Journal of Chemical Information and Modeling 最新文献

{"title":"Ranking Single Fluorescent Protein-Based Calcium Biosensor Performance by Molecular Dynamics Simulations.","authors":"Melike Berksoz, Canan Atilgan","doi":"10.1021/acs.jcim.4c01478","DOIUrl":"10.1021/acs.jcim.4c01478","url":null,"abstract":"<p><p>Genetically encoded fluorescent biosensors (GEFBs) have become indispensable tools for visualizing biological processes <i>in vivo.</i> A typical GEFB is composed of a sensory domain (SD) that undergoes a conformational change upon ligand binding or enzymatic reaction; the SD is genetically fused with a fluorescent protein (FP). The changes in the SD allosterically modulate the chromophore environment whose spectral properties are changed. Single fluorescent (FP)-based biosensors, a subclass of GEFBs, offer a simple experimental setup; they are easy to produce in living cells, structurally stable, and simple to use due to their single-wavelength operation. However, they pose a significant challenge for structure optimization, especially concerning the length and residue content of linkers between the FP and SD, which affect how well the chromophore responds to conformational change in the SD. In this work, we use all-atom molecular dynamics simulations to analyze the dynamic properties of a series of calmodulin-based calcium biosensors, all with different FP-SD interaction interfaces and varying degrees of calcium binding-dependent fluorescence change. Our results indicate that biosensor performance can be predicted based on distribution of water molecules around the chromophore and shifts in hydrogen bond occupancies between the ligand-bound and ligand-free sensor structures.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"338-350"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733952/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890673","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":"Interpretable Deep-Learning p<i>K</i>_a Prediction for Small Molecule Drugs via Atomic Sensitivity Analysis.","authors":"Joseph DeCorte, Benjamin Brown, Rathmell Jeffrey, Jens Meiler","doi":"10.1021/acs.jcim.4c01472","DOIUrl":"10.1021/acs.jcim.4c01472","url":null,"abstract":"<p><p>Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociation constant (p<i>K</i>_a). Despite recent architectural advances, these models often generalize poorly to novel compounds due to a scarcity of ground-truth data. Further, these models lack interpretability. To this end, with deliberate molecular embeddings, atomic-resolution information is accessible in chemical structures by observing the model response to atomic perturbations of an input molecule. Here, we present BCL-XpKa, a deep neural network (DNN)-based multitask classifier for p<i>K</i>_a prediction that encodes local atomic environments through Mol2D descriptors. BCL-XpKa outputs a discrete distribution for each molecule, which stores the p<i>K</i>_a prediction and the model's uncertainty for that molecule. BCL-XpKa generalizes well to novel small molecules. BCL-XpKa performs competitively with modern ML p<i>K</i>_a predictors, outperforms several models in generalization tasks, and accurately models the effects of common molecular modifications on a molecule's ionizability. We then leverage BCL-XpKa's granular descriptor set and distribution-centered output through atomic sensitivity analysis (ASA), which decomposes a molecule's predicted p<i>K</i>_a value into its respective atomic contributions without model retraining. ASA reveals that BCL-XpKa has implicitly learned high-resolution information about molecular substructures. We further demonstrate ASA's utility in structure preparation for protein-ligand docking by identifying ionization sites in 93.2% and 87.8% of complex small molecule acids and bases. We then applied ASA with BCL-XpKa to identify and optimize the physicochemical liabilities of a recently published KRAS-degrading PROTAC.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 1","pages":"101-113"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968671","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":"SynPlanner: An End-to-End Tool for Synthesis Planning.","authors":"Tagir Akhmetshin, Dmitry Zankov, Philippe Gantzer, Dmitry Babadeev, Anna Pinigina, Timur Madzhidov, Alexandre Varnek","doi":"10.1021/acs.jcim.4c02004","DOIUrl":"10.1021/acs.jcim.4c02004","url":null,"abstract":"<p><p>SynPlanner is an end-to-end tool for designing customized retrosynthetic planners from reaction data. It includes a reaction data curation pipeline (reaction atom-to-atom mapping, reaction standardization, and filtration), reaction rule extraction, retrosynthetic model training, and retrosynthetic planning. The tool is designed to be as flexible as possible, supporting the customization of each step of the pipeline to address different needs in the development of customized retrosynthetic planning solutions. The retrosynthetic planning in SynPlanner is performed by Monte Carlo Tree Search (MCTS) guided by graph neural networks for node expansion (retrosynthetic rule predictions) and evaluation (precursor synthesizability prediction). The solution can be accessed by a simple graphical user interface and a command line interface and is accompanied by a collection of tutorials. SynPlanner is available on GitHub at https://github.com/Laboratoire-de-Chemoinformatique/SynPlanner.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"15-21"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142908805","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":"Fatty Alcohol Membrane Model for Quantifying and Predicting Amphiphilicity.","authors":"Nur Afiqah Ahmad, Junming Ho","doi":"10.1021/acs.jcim.4c01615","DOIUrl":"10.1021/acs.jcim.4c01615","url":null,"abstract":"<p><p>Amphiphilicity is an important property for drug development and self-assembly. This paper introduces a general approach based on a simple fatty alcohol (dodecanol) membrane model that can be used to quantify the amphiphilicity of small molecules that are in good agreement with experimental surface tension data. By applying the model to a systematic series of compounds, it was possible to elucidate the effect of different motifs on amphiphilicity. The results further indicate that amphiphilicity correlates strongly with water-octanol partition coefficients (log<i>P</i>) for the 29 organic molecules examined in the 0 < log<i>P <</i> 4 range. Importantly, the simulation of the model membrane is an order of magnitude faster than a phospholipid membrane (e.g., 1-palmitoyl-2-oleoyl-<i>sn</i>-glycero-3-phosphocholine) simulation and offers a simple atomistic approach for quantifying and predicting amphiphilicity of small drug-like molecules that could be used in quantitative structure-activity relationship studies.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"417-426"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862523","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":"Leveraging the Thermodynamics of Protein Conformations in Drug Discovery.","authors":"Bin W Zhang, Mikolai Fajer, Wei Chen, Francesca Moraca, Lingle Wang","doi":"10.1021/acs.jcim.4c01612","DOIUrl":"10.1021/acs.jcim.4c01612","url":null,"abstract":"<p><p>As the name implies, structure-based drug design requires confidence in the holo complex structure. The ability to clarify which protein conformation to use when ambiguity arises would be incredibly useful. We present a large scale validation of the computational method Protein Reorganization Free Energy Perturbation (PReorg-FEP) and demonstrate its quantitative accuracy in selecting the correct protein conformation among candidate models in apo or ligand induced states for 14 different systems. These candidate conformations are pulled from various drug discovery related campaigns: cryptic conformations induced by novel hits in lead identification, binding site rearrangement during lead optimization, and conflicting structural biology models. We also show an example of a pH-dependent conformational change, relevant to protein design.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"252-264"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142833082","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":"Ligand Binding and Functional Effect of Novel Bicyclic α5 GABA_A Receptor Negative Allosteric Modulators.","authors":"Balázs Krámos, Zoltán Béni, György István Túrós, Olivér Éliás, Attila Potor, Gábor László Kapus, György Szabó","doi":"10.1021/acs.jcim.4c01431","DOIUrl":"10.1021/acs.jcim.4c01431","url":null,"abstract":"<p><p>The significant importance of GABA_A receptors in the treatment of central nervous system (CNS) disorders has been known for a long time. However, only in recent years have experimental protein structures been published that can open the door to understanding protein-ligand interactions and may effectively help the rational drug design for the future. In our previous work (Szabó, G. <i>J. Med. Chem.</i> 2022, 65(11), 7876), where a promising selective α5-GABA_A negative allosteric modulator (NAM) was developed containing the 3-(4-fluorophenyl)-5-methyl-1,2-oxazole headgroup, we noticed a switch-like effect of a single nitrogen atom for the receptor function in some derivatives having a dihydro-naphthyridinone or dihydro-isoquinolinone moiety. Here, we focused on this chemotype, and a small set of compounds were designed to investigate ligand-receptor interactions experimentally and through computational methods. Elaborated compounds were tested against GABA_A α1 and α5 subunit-containing receptors, and binding affinities and functional activities were measured. Starting from the published experimental structure of an engineered, homopentameric, basmisanil-binding GABA_A receptor-like construct consisting of modified α5 subunits and an α1-containing GABA_A structure, we created a new model of the ligand binding site at the α5/γ2 interface. Using this model, the measured ligand affinities were able to be reproduced well by free energy perturbation (FEP) calculations. In addition, calculations were able to explain the obtained structure-activity relationships, among others, the switch-like effect of the aromatic nitrogen position in the dihydro-naphthyridinone motif for the functional character, and suggest different binding poses for the ligands presenting silent versus negative allosteric effects in this set (SAMs vs. NAMs, respectively). We believe that our results can help design α5 selective GABA_A negative allosteric modulators and better understand the GABA_A receptor.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"402-416"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142833083","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":"Essential Considerations for Free Energy Calculations of RNA-Small Molecule Complexes: Lessons from the Theophylline-Binding RNA Aptamer.","authors":"Ali Rasouli, Frank C Pickard, Sreyoshi Sur, Alan Grossfield, Mehtap Işık Bennett","doi":"10.1021/acs.jcim.4c01505","DOIUrl":"10.1021/acs.jcim.4c01505","url":null,"abstract":"<p><p>Alchemical free energy calculations are widely used to predict the binding affinity of small molecule ligands to protein targets; however, the application of these methods to RNA targets has not been deeply explored. We systematically investigated how modeling decisions affect the performance of absolute binding free energy calculations for a relatively simple RNA model system: theophylline-binding RNA aptamer with theophylline and five analogs. The goal of this investigation was 2-fold: (1) understanding the performance levels we can expect from absolute free energy calculations for a simple RNA complex and (2) learning about practical modeling considerations that impact the success of RNA-binding predictions, which may be different from the best practices established for protein targets. We learned that magnesium ion (Mg²⁺) placement is a critical decision that impacts affinity predictions. When information regarding Mg²⁺ positions is lacking, implementing RNA backbone restraints is an alternative way of stabilizing the RNA structure that recapitulates prediction accuracy. Since mistakes in Mg²⁺ placement can be detrimental, omitting magnesium ions entirely and using RNA backbone restraints are attractive as a risk-mitigating approach. We found that predictions are sensitive to modeling experimental buffer conditions correctly, including salt type and ionic strength. We explored the effects of sampling in the alchemical protocol, choice of the ligand force field (GAFF2/OpenFF Sage), and water model (TIP3P/OPC) on predictions, which allowed us to give practical advice for the application of free energy methods to RNA targets. By capturing experimental buffer conditions and implementing RNA backbone restraints, we were able to compute binding affinities accurately (mean absolute error (MAE) = 2.2 kcal/mol, Pearson's correlation coefficient = 0.9, Kendall's τ = 0.7). We believe there is much to learn about how to apply free energy calculations for RNA targets and how to enhance their performance in prospective predictions. This study is an important first step for learning best practices and special considerations for RNA-ligand free energy calculations. Future studies will consider increasingly complicated ligands and diverse RNA systems and help the development of general protocols for therapeutically relevant RNA targets.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"223-239"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11734693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851612","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":"Durian: A Comprehensive Benchmark for Structure-Based 3D Molecular Generation.","authors":"Dou Nie, Huifeng Zhao, Odin Zhang, Gaoqi Weng, Hui Zhang, Jieyu Jin, Haitao Lin, Yufei Huang, Liwei Liu, Dan Li, Tingjun Hou, Yu Kang","doi":"10.1021/acs.jcim.4c02232","DOIUrl":"10.1021/acs.jcim.4c02232","url":null,"abstract":"<p><p>Three-dimensional (3D) molecular generation models employ deep neural networks to simultaneously generate both topological representation and molecular conformations. Due to their advantages in utilizing the structural and interaction information on targets, as well as their reduced reliance on existing bioactivity data, these models have attracted widespread attention. However, limited training and testing data sets and the unexpected biases inherent in single evaluation metrics pose a significant challenge in comparing these models in practical settings. In this work, we proposed Durian, an evaluation framework for structure-based 3D molecular generation that incorporates protein-ligand data with experimental affinity and a comprehensive array of physicochemical and geometric metrics. The benchmark tasks encompass assessing the capability of models to reproduce the property distribution of training sets, generate molecules with rational distributions of drug-related properties, and exhibit potential high affinity toward given targets. Binding affinities were evaluated using three independent docking methods (QuickVina2, Surflex and Gnina) with both \"<b>Dock</b>\" and \"<b>Score</b>\" modes to reduce false positives arising from conformational searches or scoring functions. Specifically, we applied Durian to six 3D molecular generation methods: LiGAN, Pocket2Mol, DiffSBDD, SBDD, GraphBP, and SurfGen. While most methods demonstrated the ability to generate drug-like small molecules with reasonable physicochemical properties, they exhibited varying degrees of limitations in balancing novelty, structural rationality, and synthetic accessibility, thereby constraining their practical applications in drug discovery. Based on a total of 17 metrics, Durian highlights the importance of multiobjective optimization in 3D molecular generation methods. For instance, SurfGen and SBDD showed relatively comprehensive performance but could benefit from further improvements in molecular conformational rationality. Our evaluation framework is expected to provide meaningful guidance for the selection, optimization, and application of 3D generative models in practical drug design tasks.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"173-186"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142833215","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":"Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.","authors":"Jaechan Lee, Dongmin Bang, Sun Kim","doi":"10.1021/acs.jcim.4c01255","DOIUrl":"10.1021/acs.jcim.4c01255","url":null,"abstract":"<p><p>Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges due to their reliance on time-consuming precomputed features and the heavily imbalanced nature of binding site data without further investigations on their utility in drug discovery. To address these limitations, we introduced Multiview-ATPBind and ResiBoost. Multiview-ATPBind is an end-to-end deep learning model that integrates one-dimensional (1D) sequence and three-dimensional (3D) structural information for rapid and precise residue-level pocket-ligand interaction predictions. Additionally, ResiBoost is a novel residue-level boosting algorithm designed to mitigate data imbalance by enhancing the prediction of rare positive binding residues. Our approach outperforms state-of-the-art models on benchmark data sets, showing significant improvements in balanced metrics with both experimental and AI-predicted structures. Furthermore, our model seamlessly transfers to predicting binding sites and enhancing docking simulations for kinase inhibitors, including imatinib and dasatinib, underscoring the potential of our method in drug discovery applications.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"50-61"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845308","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":"Toward Automated Simulation Research Workflow through LLM Prompt Engineering Design.","authors":"Zhihan Liu, Yubo Chai, Jianfeng Li","doi":"10.1021/acs.jcim.4c01653","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01653","url":null,"abstract":"<p><p>The advent of Large Language Models (LLMs) has created new opportunities for the automation of scientific research spanning both experimental processes and computational simulations. This study explores the feasibility of constructing an autonomous simulation agent (ASA) powered by LLMs through prompt engineering and automated program design to automate the entire simulation research process according to a human-provided research plan. This process includes experimental design, remote upload and simulation execution, data analysis, and report compilation. Using a well-studied simulation problem of polymer chain conformations as a test case, we assessed the long-task completion and reliability of ASAs powered by different LLMs, including GPT-4o, Claude-3.5, etc. Our findings revealed that ASA-GPT-4o achieved near-flawless execution on designated research missions, underscoring the potential of methods like ASA to achieve automation in simulation research processes to enhance research efficiency. The outlined automation can be iteratively performed for up to 20 cycles without human intervention, illustrating the potential of ASA for long-task workflow automation. Additionally, we discussed the intrinsic traits of ASA in managing extensive tasks, focusing on self-validation mechanisms, and the balance between local attention and global oversight.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 1","pages":"114-124"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968677","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}