Molecular Informatics最新文献_第6页

Targeting of essential mycobacterial replication enzyme DnaG primase revealed Mitoxantrone and Vapreotide as novel mycobacterial growth inhibitors. 以分枝杆菌的基本复制酶 DnaG primase 为靶标，发现米托蒽醌和伐普瑞泰是新型的分枝杆菌生长抑制剂。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-20 DOI: 10.1002/minf.202300284

Sonam Grover, Waseem Ali, Salma Jamal, Rishabh Gangwar, Faraz Ahmed, Rahul Sharma, Meetu Agarwal, Javaid Ahmad Sheikh, Abhinav Grover

{"title":"Targeting of essential mycobacterial replication enzyme DnaG primase revealed Mitoxantrone and Vapreotide as novel mycobacterial growth inhibitors.","authors":"Sonam Grover, Waseem Ali, Salma Jamal, Rishabh Gangwar, Faraz Ahmed, Rahul Sharma, Meetu Agarwal, Javaid Ahmad Sheikh, Abhinav Grover","doi":"10.1002/minf.202300284","DOIUrl":"https://doi.org/10.1002/minf.202300284","url":null,"abstract":"Tuberculosis (TB) is the second leading cause of mortality after COVID-19, with a global death toll of 1.6 million in 2021. The escalating situation of drug-resistant forms of TB has threatened the current TB management strategies. New therapeutics with novel mechanisms of action are urgently required to address the current global TB crisis. The essential mycobacterial primase DnaG with no structural homology to homo sapiens presents itself as a good candidate for drug targeting. In the present study, Mitoxantrone and Vapreotide, two FDA-approved drugs, were identified as potential anti-mycobacterial agents. Both Mitoxantrone and Vapreotide exhibit a strong Minimum Inhibitory Concentration (MIC) of ≤25µg/ml against both the virulent (M.tb-H37Rv) and avirulent (M.tb-H37Ra) strains of M.tb. Extending the validations further revealed the inhibitory potential drugs in ex-vivo conditions. Leveraging the computational high-throughput multi-level docking procedures from the pool of ~2700 FDA-approved compounds, Mitoxantrone and Vapreotide were screened out as potential inhibitors of DnaG. Extensive 200ns long all-atoms molecular dynamic simulation of DnaGDrugs complexes revealed that both drugs bind strongly and stabilize the DnaG during simulations. Reduced solvent exposure and confined motions of the active centre of DnaG upon complexation with drugs indicated that both drugs led to the closure of the active site of DnaG. From this study's findings, we propose Mitoxantrone and Vapreotide as potential anti-mycobacterial agents, with their novel mechanism of action against mycobacterial DnaG.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138825802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-14 DOI: 10.1002/minf.202300206

Jaime Pérez-Villanueva, Karen Rodríguez-Villar, Francisco Cortés-Benítez, Juan Francisco Palacios-Espinosa

引用次数: 0

Use of tree-based machine learning methods to screen affinitive peptides based on docking data. 使用基于树的机器学习方法筛选基于对接数据的亲和肽。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-01 Epub Date: 2023-11-09 DOI: 10.1002/minf.202300143

Hua Feng, Fangyu Wang, Ning Li, Qian Xu, Guanming Zheng, Xuefeng Sun, Man Hu, Xuewu Li, Guangxu Xing, Gaiping Zhang

{"title":"Use of tree-based machine learning methods to screen affinitive peptides based on docking data.","authors":"Hua Feng, Fangyu Wang, Ning Li, Qian Xu, Guanming Zheng, Xuefeng Sun, Man Hu, Xuewu Li, Guangxu Xing, Gaiping Zhang","doi":"10.1002/minf.202300143","DOIUrl":"10.1002/minf.202300143","url":null,"abstract":"Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10212086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the duration of action of β2-adrenergic receptor agonists: Ligand and structure-based approaches. 预测β2-肾上腺素能受体激动剂的作用持续时间：基于配体和结构的方法。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-01 Epub Date: 2023-11-09 DOI: 10.1002/minf.202300141

Luca Chiesa, Emilie Sick, Esther Kellenberger

引用次数: 0

An in silico investigation of Kv2.1 potassium channel: Model building and inhibitors binding sites analysis. Kv2.1钾通道的计算机研究：模型构建和抑制剂结合位点分析。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-01 Epub Date: 2023-11-07 DOI: 10.1002/minf.202300072

Xiaoyu Wang, Xinyuan Zhang, Jie Zhou, Weiping Wang, Xiaoliang Wang, Bailing Xu

引用次数: 0

AliNA - a deep learning program for RNA secondary structure prediction. AliNA-一个用于RNA二级结构预测的深度学习程序。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-01 Epub Date: 2023-11-02 DOI: 10.1002/minf.202300113

Shamsudin S Nasaev, Artem R Mukanov, Ivan I Kuznetsov, Alexander V Veselovsky

引用次数: 0

Classification of tastants: A deep learning based approach. 味觉分类：一种基于深度学习的方法。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-12-01 Epub Date: 2023-11-09 DOI: 10.1002/minf.202300146

Prantar Dutta, Deepak Jain, Rakesh Gupta, Beena Rai

{"title":"Classification of tastants: A deep learning based approach.","authors":"Prantar Dutta, Deepak Jain, Rakesh Gupta, Beena Rai","doi":"10.1002/minf.202300146","DOIUrl":"10.1002/minf.202300146","url":null,"abstract":"Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules- the three basic tastes whose sensation is mediated by G protein-coupled receptors. An extensive dataset containing 1466 bitter, 1764 sweet, and 238 umami tastants was curated from existing literature. We analyzed the chemical characteristics of the molecules, with special focus on the presence of different functional groups. A deep neural network model based on molecular descriptors and a graph neural network model were trained for taste prediction. The class imbalance due to fewer umami molecules was tackled using special sampling techniques. Both models show comparable performance during evaluation, but the graph-based model can learn task-specific representations from the molecular structure without requiring handcrafted features. We further explain the deep neural network predictions using Shapley additive explanations. Finally, we demonstrated the applicability of the models by screening bitter, sweet, and umami molecules from a large food database. This study develops an in-silico approach to classify molecules based on their taste by leveraging the recent progress in deep learning, which can serve as a powerful tool for tastant design.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54230131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Development of novel ligands against SARS-CoV-2 M^pro enzyme: an in silico and in vitro Study. 新型抗SARS-CoV-2 Mpro酶配体的研制:硅化和体外研究

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-11-01 Epub Date: 2023-09-06 DOI: 10.1002/minf.202300120

Navid Kaboudi, Nadine Krüger, Maryam Hamzeh-Mivehroud

{"title":"Development of novel ligands against SARS-CoV-2 Mpro enzyme: an in silico and in vitro Study.","authors":"Navid Kaboudi, Nadine Krüger, Maryam Hamzeh-Mivehroud","doi":"10.1002/minf.202300120","DOIUrl":"10.1002/minf.202300120","url":null,"abstract":"Background: Despite tremendous efforts made by scientific community during the outbreak of COVID-19 pandemic, this disease still remains as a public health concern. Although different types of vaccines were globally used to reduce the mortality, emergence of new variants of SARS-CoV-2 is a challenging issue in COVID-19 pharmacotherapy. In this context, target therapy of SARS-CoV-2 by small ligands is a promising strategy.Methods: In this investigation, we applied ligand-based virtual screening for finding novel molecules based on nirmatrelvir structure. Various criteria including drug-likeness, ADME, and toxicity properties were applied for filtering the compounds. The selected candidate molecules were subjected to molecular docking and dynamics simulation for predicting the binding mode and binding free energy, respectively. Then the molecules were experimentally evaluated in terms of antiviral activity against SARS-CoV-2 and toxicity assessment.Results: The results demonstrated that the identified compounds showed inhibitory activity towards SARS-CoV-2 Mpro .Conclusion: In summary, the introduced compounds may provide novel scaffold for further structural modification and optimization with improved anti SARS-CoV-2 Mpro activity.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10166837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cell-penetrating peptides predictors: A comparative analysis of methods and datasets. 细胞穿透多肽预测因子:方法和数据集的比较分析。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-11-01 Epub Date: 2023-09-06 DOI: 10.1002/minf.202300104

Karen Guerrero-Vázquez, Gabriel Del Rio, Carlos A Brizuela

{"title":"Cell-penetrating peptides predictors: A comparative analysis of methods and datasets.","authors":"Karen Guerrero-Vázquez, Gabriel Del Rio, Carlos A Brizuela","doi":"10.1002/minf.202300104","DOIUrl":"10.1002/minf.202300104","url":null,"abstract":"Cell-Penetrating Peptides (CPP) are emerging as an alternative to small-molecule drugs to expand the range of biomolecules that can be targeted for therapeutic purposes. Due to the importance of identifying and designing new CPP, a great variety of predictors have been developed to achieve these goals. To establish a ranking for these predictors, a couple of recent studies compared their performances on specific datasets, yet their conclusions cannot determine if the ranking obtained is due to the model, the set of descriptors or the datasets used to test the predictors. We present a systematic study of the influence of the peptide sequence's similarity of the datasets on the predictors' performance. The analysis reveals that the datasets used for training have a stronger influence on the predictors performance than the model or descriptors employed. We show that datasets with low sequence similarity between the positive and negative examples can be easily separated, and the tested classifiers showed good performance on them. On the other hand, a dataset with high sequence similarity between CPP and non-CPP will be a hard dataset, and it should be the one to be used for assessing the performance of new predictors.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10225885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Absorption matters: A closer look at popular oral bioavailability rules for drug approvals. 吸收问题:药物批准流行的口服生物利用度规则。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-11-01 Epub Date: 2023-08-31 DOI: 10.1002/minf.202300115

Artur Caminero Gomes Soares, Gustavo Henrique Marques Sousa, Raisa Ludmila Calil, Gustavo Henrique Goulart Trossini

{"title":"Absorption matters: A closer look at popular oral bioavailability rules for drug approvals.","authors":"Artur Caminero Gomes Soares, Gustavo Henrique Marques Sousa, Raisa Ludmila Calil, Gustavo Henrique Goulart Trossini","doi":"10.1002/minf.202300115","DOIUrl":"10.1002/minf.202300115","url":null,"abstract":"This study examines how two popular drug‐likeness concepts used in early development, Lipinski Rule of Five (Ro5) and Veber's Rules, possibly affected drug profiles of FDA approved drugs since 1997. Our findings suggest that when all criteria are applied, relevant compounds may be excluded, addressing the harmfulness of blindly employing these rules. Of all oral drugs in the period used for this analysis, around 66 % conform to the RO5 and 85 % to Veber's Rules. Molecular Weight and calculated LogP showed low consistent values over time, apart from being the two least followed rules, challenging their relevance. On the other hand, hydrogen bond related rules and the number of rotatable bonds are amongst the most followed criteria and show exceptional consistency over time. Furthermore, our analysis indicates that topological polar surface area and total count of hydrogen bonds cannot be used as interchangeable parameters, contrary to the original proposal. This research enhances the comprehension of drug profiles that were FDA approved in the post‐Lipinski period. Medicinal chemists could utilize these heuristics as a limited guide to direct their exploration of the oral bioavailability chemical space, but they must also steer the wheel to break these rules and explore different regions when necessary.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10117941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0