Journal of Chemical Information and Modeling 最新文献_第5页

Comparative Assessment of Water Models in Protein-Glycan Interaction: Insights from Alchemical Free Energy Calculations and Molecular Dynamics Simulations. 蛋白质-糖相互作用中水模型的比较评估：炼金术自由能计算和分子动力学模拟的启示。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-10-08 DOI: 10.1021/acs.jcim.4c01361

Deng Li, Mona S Minkara

{"title":"Comparative Assessment of Water Models in Protein-Glycan Interaction: Insights from Alchemical Free Energy Calculations and Molecular Dynamics Simulations.","authors":"Deng Li, Mona S Minkara","doi":"10.1021/acs.jcim.4c01361","DOIUrl":"10.1021/acs.jcim.4c01361","url":null,"abstract":"Accurate computational simulations of protein-glycan dynamics are crucial for a comprehensive understanding of critical biological mechanisms, including host-pathogen interactions, immune system defenses, and intercellular communication. The accuracy of these simulations, including molecular dynamics (MD) simulation and alchemical free energy calculations, critically relies on the appropriate parameters, including the water model, because of the extensive hydrogen bonding with glycan hydroxyl groups. However, a systematic evaluation of water models' accuracy in simulating protein-glycan interaction at the molecular level is still lacking. In this study, we used full atomistic MD simulations and alchemical absolute binding free energy (ABFE) calculations to investigate the performance of five distinct water models in six protein-glycan complex systems. We evaluated water models' impact on structural dynamics and binding affinity through over 5.8 μs of simulation time per system. Our results reveal that most protein-glycan complexes are stable in the overall structural dynamics regardless of the water model used, while some show obvious fluctuations with specific water models. More importantly, we discover that the stability of the binding motif's conformation is dependent on the water model chosen when its residues form weak hydrogen bonds with the glycan. The water model also influences the conformational stability of the glycan in its bound state according to density functional theory (DFT) calculations. Using alchemical ABFE calculations, we find that the OPC water model exhibits exceptional consistency with experimental binding affinity data, whereas commonly used models such as TIP3P are less accurate. The findings demonstrate how different water models affect protein-glycan interactions and the accuracy of binding affinity calculations, which is crucial in developing therapeutic strategies targeting these interactions.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9459-9473"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142386397","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

XDock: A General Docking Method for Modeling Protein-Ligand and Nucleic Acid-Ligand Interactions. XDock：用于模拟蛋白质配体和核酸配体相互作用的通用对接方法。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-11-27 DOI: 10.1021/acs.jcim.4c00855

Qilong Wu, Sheng-You Huang

引用次数: 0

All-Atom Simulations Reveal the Effect of Membrane Composition on the Signaling of the NKG2A/CD94/HLA-E Immune Receptor Complex.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-12-02 DOI: 10.1021/acs.jcim.4c01357

Martin Ljubič, Andrej Perdih, Jure Borišek

{"title":"All-Atom Simulations Reveal the Effect of Membrane Composition on the Signaling of the NKG2A/CD94/HLA-E Immune Receptor Complex.","authors":"Martin Ljubič, Andrej Perdih, Jure Borišek","doi":"10.1021/acs.jcim.4c01357","DOIUrl":"10.1021/acs.jcim.4c01357","url":null,"abstract":"Understanding how membrane composition influences the dynamics and function of transmembrane proteins is crucial for the comprehensive elucidation of cellular signaling mechanisms and the development of targeted therapeutics. In this study, we employed all-atom molecular dynamics simulations to investigate the impact of different membrane compositions on the conformational dynamics of the NKG2A/CD94/HLA-E immune receptor complex, a key negative regulator of natural killer cell cytotoxic activity. Our results reveal significant variations in the behavior of the immune complex structure across five different membrane compositions, which include POPC, POPA, DPPC, and DLPC phospholipids, and a mixed POPC/cholesterol system. These variations are particularly evident in the intracellular domain of NKG2A, manifested as changes in mobility, tyrosine exposure, and interdomain communication. Additionally, we found that a large concentration of negative charge at the surface of the POPA-based membrane greatly increased the number of contacts with lipid molecules and significantly decreased the exposure of intracellular NKG2A ITIM regions to water molecules, thus likely halting the signal transduction process. Furthermore, the DPPC model with a membrane possessing a high transition temperature in a gel-like state became curved, affecting the exposure of one ITIM region. The decreased membrane thickness in the DPLC model caused a significant transmembrane domain tilt, altering the linker protrusion angle and potentially disrupting the hydrogen bonding network in the extracellular domain. Overall, our findings highlight the importance of considering membrane composition in the analysis of transmembrane protein dynamics and in the exploration of novel strategies for the external modulation of their signaling pathways.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9374-9387"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142764628","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

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation. 机器学习驱动的蓝藻生物活性化合物的发现和数据库：治疗和生物修复资源。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-11-27 DOI: 10.1021/acs.jcim.4c00995

Renato Soares, Luísa Azevedo, Vitor Vasconcelos, Diogo Pratas, Sérgio F Sousa, João Carneiro

{"title":"Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.","authors":"Renato Soares, Luísa Azevedo, Vitor Vasconcelos, Diogo Pratas, Sérgio F Sousa, João Carneiro","doi":"10.1021/acs.jcim.4c00995","DOIUrl":"10.1021/acs.jcim.4c00995","url":null,"abstract":"Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and research applications is essential. In this study, a searchable, updated, curated, and downloadable database of cyanobacteria bioactive compounds was designed, along with a machine-learning model to predict the compounds' targets of newly discovered molecules. A Python programming protocol obtained 3431 cyanobacteria bioactive compounds, 373 unique protein targets, and 3027 molecular descriptors. PaDEL-descriptor, Mordred, and Drugtax software were used to calculate the chemical descriptors for each bioactive compound database record. The biochemical descriptors were then used to determine the most promising protein targets for human therapeutic approaches and environmental bioremediation using the best machine learning (ML) model. The creation of our database, coupled with the integration of computational docking protocols, represents an innovative approach to understanding the potential of cyanobacteria bioactive compounds. This resource, adhering to the findability, accessibility, interoperability, and reuse of digital assets (FAIR) principles, is an excellent tool for pharmaceutical and bioremediation researchers. Moreover, its capacity to facilitate the exploration of specific compounds' interactions with environmental pollutants is a significant advancement, aligning with the increasing reliance on data science and machine learning to address environmental challenges. This study is a notable step forward in leveraging cyanobacteria for both therapeutic and ecological sustainability.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9576-9593"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737776","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

Optimal Dielectric Boundary for Binding Free Energy Estimates in the Implicit Solvent.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-12-10 DOI: 10.1021/acs.jcim.4c01190

Negin Forouzesh, Fatemeh Ghafouri, Igor S Tolokh, Alexey V Onufriev

{"title":"Optimal Dielectric Boundary for Binding Free Energy Estimates in the Implicit Solvent.","authors":"Negin Forouzesh, Fatemeh Ghafouri, Igor S Tolokh, Alexey V Onufriev","doi":"10.1021/acs.jcim.4c01190","DOIUrl":"10.1021/acs.jcim.4c01190","url":null,"abstract":"Accuracy of binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a multidimensional optimization pipeline is used to find optimal atomic radii, specifically for binding calculations in the implicit solvent. To reduce overfitting, the optimization target includes separate, weighted contributions from both binding and hydration free energies. The resulting five-parameter radii set, OPT_BIND5D, is evaluated against experiment for binding free energies of 20 host-guest (H-G) systems, unrelated to the types of structures used in the training. The resulting accuracy for this H-G test set (root mean square error of 2.03 kcal/mol, mean signed error of -0.13 kcal/mol, mean absolute error of 1.68 kcal/mol, and Pearson's correlation of r = 0.79 with the experimental values) is on par with what can be expected from the fixed charge explicit solvent models. Best agreement with the experiment is achieved when the implicit salt concentration is set equal or close to the experimental conditions.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9433-9448"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798650","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

Geometry Optimization Using the Frozen Domain and Partial Dimer Approaches in the Fragment Molecular Orbital Method: Implementation, Benchmark, and Applications to Protein Ligand-Binding Sites.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-12-02 DOI: 10.1021/acs.jcim.4c01280

Koji Okuwaki, Naoki Watanabe, Koichiro Kato, Chiduru Watanabe, Naofumi Nakayama, Akifumi Kato, Yuji Mochizuki, Tatsuya Nakano, Teruki Honma, Kaori Fukuzawa

{"title":"Geometry Optimization Using the Frozen Domain and Partial Dimer Approaches in the Fragment Molecular Orbital Method: Implementation, Benchmark, and Applications to Protein Ligand-Binding Sites.","authors":"Koji Okuwaki, Naoki Watanabe, Koichiro Kato, Chiduru Watanabe, Naofumi Nakayama, Akifumi Kato, Yuji Mochizuki, Tatsuya Nakano, Teruki Honma, Kaori Fukuzawa","doi":"10.1021/acs.jcim.4c01280","DOIUrl":"10.1021/acs.jcim.4c01280","url":null,"abstract":"The frozen domain (FD) approximation with the fragment molecular orbital (FMO) method is efficient for partial geometry optimization of large systems. We implemented the FD formulation (FD and frozen domain dimer [FDD] methods) already proposed by Fedorov, D. G. et al. (J. Phys. Chem. Lett. 2011, 2, 282-288); proposed a variation of it, namely frozen domain and partial dimer (FDPD) method; and applied it to several protein-ligand complexes. The computational time for geometry optimization at the FDPD/HF/6-31G* level for the active site (six fragments) of the largest β2-adrenergic G-protein-coupled receptor (440 residues) was almost half that of the conventional partial geometry optimization method. In the human estrogen receptor, the crystal structure was refined by FDPD geometry optimization of estradiol, surrounding hydrogen-bonded residues and a water molecule. The rather polarized ligand binding site of influenza virus neuraminidase was also optimized by FDPD optimization, which relaxed steric repulsion around the ligand in the crystal structure and optimized hydrogen bonding. For Serine-Threonine Kinase Pim1 and six inhibitors, the structures of the ligand binding site, Lys67, Glu121, Arg122, and benzofuranone ring and indole/azaindole ring of the ligand, were optimized at FDPD/HF/6-31G* and the ligand binding energy was estimated at the FMO-MP2/6-31G* level. As a result of examining three different optimization regions, the correlation coefficient between pIC50 and ligand binding energy was considerably improved by expanding the optimized region; in other words, better structure-activity relationships was obtained. Thus, this approach is promising as a high-precision structure refinement method for structure-based drug discovery.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9449-9458"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142764654","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

Analog Accessibility Score (AAscore) for Rational Compound Selection.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-12-06 DOI: 10.1021/acs.jcim.4c01691

Takato Ue, Akinori Sato, Tomoyuki Miyao

{"title":"Analog Accessibility Score (AAscore) for Rational Compound Selection.","authors":"Takato Ue, Akinori Sato, Tomoyuki Miyao","doi":"10.1021/acs.jcim.4c01691","DOIUrl":"10.1021/acs.jcim.4c01691","url":null,"abstract":"Various in silico scores have been proposed to objectively assess the characteristics and properties of a compound. However, there is still no score that represents the analog accessibility of a compound. Such a score would be valuable for selecting compounds proposed by virtual screening or for prioritizing hit compounds for the hit-to-lead phase. This study proposes an analog accessibility score (AAscore), where retrosynthesis prediction and forward product prediction models were utilized to generate virtual analogs. The AAscore is defined as the number of unique analogs and virtual synthetic routes. To evaluate the AAscore in terms of the number of actually synthesized analog compounds, analog compounds were prepared by using the compound-core relationship (CCR) method. It was found that the AAscore was little correlated with the number of CCR-based analogs. Furthermore, AAscores were found to be significantly influenced by the number of extracted candidate reactants from a reactant database. A case study targeting compounds active against carbonic anhydrase 2 showed that the AAscore could identify compounds that were synthesized into analogs.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9350-9360"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142783337","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

Chemoenzymatic Synthesis Planning Guided by Reaction Type Score.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-12-08 DOI: 10.1021/acs.jcim.4c01525

Hongxiang Li, Xuan Liu, Guangde Jiang, Huimin Zhao

{"title":"Chemoenzymatic Synthesis Planning Guided by Reaction Type Score.","authors":"Hongxiang Li, Xuan Liu, Guangde Jiang, Huimin Zhao","doi":"10.1021/acs.jcim.4c01525","DOIUrl":"10.1021/acs.jcim.4c01525","url":null,"abstract":"Thanks to the growing interest in computer-aided synthesis planning (CASP), a wide variety of retrosynthesis and retrobiosynthesis tools have been developed in the past decades. However, synthesis planning tools for multistep chemoenzymatic reactions are still rare despite the widespread use of enzymatic reactions in chemical synthesis. Herein, we report a reaction type score (RTscore)-guided chemoenzymatic synthesis planning (RTS-CESP) strategy. Briefly, the RTscore is trained using a text-based convolutional neural network (TextCNN) to distinguish synthesis reactions from decomposition reactions and evaluate synthesis efficiency. Once multiple chemical synthesis routes are generated by a retrosynthesis tool for a target molecule, RTscore is used to rank them and find the step(s) that can be replaced by enzymatic reactions to improve synthesis efficiency. As proof of concept, RTS-CESP was applied to 10 molecules with known chemoenzymatic synthesis routes in the literature and was able to predict all of them with six being the top-ranked routes. Moreover, RTS-CESP was employed for 1000 molecules in the boutique database and was able to predict the chemoenzymatic synthesis routes for 554 molecules, outperforming ASKCOS, a state-of-the-art chemoenzymatic synthesis planning tool. Finally, RTS-CESP was used to design a new chemoenzymatic synthesis route for the FDA-approved drug Alclofenac, which was shorter than the literature-reported route and has been experimentally validated.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9240-9248"},"PeriodicalIF":5.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794034","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

CACHE Challenge #1: Docking with GNINA Is All You Need.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-12-09 DOI: 10.1021/acs.jcim.4c01429

Ian Dunn, Somayeh Pirhadi, Yao Wang, Smmrithi Ravindran, Carter Concepcion, David Ryan Koes

引用次数: 0

Ordinal Confidence Level Assignments for Regression Model Predictions.

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-12-09 DOI: 10.1021/acs.jcim.4c01755

Steven Kearnes, Patrick Riley

引用次数: 0