.5), and calcium (Cav1.2) channels. Experimental results demonstrate that the proposed model significantly outperforms existing state-of-the-art methods in cardiotoxicity prediction. We anticipate that this model will contribute significantly to the development and safety evaluation of cardiac drugs, reducing cardiotoxicity-related risks.</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":"143717620","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":"Structural Descriptors for Subunit Interface Regions in Homodimers: Effect of Lipid Membrane and Secondary Structure Type.","authors":"Aslı Yüksek, Batuhan Yıkınç, İrem Nayır, Defne Alnıgeniş, Vahap Gazi Fidan, Tayyip Topuz, Ebru Demet Akten","doi":"10.1021/acs.jcim.4c01233","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01233","url":null,"abstract":"<p><p>A total of 1311 homodimers were collected and analyzed in three different categories to highlight the impact of lipid environment and secondary structure type: 422 cytoplasmic α-helix, 411 cytoplasmic β-strand, and 478 membrane complexes. Structural features of the interface connecting two monomers were investigated and compared to those of the non-interface surface. Every residue on the surface of each monomer was explored based on four attributes: solvent-accessible surface area (SASA), protrusion index (C_<i>x</i>), surface planarity, and surface roughness. SASA and C_<i>x</i> distribution profiles clearly distinguished the interface from the surface in all categories, where the rim of the interface displayed higher SASA and C_<i>x</i> values than the rest of the surface. Surface residues in membrane complexes protruded less than cytoplasmic ones due to the hydrophobic environment, and consequently, the difference between surface and interface residues became less noticeable in that category. Cytoplasmic β-strand complexes displayed markedly lower SASA at the interface core than at the surface. The major distinction between the surface and interface was achieved through surface roughness, which displayed significantly higher values for the interface than the surface, especially in cytoplasmic complexes. Clearly, a surface which is relatively rugged favors the association of two monomers through multiple van der Waals interactions and hydrogen-bond formations. Another structural descriptor with strong distinguishing ability was surface planarity, which was higher at the interface than at the non-interface surface. Surface flatness would eventually facilitate the interconnectedness of an interface with a network of residue pairs bridging two complementary surfaces. Analysis of contact pairs revealed that hydrophobic pairs have the highest frequency of occurrence in the lipid environment of membrane complexes. However, despite the scarcity of polar residues at the interface, the likelihood of observing a contact between polar residues was markedly higher than that of hydrophobic ones.</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":"143717632","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":"Energetics of Expanded PAM Readability by Engineered Cas9-NG.","authors":"Shreya Bhattacharya, Priyadarshi Satpati","doi":"10.1021/acs.jcim.5c00011","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00011","url":null,"abstract":"<p><p>The energetic basis for the enhanced PAM (protospacer adjacent motif) readability in engineered Cas9-NG (a variant of Cas9 from <i>Streptococcus pyogenes</i> (<i>Sp</i>Cas9)) with seven mutations: (R1335V, E1219F, D1135V, L1111R, T1337R, G1218R, and A1322R) remains a fundamental unsolved problem. Utilizing the X-ray structure of the precatalytic complex (<i>Sp</i>Cas9:sgRNA:dsDNA) as a template, we calculated the changes in PAM (TGG, TGA, TGT, or TGC) binding affinity (ΔΔ<i>G</i>) associated with each of the seven mutations in <i>Sp</i>Cas9 through rigorous alchemical simulations (sampling ∼ 53 μs). The underlying thermodynamics (ΔΔ<i>G</i>) accounts for the experimentally observed differences in DNA cleavage activity between <i>Sp</i>Cas9 and Cas9-NG across various DNA substrates. The interaction energies between <i>Sp</i>Cas9 and DNA are significantly influenced by the type and location of the amino acid mutations. Notably, the R1335V mutation disfavors DNA binding by disrupting critical interactions with the PAM. However, the destabilizing effect of the R1335V mutation is mitigated by four advantageous mutations (E1219F, D1135V, L1111R, and T1337R), which primarily introduce nonbase-specific interactions and enhance PAM readability. The hydrophobic substitutions (E1219F and D1135V) are particularly impactful, as they exclude solvent from the PAM binding pocket, strengthening electrostatic interactions in the low dielectric medium and increasing the stability of the noncognate PAM complexes by ∼2-5 kcal/mol. Additionally, L1111R and T1337R facilitate DNA binding by forming direct electrostatic contacts. In contrast, the charge mutations G1218R and A1322R do not effectively promote interactions with the negatively charged DNA, clearly demonstrating that the location of mutations is crucial in shaping these interaction energetics. We demonstrated that stabilization of the Cas9-NG: noncognate PAM complexes enables broader PAM recognition. This is primarily achieved through two mechanisms: (1) the establishment of new nonbase-specific interactions between the protein and nucleotides and (2) the enhancement of electrostatic interactions within a relatively dry and hydrophobic pocket. The findings revealed that mutation-induced desolvation can improve the recognition of noncognate PAMs, paving the way for the rational and innovative design of <i>Sp</i>Cas9 mutants.</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":"143717614","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}