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1
Dai, Weixing, and Dianjing Guo. "A Ligand-Based Virtual Screening Method Using Direct Quantification of Generalization Ability." Molecules 24, no. 13 (June 30, 2019): 2414. http://dx.doi.org/10.3390/molecules24132414.
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Machine learning plays an important role in ligand-based virtual screening. However, conventional machine learning approaches tend to be inefficient when dealing with such problems where the data are imbalanced and features describing the chemical characteristic of ligands are high-dimensional. We here describe a machine learning algorithm LBS (local beta screening) for ligand-based virtual screening. The unique characteristic of LBS is that it quantifies the generalization ability of screening directly by a refined loss function, and thus can assess the risk of over-fitting accurately and efficiently for imbalanced and high-dimensional data in ligand-based virtual screening without the help of resampling methods such as cross validation. The robustness of LBS was demonstrated by a simulation study and tests on real datasets, in which LBS outperformed conventional algorithms in terms of screening accuracy and model interpretation. LBS was then used for screening potential activators of HIV-1 integrase multimerization in an independent compound library, and the virtual screening result was experimentally validated. Of the 25 compounds tested, six were proved to be active. The most potent compound in experimental validation showed an EC50 value of 0.71 µM.
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Dotolo, Serena, Carmen Cervellera, Maria Russo, Gian Luigi Russo, and Angelo Facchiano. "Virtual Screening of Natural Compounds as Potential PI3K-AKT1 Signaling Pathway Inhibitors and Experimental Validation." Molecules 26, no. 2 (January 18, 2021): 492. http://dx.doi.org/10.3390/molecules26020492.
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A computational screening for natural compounds suitable to bind the AKT protein has been performed after the generation of a pharmacophore model based on the experimental structure of AKT1 complexed with IQO, a well-known inhibitor. The compounds resulted as being most suitable from the screening have been further investigated by molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis and toxicity profiles. Two compounds selected at the end of the computational analysis, i.e., ZINC2429155 (also named STL1) and ZINC1447881 (also named AC1), have been tested in an experimental assay, together with IQO as a positive control and quercetin as a negative control. Only STL1 clearly inhibited AKT activation negatively modulating the PI3K/AKT pathway.
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Lewis, Stephanie N., Josep Bassaganya-Riera, and David R. Bevan. "Virtual Screening as a Technique for PPAR Modulator Discovery." PPAR Research 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/861238.
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Virtual screening (VS) is a discovery technique to identify novel compounds with therapeutic and preventive efficacy against disease. Our current focus is on the in silico screening and discovery of novel peroxisome proliferator-activated receptor-gamma (PPARγ) agonists. It is well recognized that PPARγagonists have therapeutic applications as insulin sensitizers in type 2 diabetes or as anti-inflammatories. VS is a cost- and time-effective means for identifying small molecules that have therapeutic potential. Our long-term goal is to devise computational approaches for testing the PPARγ-binding activity of extensive naturally occurring compound libraries prior to testing agonist activity using ligand-binding and reporter assays. This review summarizes the high potential for obtaining further fundamental understanding of PPARγbiology and development of novel therapies for treating chronic inflammatory diseases through evolution and implementation of computational screening processes for immunotherapeutics in conjunction with experimental methods for calibration and validation of results.
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Ferreira, Letícia Tiburcio, Joyce V. B. Borba, José Teófilo Moreira-Filho, Aline Rimoldi, Carolina Horta Andrade, and Fabio Trindade Maranhão Costa. "QSAR-Based Virtual Screening of Natural Products Database for Identification of Potent Antimalarial Hits." Biomolecules 11, no. 3 (March 19, 2021): 459. http://dx.doi.org/10.3390/biom11030459.
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With about 400,000 annual deaths worldwide, malaria remains a public health burden in tropical and subtropical areas, especially in low-income countries. Selection of drug-resistant Plasmodium strains has driven the need to explore novel antimalarial compounds with diverse modes of action. In this context, biodiversity has been widely exploited as a resourceful channel of biologically active compounds, as exemplified by antimalarial drugs such as quinine and artemisinin, derived from natural products. Thus, combining a natural product library and quantitative structure–activity relationship (QSAR)-based virtual screening, we have prioritized genuine and derivative natural compounds with potential antimalarial activity prior to in vitro testing. Experimental validation against cultured chloroquine-sensitive and multi-drug-resistant P. falciparum strains confirmed the potent and selective activity of two sesquiterpene lactones (LDT-597 and LDT-598) identified in silico. Quantitative structure–property relationship (QSPR) models predicted absorption, distribution, metabolism, and excretion (ADME) and physiologically based pharmacokinetic (PBPK) parameters for the most promising compound, showing that it presents good physiologically based pharmacokinetic properties both in rats and humans. Altogether, the in vitro parasite growth inhibition results obtained from in silico screened compounds encourage the use of virtual screening campaigns for identification of promising natural compound-based antimalarial molecules.
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Scarpino, Andrea, László Petri, Damijan Knez, Tímea Imre, Péter Ábrányi-Balogh, György G. Ferenczy, Stanislav Gobec, and György M. Keserű. "WIDOCK: a reactive docking protocol for virtual screening of covalent inhibitors." Journal of Computer-Aided Molecular Design 35, no. 2 (January 18, 2021): 223–44. http://dx.doi.org/10.1007/s10822-020-00371-5.
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AbstractHere we present WIDOCK, a virtual screening protocol that supports the selection of diverse electrophiles as covalent inhibitors by incorporating ligand reactivity towards cysteine residues into AutoDock4. WIDOCK applies the reactive docking method (Backus et al. in Nature 534:570–574, 2016) and extends it into a virtual screening tool by introducing facile experimental or computational parametrization and a ligand focused evaluation scheme together with a retrospective and prospective validation against various therapeutically relevant targets. Parameters accounting for ligand reactivity are derived from experimental reaction kinetic data or alternatively from computed reaction barriers. The performance of this docking protocol was first evaluated by investigating compound series with diverse warhead chemotypes against KRASG12C, MurA and cathepsin B. In addition, WIDOCK was challenged on larger electrophilic libraries screened against OTUB2 and NUDT7. These retrospective analyses showed high sensitivity in retrieving experimental actives, by also leading to superior ROC curves, AUC values and better enrichments than the standard covalent docking tool available in AutoDock4 when compound collections with diverse warheads were investigated. Finally, we applied WIDOCK for the prospective identification of covalent human MAO-A inhibitors acting via a new mechanism by binding to Cys323. The inhibitory activity of several predicted compounds was experimentally confirmed and the labelling of Cys323 was proved by subsequent MS/MS measurements. These findings demonstrate the usefulness of WIDOCK as a warhead-sensitive, covalent virtual screening protocol.
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Rahman, A. S. M. Zisanur, Chengyou Liu, Hunter Sturm, Andrew M. Hogan, Rebecca Davis, Pingzhao Hu, and Silvia T. Cardona. "A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery." PLOS Computational Biology 18, no. 10 (October 13, 2022): e1010613. http://dx.doi.org/10.1371/journal.pcbi.1010613.
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Screening for novel antibacterial compounds in small molecule libraries has a low success rate. We applied machine learning (ML)-based virtual screening for antibacterial activity and evaluated its predictive power by experimental validation. We first binarized 29,537 compounds according to their growth inhibitory activity (hit rate 0.87%) against the antibiotic-resistant bacterium Burkholderia cenocepacia and described their molecular features with a directed-message passing neural network (D-MPNN). Then, we used the data to train an ML model that achieved a receiver operating characteristic (ROC) score of 0.823 on the test set. Finally, we predicted antibacterial activity in virtual libraries corresponding to 1,614 compounds from the Food and Drug Administration (FDA)-approved list and 224,205 natural products. Hit rates of 26% and 12%, respectively, were obtained when we tested the top-ranked predicted compounds for growth inhibitory activity against B. cenocepacia, which represents at least a 14-fold increase from the previous hit rate. In addition, more than 51% of the predicted antibacterial natural compounds inhibited ESKAPE pathogens showing that predictions expand beyond the organism-specific dataset to a broad range of bacteria. Overall, the developed ML approach can be used for compound prioritization before screening, increasing the typical hit rate of drug discovery.
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Ji, Xin, Zhensheng Wang, Qianqian Chen, Jingzhong Li, Heng Wang, Zenglei Wang, and Lan Yang. "In Silico and In Vitro Antimalarial Screening and Validation Targeting Plasmodium falciparum Plasmepsin V." Molecules 27, no. 9 (April 21, 2022): 2670. http://dx.doi.org/10.3390/molecules27092670.
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Malaria chemotherapy is greatly threatened by the recent emergence and spread of resistance in the Plasmodium falciparum parasite against artemisinins and their partner drugs. Therefore, it is an urgent priority to develop new antimalarials. Plasmepsin V (PMV) is regarded as a superior drug target for its essential role in protein export. In this study, we performed virtual screening based on homology modeling of PMV structure, molecular docking and pharmacophore model analysis against a library with 1,535,478 compounds, which yielded 233 hits. Their antimalarial activities were assessed amongst four non-peptidomimetic compounds that demonstrated the promising inhibition of parasite growth, with mean IC50 values of 6.67 μM, 5.10 μM, 12.55 μM and 8.31 μM. No significant affection to the viability of L929 cells was detected in these candidates. These four compounds displayed strong binding activities with the PfPMV model through H-bond, hydrophobic, halogen bond or π-π interactions in molecular docking, with binding scores under −9.0 kcal/mol. The experimental validation of molecule-protein interaction identified the binding of four compounds with multiple plasmepsins; however, only compound 47 showed interaction with plasmepsin V, which exhibited the potential to be developed as an active PfPMV inhibitor.
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Zhu, Hui, Yulin Zhang, Wei Li, and Niu Huang. "A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years." International Journal of Molecular Sciences 23, no. 24 (December 15, 2022): 15961. http://dx.doi.org/10.3390/ijms232415961.
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Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 μM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.
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Wang, Jing, Yu Jiang, Yingnan Wu, Hui Yu, Zhanli Wang, and Yuheng Ma. "Pharmacophore-Based Virtual Screening of Potential SARS-CoV-2 Main Protease Inhibitors from Library of Natural Products." Natural Product Communications 17, no. 12 (December 2022): 1934578X2211436. http://dx.doi.org/10.1177/1934578x221143635.
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Background: The SARS-CoV-2 main protease (Mpro) is an attractive target for drug discovery. Methods: A pharmacophore model was built using the three-dimensional (3D) pharmacophore generation algorithm HypoGen in Discovery Studio 2019. The best pharmacophore model was selected for validation using a test set of 24 compounds and was used as a 3D query for further screening of an in-house database of natural compounds. Lipinski's rule of five was used to assess the drug-like properties of the hit compounds. The filtered compounds were then subjected to bioactivity evaluations. The active compounds were docked into the active site of the SARS-CoV-2 Mpro crystal structure (PDB ID: 7D1M). Results: A suitable 3D pharmacophore model, Hypo1, was found to be the best model, consisting of four features (one hydrophobic feature, one hydrogen bond donor, and two hydrogen bond acceptors). Pharmacophore-based virtual screening with Hypo1 as the query to search an in-house database of 34 439 natural compounds resulted in 1502 hits. Among these, 255 compounds satisfied Lipinski's rule of five. The highest ranking 10 compounds were selected for further experimental testing, and one hit (W-7) illustrated inhibitory activity against SARS-CoV-2 Mpro with an IC50 value of 75 μM. Docking studies revealed that this hit compound retained the necessary interactions within the active site of SARS-CoV-2 Mpro. Conclusion The identified lead natural compound could provide a scaffold for the further development of SARS-CoV-2 Mpro inhibitors.
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Bajusz, Dávid, Zsolt Bognár, Jessica Ebner, Florian Grebien, and György M. Keserű. "Discovery of a Non-Nucleoside SETD2 Methyltransferase Inhibitor against Acute Myeloid Leukemia." International Journal of Molecular Sciences 22, no. 18 (September 17, 2021): 10055. http://dx.doi.org/10.3390/ijms221810055.
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Histone methyltransferases (HMTs) have attracted considerable attention as potential targets for pharmaceutical intervention in various malignant diseases. These enzymes are known for introducing methyl marks at specific locations of histone proteins, creating a complex system that regulates epigenetic control of gene expression and cell differentiation. Here, we describe the identification of first-generation cell-permeable non-nucleoside type inhibitors of SETD2, the only mammalian HMT that is able to tri-methylate the K36 residue of histone H3. By generating the epigenetic mark H3K36me3, SETD2 is involved in the progression of acute myeloid leukemia. We developed a structure-based virtual screening protocol that was first validated in retrospective studies. Next, prospective screening was performed on a large library of commercially available compounds. Experimental validation of 22 virtual hits led to the discovery of three compounds that showed dose-dependent inhibition of the enzymatic activity of SETD2. Compound C13 effectively blocked the proliferation of two acute myeloid leukemia (AML) cell lines with MLL rearrangements and led to decreased H3K36me3 levels, prioritizing this chemotype as a viable chemical starting point for drug discovery projects.
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Ponnusamy, Nirmaladevi, Rajasree Odumpatta, Pavithra Damodharan, and Mohanapriya Arumugam. "Computational investigation of marine bioactive compounds reveals frigocyclinone as a potent inhibitor of Kaposi’s Sarcoma Associated Herpesvirus (KSHV) targets." Biomedical & Pharmacology Journal 12, no. 3 (August 21, 2019): 1289–302. http://dx.doi.org/10.13005/bpj/1757.
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In the present study, in silico analysis was employed to identify the action of marine bioactive compounds against KSHV targets. Virulence factor analysis of KSHV from literature review, three proteins LANA1, vIRF3/LANA2 and PF-8 were identified as putative drug targets. The quality of protein structures play a significant role in the experimental structure validation and prediction, where the predicted structures may contain considerable errors was checked by SAVES v5.0 servers. By virtual screening four potential bioactive compounds Ascorbic acid, Salicylihalamide A, Salicylihalamide B and Frigocyclinone were predicted. One of the potential compounds of Frigocyclinone has acting against KSHV proteins. Hence, determined as the good lead molecule against KSHV. Molecular dynamic simulation studies revealed the stability of LANA1- Frigocyclinone complex and it could be a futuristic perspective chemical compound for Kaposi’s sarcoma.
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Doğan, Tunca, Ece Akhan Güzelcan, Marcus Baumann, Altay Koyas, Heval Atas, Ian R. Baxendale, Maria Martin, and Rengul Cetin-Atalay. "Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases." PLOS Computational Biology 17, no. 11 (November 29, 2021): e1009171. http://dx.doi.org/10.1371/journal.pcbi.1009171.
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Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins’ structure/function, and bias in system training datasets. Here, we propose a new method “DRUIDom” (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound–target pairs (~2.9M data points), and used as training data for calculating parameters of compound–domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound–protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound–domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom.
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Zhong, Cai, Jiali Ai, Yaxin Yang, Fangyuan Ma, and Wei Sun. "Small Molecular Drug Screening Based on Clinical Therapeutic Effect." Molecules 27, no. 15 (July 27, 2022): 4807. http://dx.doi.org/10.3390/molecules27154807.
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Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard–Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future.
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Urista, Diana V., Diego B. Carrué, Iago Otero, Sonia Arrasate, Viviana F. Quevedo-Tumailli, Marcos Gestal, Humbert González-Díaz, and Cristian R. Munteanu. "Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models." Biology 9, no. 8 (July 30, 2020): 198. http://dx.doi.org/10.3390/biology9080198.
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Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.
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Glaser, Jens, Josh V. Vermaas, David M. Rogers, Jeff Larkin, Scott LeGrand, Swen Boehm, Matthew B. Baker, et al. "High-throughput virtual laboratory for drug discovery using massive datasets." International Journal of High Performance Computing Applications 35, no. 5 (March 23, 2021): 452–68. http://dx.doi.org/10.1177/10943420211001565.
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Time-to-solution for structure-based screening of massive chemical databases for COVID-19 drug discovery has been decreased by an order of magnitude, and a virtual laboratory has been deployed at scale on up to 27,612 GPUs on the Summit supercomputer, allowing an average molecular docking of 19,028 compounds per second. Over one billion compounds were docked to two SARS-CoV-2 protein structures with full optimization of ligand position and 20 poses per docking, each in under 24 hours. GPU acceleration and high-throughput optimizations of the docking program produced 350× mean speedup over the CPU version (50× speedup per node). GPU acceleration of both feature calculation for machine-learning based scoring and distributed database queries reduced processing of the 2.4 TB output by orders of magnitude. The resulting 50× speedup for the full pipeline reduces an initial 43 day runtime to 21 hours per protein for providing high-scoring compounds to experimental collaborators for validation assays.
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Cruz-Vicente, Pedro, Ana M. Gonçalves, Octávio Ferreira, João A. Queiroz, Samuel Silvestre, Luís A. Passarinha, and Eugenia Gallardo. "Discovery of Small Molecules as Membrane-Bound Catechol-O-methyltransferase Inhibitors with Interest in Parkinson’s Disease: Pharmacophore Modeling, Molecular Docking and In Vitro Experimental Validation Studies." Pharmaceuticals 15, no. 1 (December 31, 2021): 51. http://dx.doi.org/10.3390/ph15010051.
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A pharmacophore-based virtual screening methodology was used to discover new catechol-O-methyltransferase (COMT) inhibitors with interest in Parkinson’s disease therapy. To do so, pharmacophore models were constructed using the structure of known inhibitors and then they were used in a screening in the ZINCPharmer database to discover hit molecules with the desired structural moieties and drug-likeness properties. Following this, the 50 best ranked molecules were submitted to molecular docking to better understand their atomic interactions and binding poses with the COMT (PDB#6I3C) active site. Additionally, the hits’ ADMET properties were also studied to improve the obtained results and to select the most promising compounds to advance for in-vitro studies. Then, the 10 compounds selected were purchased and studied regarding their in-vitro inhibitory potency on human recombinant membrane-bound COMT (MBCOMT), as well as their cytotoxicity in rat dopaminergic cells (N27) and human dermal fibroblasts (NHDF). Of these, the compound ZIN27985035 displayed the best results: For MBCOMT inhibition an IC50 of 17.6 nM was determined, and low cytotoxicity was observed in both cell lines (61.26 and 40.32 μM, respectively). Therefore, the promising results obtained, combined with the structure similarity with commercial COMT inhibitors, can allow for the future development of a potential new Parkinson’s disease drug candidate with improved properties.
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Zhang, Xin, Hui Chen, Hui Lin, Ronglan Wen, and Fan Yang. "High-Throughput Screening and Molecular Dynamics Simulation of Natural Products for the Identification of Anticancer Agents against MCM7 Protein." Applied Bionics and Biomechanics 2022 (September 15, 2022): 1–12. http://dx.doi.org/10.1155/2022/8308192.
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Minichromosome maintenance complex component 7 (MCM7) belongs to the minichromosome maintenance family that is necessary for the initiation of eukaryotic DNA replication. Overexpression of the MCM7 protein is linked to cellular proliferation and is accountable for critical malignancy in many cancers. Mechanistically, the suppression of MCM7 greatly lowers the cellular proliferation associated with cancer. Advances in immunotherapy have revolutionized treatments for many types of cancer. To date, no effective small molecular candidate has been found that can stop the advancement of cancer produced by the MCM7 protein. Here, we present the findings of methods that used a combination of structure-assisted drug design, high-throughput virtual screening, and simulations studies to swiftly generate lead compounds against MCM7 protein. In the current study, we designed efficient compounds that may combat all emerging cancer targeting the common MCM7 protein. For this objective, a molecular docking and molecular dynamics (MD) simulation-based virtual screening of 29,000 NPASS library was carried out. As a consequence of using specific pharmacological, physiological, and ADMET criteria, four new prevailing compounds, NPA000018, NPA000111, NPA00305, and NPA014826, were successfully selected. The MD simulations were also used for a time period of 50 ns to evaluate for stability and dynamics behavior of the compounds. Eventually, compounds NPA000111 and NPA014826 were found to be highly potent against MCM7 protein. According to our results, the selected compounds may be effective in treating certain cancer subtypes, for which additional follow-up experimental validation is recommended.
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Christie, Stephanie, Shashidhar Jatiani, Pei-Yu Kuo, Violetta Leshchenko, Abhijeet Kapoor, Paola Bisignano, Aneel Aggarwal, Marta Filizola, and Samir Parekh. "Inhibiting SOX11-DNA Interaction in Mantle Cell Lymphoma." Blood 128, no. 22 (December 2, 2016): 1840. http://dx.doi.org/10.1182/blood.v128.22.1840.1840.
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Abstract Mantle Cell Lymphoma (MCL) is a fatal subtype of Non-Hodgkins Lymphoma. MCL is characterized by cell cycle dysregulation due to Cyclin D1 (CCND1) overexpression. Murine models overexpressing CCND1 do not develop B-cell proliferation. The SOX11 transcription factor is overexpressed in the majority of nodal human MCL and has been implicated in MCL pathogenesis. We have developed a transgenic Eµ-SOX11-EGFP mouse model that overexpresses SOX11 with aberrant B cell proliferation, lymphadenopathy, spenomegaly and hepatomegaly phenocopying the characteristics of patients with MCL. Using Mass Cytometry (CyTOF), we further characterized this B cell population to be CD23-, CD21/35 dim, CD138-, high surface IgM, and variable IgD expression, an immunophenotype identical to human MCL. SOX11 overexpression drives B cell receptor (BCR) signaling in murine MCL cells, assayed by phosphorylation of p-BTK and p-PLC gamma. To further the study of the role of SOX11 in MCL, compounds inhibiting the binding interaction of SOX11 and DNA are highly desirable and we aim at their discovery by using structure-based virtual screening predictions and experimental validations. No small-molecule inhibitor of SOX11-DNA binding has been developed to date and there is an unmet need in MCL therapeutics for novel and successful treatments. Transcription factors have been considered "undruggable" but since SOX11 binds the minor groove in DNA, we hypothesized that there may be pockets created by SOX11-DNA binding that could be inhibited by small molecules. We therefore built a human SOX11 homology model using the crystal structure of murine SOX4 (98% homology). We first docked molecules from the NIH Chemical Genomics Center (NCGC) Pharmaceutical Collection (NPC). From this virtual screening, 70 compounds were purchased for experimental testing. Using a fluorescence anisotropy assay, Flavitan was identified as a potential inhibitor of the SOX11-DNA interaction. Based on inferences from the predicted binding mode of Flavitan on SOX11, we performed another virtual screening of ~12 million molecules from the ZINC database followed by assaying 26 top-scoring compounds, as well as, 16 derivatives of one of them, for SOX11 binding. We thus discovered two compounds, R and T, with binding constants in the low micromolar range. Importantly, compounds R and T were found not to intercalate DNA in a Topoisomerase I based assay. To assess the biological activity of these molecules, we tested whether BCR signaling in MCL cell lines expressing SOX11 would be inhibited by treatment with these compounds. Compounds R and T significantly inhibited p-BTK as compared to vehicle control. Ibrutinib, an FDA approved BTK inhibitor, was used as a positive control in these experiments. To determine whether BCR pathway inhibition through compounds R and T would be cytotoxic in SOX11 positive MCL cell lines, we treated Z138 and JEKO cells with these small-molecules and assayed Annexin-V and PI staining 48 hours later by flow-cytometry. Our results indicate that compound R has greater single agent cytotoxic activity than compound T and Ibrutinib. Compounds R and T also showed synergistic cytotoxicity in combination with Ibrutinib in SOX11 expressing Z138 and JEKO cells, but not in JVM2, a SOX11 negative MCL cell line. In summary, through a combination of in silico predictions and experimental validation, we have identified small-molecule inhibitors of SOX11-DNA binding as candidate leads for optimization for anti-MCL therapy. Disclosures No relevant conflicts of interest to declare.
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Ademuwagun, Ibitayo Abigail, Gbolahan Oladipupo Oduselu, Solomon Oladapo Rotimi, and Ezekiel Adebiyi. "Pharmacophore-Aided Virtual Screening and Molecular Dynamics Simulation Identifies TrkB Agonists for Treatment of CDKL5-Deficiency Disorders." Bioinformatics and Biology Insights 17 (January 2023): 117793222311582. http://dx.doi.org/10.1177/11779322231158254.
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Therapeutic intervention in cyclin-dependent kinase-like 5 (CDKL5) deficiency disorders (CDDs) has remained a concern over the years. Recent advances into the mechanistic interplay of signalling pathways has revealed the role of deficient tropomyosin receptor kinase B (TrkB)/phospholipase C γ1 signalling cascade in CDD. Novel findings showed that in vivo administration of a TrkB agonist, 7,8-dihydroxyflavone (7,8-DHF), resulted in a remarkable reversal in the molecular pathologic mechanisms underlying CDD. Owing to this discovery, this study aimed to identify more potent TrkB agonists than 7,8-DHF that could serve as alternatives or combinatorial drugs towards effective management of CDD. Using pharmacophore modelling and multiple database screening, we identified 691 compounds with identical pharmacophore features with 7,8-DHF. Virtual screening of these ligands resulted in identification of at least 6 compounds with better binding affinities than 7,8-DHF. The in silico pharmacokinetic and ADMET studies of the compounds also indicated better drug-like qualities than those of 7,8-DHF. Postdocking analyses and molecular dynamics simulations of the best hits, 6-hydroxy-10-(2-oxo-1-azatricyclo[7.3.1.05,13]trideca-3,5(13),6,8-tetraen-3-yl)-8-oxa-13,14,16-triazatetracyclo[7.7.0.02,7.011,15]hexadeca-1,3,6,9,11,15-hexaen-5-one (PubChem: 91637738) and 6-hydroxy-10-(8-methyl-2-oxo-1H-quinolin-3-yl)-8-oxa-13,14,16-triazatetracyclo[7.7.0.02,7.011,15]hexadeca-1,3,6,9,11,15-hexaen-5-one (PubChem ID: 91641310), revealed unique ligand interactions, validating the docking findings. We hereby recommend experimental validation of the best hits in CDKL5 knock out models before consideration as drugs in CDD management.
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Casañola-Martín, Gerardo M., Yovani Marrero-Ponce, Mahmud Tareq Hassan Khan, Francisco Torrens, Facundo Pérez-Giménez, and Antonio Rescigno. "Atom- and Bond-Based 2D TOMOCOMD-CARDD Approach and Ligand-Based Virtual Screening for the Drug Discovery of New Tyrosinase Inhibitors." Journal of Biomolecular Screening 13, no. 10 (November 17, 2008): 1014–24. http://dx.doi.org/10.1177/1087057108326078.
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Two-dimensional atom- and bond-based TOMOCOMD-CARDD descriptors and linear discriminant analysis (LDA) are used in this report to perform a quantitative structure-activity relationship (QSAR) study of tyrosinase-inhibitory activity. A database of inhibitors of the enzyme is collected for this study, within 246 highly dissimilar molecules presenting antityrosinase activity. In total, 7 discriminant functions are obtained by using the whole set of atom- and bond-based 2D indices. All the LDA-based QSAR models show accuracies above 90% in the training set and values of the Matthews correlation coefficient ( C) varying from 0.85 to 0.90. The external validation set shows globally good classifications between 89% and 91% and C values ranging from 0.75 to 0.81. Finally, QSAR models are used in the selection/identification of the 20 new dicoumarins subset to search for tyrosinase inhibitory activity. Theoretical and experimental results show good correspondence between one another. It is important to remark that most compounds in this series exhibit a more potent inhibitory activity against the mushroom tyrosinase enzyme than the reference compound, Kojic acid (IC50 = 16.67 μM), resulting in a novel nucleus base (lead) with antityrosinase activity, and this could serve as a starting point for the drug discovery of novel tyrosinase inhibitor lead compounds. ( Journal of Biomolecular Screening 2008:1014-1024)
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Gómez-Ganau, Sergi, Josefa Castillo, Andrés Cervantes, Jesus Vicente de Julián-Ortiz, and Rafael Gozalbes. "Computational Evaluation and In Vitro Validation of New Epidermal Growth Factor Receptor Inhibitors." Current Topics in Medicinal Chemistry 20, no. 18 (August 24, 2020): 1628–39. http://dx.doi.org/10.2174/1568026620666200603122726.
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Background: The Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein that acts as a receptor of extracellular protein ligands of the epidermal growth factor (EGF/ErbB) family. It has been shown that EGFR is overexpressed by many tumours and correlates with poor prognosis. Therefore, EGFR can be considered as a very interesting therapeutic target for the treatment of a large variety of cancers such as lung, ovarian, endometrial, gastric, bladder and breast cancers, cervical adenocarcinoma, malignant melanoma and glioblastoma. Methods: We have followed a structure-based virtual screening (SBVS) procedure with a library composed of several commercial collections of chemicals (615,462 compounds in total) and the 3D structure of EGFR obtained from the Protein Data Bank (PDB code: 1M17). The docking results from this campaign were then ranked according to the theoretical binding affinity of these molecules to EGFR, and compared with the binding affinity of erlotinib, a well-known EGFR inhibitor. A total of 23 top-rated commercial compounds displaying potential binding affinities similar or even better than erlotinib were selected for experimental evaluation. In vitro assays in different cell lines were performed. A preliminary test was carried out with a simple and standard quick cell proliferation assay kit, and six compounds showed significant activity when compared to positive control. Then, viability and cell proliferation of these compounds were further tested using a protocol based on propidium iodide (PI) and flow cytometry in HCT116, Caco-2 and H358 cell lines. Results: The whole six compounds displayed good effects when compared with erlotinib at 30 μM. When reducing the concentration to 10μM, the activity of the 6 compounds depends on the cell line used: the six compounds showed inhibitory activity with HCT116, two compounds showed inhibition with Caco-2, and three compounds showed inhibitory effects with H358. At 2 μM, one compound showed inhibiting effects close to those from erlotinib. Conclusion: Therefore, these compounds could be considered as potential primary hits, acting as promising starting points to expand the therapeutic options against a wide range of cancers.
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Altharawi, Ali. "Targeting Toxoplasma gondii ME49 TgAPN2: A Bioinformatics Approach for Antiparasitic Drug Discovery." Molecules 28, no. 7 (April 3, 2023): 3186. http://dx.doi.org/10.3390/molecules28073186.
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As fewer therapeutic options are available for treating toxoplasmosis, newer antiparasitic drugs that can block TgAPN2 M1 aminopeptidase are of significant value. Herein, we employed several computer-aided drug-design approaches with the objective of identifying drug molecules from the Asinex library with stable conformation and binding energy scores. By a structure-based virtual screening process, three molecules—LAS_52160953, LAS_51177972, and LAS_52506311—were identified as promising candidates, with binding affinity scores of −8.6 kcal/mol, −8.5 kcal/mol, and −8.3 kcal/mol, respectively. The compounds produced balanced interacting networks of hydrophilic and hydrophobic interactions, vital for holding the compounds at the docked cavity and stable binding conformation. The docked compound complexes with TgAPN2 were further subjected to molecular dynamic simulations that revealed mean RMSD for the LAS_52160953 complex of 1.45 Å), LAS_51177972 complex 1.02 Å, and LAS_52506311 complex 1.087 Å. Another round of binding free energy validation by MM-GBSA/MM-PBSA was done to confirm docking and simulation findings. The analysis predicted average MM-GBSA value of <−36 kcal/mol and <−35 kcal/mol by MM-PBSA. The compounds were further classified as appropriate candidates to be used as drug-like molecules and showed favorable pharmacokinetics. The shortlisted compounds showed promising biological potency against the TgAPN2 enzyme and may be used in experimental validation. They may also serve as parent structures to design novel derivatives with enhanced biological potency.
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Bhojwani, Heena R., and Urmila J. Joshi. "Homology Modelling, Docking-based Virtual Screening, ADME Properties, and Molecular Dynamics Simulation for Identification of Probable Type II Inhibitors of AXL Kinase." Letters in Drug Design & Discovery 19, no. 3 (March 2022): 214–41. http://dx.doi.org/10.2174/1570180818666211004102043.
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Background: AXL kinase is an important member of the TAM family for kinases which is involved in most cancers. Considering its role in different cancers due to its pro-tumorigenic effects and its involvement in the resistance, it has gained importance recently. Majority of research carried out is on Type I inhibitors and limited studies have been carried out for Type II inhibitors. Taking this into consideration, we have attempted to build Homology models to identify the Type II inhibitors for the AXL kinase. Methods: Homology Models for DFG-out C-helix-in/out state were developed using SWISS Model, PRIMO, and Prime. These models were validated by different methods and further evaluated for stability by molecular dynamics simulation using Desmond software. Selected models PED1-EB and PEDI1-EB were used for the docking-based virtual screening of four compound libraries using Glide software. The hits identified were subjected to interaction analysis and shortlisted compounds were subjected to Prime MM-GBSA studies for energy calculation. These compounds were also docked in the DFG-in state to check for binding and elimination of any compounds that may not be Type II inhibitors. The Prime energies were calculated for these complexes as well and some compounds were eliminated. ADMET studies were carried out using Qikprop. Some selected compounds were subjected to molecular dynamics simulation using Desmond for evaluating the stability of the complexes. Results: Out of 78 models inclusive of both DFG-out C-helix-in and DFG-out C-helix-out, 5 models were identified after different types of evaluation as well as validation studies. 1 model representing each type (PED1-EB and PEDI1-EB) was selected for the screening studies. The screening studies resulted in the identification of 29 compounds from the screen on PED1-EB and 10 compounds from the screen on PEDI1-EB. Hydrogen bonding interactions with Pro621, Met623, and Asp690 were observed for these compounds primarily. In some compounds, hydrogen bonding with Leu542, Glu544, Lys567, and Asn677 as well as pi-pi stacking interactions with either Phe622 or Phe691 were also seen. 4 compounds identified from PED1-EB screen were subjected to molecular dynamics simulation and their interactions were found to be consistent during the simulation. 2 compounds identified from PEDI1-EB screen were also subjected to the simulation studies, however, their interactions with Asp690 were not observed for a significant time and in both cases differed from the docked pose. Conclusion: Multiple models of DFG-out conformations of AXL kinase were built, validated and used for virtual screening. Different compounds were identified in the virtual screening, which may possibly act as Type II inhibitors for AXL kinase. Some more experimental studies can be done to validate these findings in future. This study will play a guiding role in the further development of the newer Type II inhibitors of the AXL kinase for the probable treatment of cancer.
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Bhojwani, Heena R., and Urmila J. Joshi. "Homology Modelling, Docking-based Virtual Screening, ADME Properties, and Molecular Dynamics Simulation for Identification of Probable Type II Inhibitors of AXL Kinase." Letters in Drug Design & Discovery 19, no. 3 (March 2022): 214–41. http://dx.doi.org/10.2174/1570180818666211004102043.
Full textAbstract:
Background: AXL kinase is an important member of the TAM family for kinases which is involved in most cancers. Considering its role in different cancers due to its pro-tumorigenic effects and its involvement in the resistance, it has gained importance recently. Majority of research carried out is on Type I inhibitors and limited studies have been carried out for Type II inhibitors. Taking this into consideration, we have attempted to build Homology models to identify the Type II inhibitors for the AXL kinase. Methods: Homology Models for DFG-out C-helix-in/out state were developed using SWISS Model, PRIMO, and Prime. These models were validated by different methods and further evaluated for stability by molecular dynamics simulation using Desmond software. Selected models PED1-EB and PEDI1-EB were used for the docking-based virtual screening of four compound libraries using Glide software. The hits identified were subjected to interaction analysis and shortlisted compounds were subjected to Prime MM-GBSA studies for energy calculation. These compounds were also docked in the DFG-in state to check for binding and elimination of any compounds that may not be Type II inhibitors. The Prime energies were calculated for these complexes as well and some compounds were eliminated. ADMET studies were carried out using Qikprop. Some selected compounds were subjected to molecular dynamics simulation using Desmond for evaluating the stability of the complexes. Results: Out of 78 models inclusive of both DFG-out C-helix-in and DFG-out C-helix-out, 5 models were identified after different types of evaluation as well as validation studies. 1 model representing each type (PED1-EB and PEDI1-EB) was selected for the screening studies. The screening studies resulted in the identification of 29 compounds from the screen on PED1-EB and 10 compounds from the screen on PEDI1-EB. Hydrogen bonding interactions with Pro621, Met623, and Asp690 were observed for these compounds primarily. In some compounds, hydrogen bonding with Leu542, Glu544, Lys567, and Asn677 as well as pi-pi stacking interactions with either Phe622 or Phe691 were also seen. 4 compounds identified from PED1-EB screen were subjected to molecular dynamics simulation and their interactions were found to be consistent during the simulation. 2 compounds identified from PEDI1-EB screen were also subjected to the simulation studies, however, their interactions with Asp690 were not observed for a significant time and in both cases differed from the docked pose. Conclusion: Multiple models of DFG-out conformations of AXL kinase were built, validated and used for virtual screening. Different compounds were identified in the virtual screening, which may possibly act as Type II inhibitors for AXL kinase. Some more experimental studies can be done to validate these findings in future. This study will play a guiding role in the further development of the newer Type II inhibitors of the AXL kinase for the probable treatment of cancer.
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Dhasmana, Anupam, Vivek K. Kashyap, Swati Dhasmana, Sudhir Kotnala, Shafiul Haque, Ghulam Md Ashraf, Meena Jaggi, Murali M. Yallapu, and Subhash C. Chauhan. "Neutralization of SARS-CoV-2 Spike Protein via Natural Compounds: A Multilayered High Throughput Virtual Screening Approach." Current Pharmaceutical Design 26, no. 41 (December 12, 2020): 5300–5309. http://dx.doi.org/10.2174/1381612826999200820162937.
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Background: Previously human society has faced various unprecedented pandemics in the history and viruses have majorly held the responsibilities of those outbreaks. Furthermore, due to amplified global connection and speedy modernization, epidemic outbreaks caused by novel and re-emerging viruses signify potential risk to community health. Despite great advancements in immunization and drug discovery processes, various viruses still lack prophylactic vaccines and efficient antiviral therapies. Although, vaccine is a prophylaxes option, but it cannot be applied to infected patients, hence therapeutic interventions are urgently needed to control the ongoing global SARS- CoV-2 pandemic condition. To spot the novel antiviral therapy is of decisive importance and Mother Nature is an excellent source for such discoveries. Methodology: In this article, prompt high through-put virtual screening for vetting the best possible drug candidates from natural compounds’ databases has been implemented. Herein, time tested rigorous multi-layered drug screening process to narrow down 66,969 natural compounds for the identification of potential lead(s) is implemented. Druggability parameters, different docking approaches and neutralization tendency of the natural products were employed in this study to screen the best possible natural compounds from the digital libraries. Conclusion: The results of this study conclude that compounds PALA and HMCA are potential inhibitors of SARS-CoV-2 spike protein and can be further explored for experimental validation. Overall, the methodological approach reported in this article can be suitably used to find the potential drug candidates against SARS-CoV2 in the burning situation of COVID-19 with less expenditure and a concise span of time.
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Alturki, Norah A., Mutaib M. Mashraqi, Ahmad Alzamami, Youssef S. Alghamdi, Afaf A. Alharthi, Saeed A. Asiri, Shaban Ahmad, and Saleh Alshamrani. "In-Silico Screening and Molecular Dynamics Simulation of Drug Bank Experimental Compounds against SARS-CoV-2." Molecules 27, no. 14 (July 8, 2022): 4391. http://dx.doi.org/10.3390/molecules27144391.
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For the last few years, the world has been going through a difficult time, and the reason behind this is severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), one of the significant members of the Coronaviridae family. The major research groups have shifted their focus towards finding a vaccine and drugs against SARS-CoV-2 to reduce the infection rate and save the life of human beings. Even the WHO has permitted using certain vaccines for an emergency attempt to cut the infection curve down. However, the virus has a great sense of mutation, and the vaccine’s effectiveness remains questionable. No natural medicine is available at the community level to cure the patients for now. In this study, we have screened the vast library of experimental drugs of Drug Bank with Schrodinger’s maestro by using three algorithms: high-throughput virtual screening (HTVS), standard precision, and extra precise docking followed by Molecular Mechanics/Generalized Born Surface Area (MMGBSA). We have identified 3-(7-diaminomethyl-naphthalen-2-YL)-propionic acid ethyl ester and Thymidine-5′-thiophosphate as potent inhibitors against the SARS-CoV-2, and both drugs performed impeccably and showed stability during the 100 ns molecular dynamics simulation. Both of the drugs are among the category of small molecules and have an acceptable range of ADME properties. They can be used after their validation in in-vitro and in-vivo conditions.
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Modanwal, Shristi, Vishal Mugetia, and Nidhi Mishra. "In silico screening of potential drug against MDR genes (PfCRT/PfMDR1) responsible for Malaria." Research Journal of Chemistry and Environment 26, no. 11 (October 25, 2022): 76–100. http://dx.doi.org/10.25303/2611rjce0760100.
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The increasing availability of drug-resistant Plasmodium falciparum infections is putting a strain on the accessibility of potent, safe and cost-effective anti-malarial treatments, necessitating the development of new anti-malarial drug. Malaria deaths were estimated to be around 409000 in 2019. The present study sets to identify novel antimalarial compounds and the virtual screening study reveals that the designed compounds bind more effectively to Plasmodium falciparum chloroquine resistance transporter (PfCRT) and Plasmodium falciparum Multidrug Resistant1 (PfMDR1) than the known inhibitors. Marvin JS was used to design the chemical structure of the molecules and the molecular docking of 75 designed molecules with PfCRT and PfMDR1 was performed to study the interaction between the small molecule and the proteins. The top docked scoring compounds with the respective proteins were subjected to molecular dynamic simulation to study their interaction stability. The ADME/T (absorption, distribution, metabolism and excretion/toxicity) properties of those molecules were also studied and the majority of the properties was found to be within acceptable ranges. After experimental validation to confirm the findings, the screened molecules could be used as potential anti-malarial drugs.
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Faheem, Muhammad, and Syed Babar Jamal. "Identification of Zika Virus NS5 Novel Inhibitors through Virtual Screening and Docking Studies." Life and Science 1, no. 1 (February 13, 2020): 5. http://dx.doi.org/10.37185/lns.1.1.42.
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Objective: Screening of ZINC inhibitors library for Zika virus (ZIKV) Non-Structural 5 (NS5) protein as potential drug target. Study Design: Cross sectional.Place and Duration of Study: The study was carried out at Department of Biological Sciences of National University of Medical Sciences, Rawalpindi, Pakistan from December 2018 to March 2019.Materials and Methods: NS5 protein was obtained from Protein databank (PDB ID: 5TMH) and screened against ZINC library of 11,193 drug-like molecules for NS5 and 3 ligands were identified based on optimum binding energy. MOE, PyMOL and CLUSTALW were used for docking studies and structural analysis.Results: Out of 11, 193 compounds, three ligands were observed to interact with residues of the Methyl Transferase (MT) domain of NS5. These ligands fit in the MT domain by making hydrogen and hydrophobic interactions in the active site and S-adenosyl-methionine (SAM) binding pocket.Conclusion: Hence, upon experimental validation, these ligands can be utilized as potential inhibitors against NS5 MT activity to control ZIKV viral replication and ultimately control the disease. How to cite this: Faheem M, Jamal SB. Identification of Zika Virus NS5 Novel Inhibitors through Virtual Screening and Docking Studies. Life and Science. 2020; 1(1): 3-7. doi:https://doi.org/10.37185/L&S.1.1.42
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Szal, Tania, Shweta Singh Chauhan, Philipp Lewe, Fatima-Zahra Rachad, Marina Madre, Laura Paunina, Susanne Witt, Ramakrishnan Parthasarathi, and Björn Windshügel. "Efflux Pump-Binding 4(3-Aminocyclobutyl)Pyrimidin-2-Amines Are Colloidal Aggregators." Biomolecules 13, no. 6 (June 16, 2023): 1000. http://dx.doi.org/10.3390/biom13061000.
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Efflux pumps are a relevant factor in antimicrobial resistance. In E. coli, the tripartite efflux pump AcrAB-TolC removes a chemically diverse set of antibiotics from the bacterium. Therefore, small molecules interfering with efflux pump function are considered adjuvants for improving antimicrobial therapies. Several compounds targeting the periplasmic adapter protein AcrA and the efflux pump AcrB have been identified to act synergistically with different antibiotics. Among those, several 4(3-aminocyclobutyl)pyrimidin-2-amines have been shown to bind to both proteins. In this study, we intended to identify analogs of these substances with improved binding affinity to AcrA using virtual screening followed by experimental validation. While we succeeded in identifying several compounds showing a synergistic effect with erythromycin on E. coli, biophysical studies suggested that 4(3-aminocyclobutyl)pyrimidin-2-amines form colloidal aggregates that do not bind specifically to AcrA. Therefore, these substances are not suited for further development. Our study emphasizes the importance of implementing additional control experiments to identify aggregators among bioactive compounds.
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Martins, Fábio G., André Melo, and Sérgio F. Sousa. "Identification of New Potential Inhibitors of Quorum Sensing through a Specialized Multi-Level Computational Approach." Molecules 26, no. 9 (April 29, 2021): 2600. http://dx.doi.org/10.3390/molecules26092600.
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Biofilms are aggregates of microorganisms anchored to a surface and embedded in a self-produced matrix of extracellular polymeric substances and have been associated with 80% of all bacterial infections in humans. Because bacteria in biofilms are less amenable to antibiotic treatment, biofilms have been associated with developing antibiotic resistance, a problem that urges developing new therapeutic options and approaches. Interfering with quorum-sensing (QS), an important process of cell-to-cell communication by bacteria in biofilms is a promising strategy to inhibit biofilm formation and development. Here we describe and apply an in silico computational protocol for identifying novel potential inhibitors of quorum-sensing, using CviR—the quorum-sensing receptor from Chromobacterium violaceum—as a model target. This in silico approach combines protein-ligand docking (with 7 different docking programs/scoring functions), receptor-based virtual screening, molecular dynamic simulations, and free energy calculations. Particular emphasis was dedicated to optimizing the discrimination ability between active/inactive molecules in virtual screening tests using a target-specific training set. Overall, the optimized protocol was used to evaluate 66,461 molecules, including those on the ZINC/FDA-Approved database and to the Mu.Ta.Lig Virtual Chemotheca. Multiple promising compounds were identified, yielding good prospects for future experimental validation and for drug repurposing towards QS inhibition.
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Scarano, Naomi, Elena Abbotto, Francesca Musumeci, Annalisa Salis, Chiara Brullo, Paola Fossa, Silvia Schenone, Santina Bruzzone, and Elena Cichero. "Virtual Screening Combined with Enzymatic Assays to Guide the Discovery of Novel SIRT2 Inhibitors." International Journal of Molecular Sciences 24, no. 11 (May 27, 2023): 9363. http://dx.doi.org/10.3390/ijms24119363.
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Sirtuin isoform 2 (SIRT2) is one of the seven sirtuin isoforms present in humans, being classified as class III histone deacetylases (HDACs). Based on the high sequence similarity among SIRTs, the identification of isoform selective modulators represents a challenging task, especially for the high conservation observed in the catalytic site. Efforts in rationalizing selectivity based on key residues belonging to the SIRT2 enzyme were accompanied in 2015 by the publication of the first X-ray crystallographic structure of the potent and selective SIRT2 inhibitor SirReal2. The subsequent studies led to different experimental data regarding this protein in complex with further different chemo-types as SIRT2 inhibitors. Herein, we reported preliminary Structure-Based Virtual Screening (SBVS) studies using a commercially available library of compounds to identify novel scaffolds for the design of new SIRT2 inhibitors. Biochemical assays involving five selected compounds allowed us to highlight the most effective chemical features supporting the observed SIRT2 inhibitory ability. This information guided the following in silico evaluation and in vitro testing of further compounds from in-house libraries of pyrazolo-pyrimidine derivatives towards novel SIRT2 inhibitors (1–5). The final results indicated the effectiveness of this scaffold for the design of promising and selective SIRT2 inhibitors, featuring the highest inhibition among the tested compounds, and validating the applied strategy.
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Jabeen, Amara, Claire A. de March, Hiroaki Matsunami, and Shoba Ranganathan. "Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors." International Journal of Molecular Sciences 22, no. 21 (October 26, 2021): 11546. http://dx.doi.org/10.3390/ijms222111546.
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Olfactory receptors (ORs) constitute the largest superfamily of G protein-coupled receptors (GPCRs). ORs are involved in sensing odorants as well as in other ectopic roles in non-nasal tissues. Matching of an enormous number of the olfactory stimulation repertoire to its counterpart OR through machine learning (ML) will enable understanding of olfactory system, receptor characterization, and exploitation of their therapeutic potential. In the current study, we have selected two broadly tuned ectopic human OR proteins, OR1A1 and OR2W1, for expanding their known chemical space by using molecular descriptors. We present a scheme for selecting the optimal features required to train an ML-based model, based on which we selected the random forest (RF) as the best performer. High activity agonist prediction involved screening five databases comprising ~23 M compounds, using the trained RF classifier. To evaluate the effectiveness of the machine learning based virtual screening and check receptor binding site compatibility, we used docking of the top target ligands to carefully develop receptor model structures. Finally, experimental validation of selected compounds with significant docking scores through in vitro assays revealed two high activity novel agonists for OR1A1 and one for OR2W1.
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Alsaady, Isra M., Leena H. Bajrai, Thamir A. Alandijany, Hattan S. Gattan, Mai M. El-Daly, Sarah A. Altwaim, Rahaf T. Alqawas, Vivek Dhar Dwivedi, and Esam I. Azhar. "Cheminformatics Strategies Unlock Marburg Virus VP35 Inhibitors from Natural Compound Library." Viruses 15, no. 8 (August 15, 2023): 1739. http://dx.doi.org/10.3390/v15081739.
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The Ebola virus and its close relative, the Marburg virus, both belong to the family Filoviridae and are highly hazardous and contagious viruses. With a mortality rate ranging from 23% to 90%, depending on the specific outbreak, the development of effective antiviral interventions is crucial for reducing fatalities and mitigating the impact of Marburg virus outbreaks. In this investigation, a virtual screening approach was employed to evaluate 2042 natural compounds for their potential interactions with the VP35 protein of the Marburg virus. Average and worst binding energies were calculated for all 20 poses, and compounds that exhibited binding energies <−6 kcal/mol in both criteria were selected for further analysis. Based on binding energies, only six compounds (Estradiol benzoate, INVEGA (paliperidone), Isosilybin, Protopanaxadiol, Permethrin, and Bufalin) were selected for subsequent investigations, focusing on interaction analysis. Among these selected compounds, Estradiol benzoate, INVEGA (paliperidone), and Isosilybin showed strong hydrogen bonds, while the others did not. In this study, the compounds Myricetin, Isosilybin, and Estradiol benzoate were subjected to a molecular dynamics (MD) simulation and free binding energy calculation using MM/GBSA analysis. The reference component Myricetin served as a control. Estradiol benzoate exhibited the most stable and consistent root-mean-square deviation (RMSD) values, whereas Isosilybin showed significant fluctuations in RMSD. The compound Estradiol benzoate exhibited the lowest ΔG binding free energy (−22.89 kcal/mol), surpassing the control compound’s binding energy (−9.29 kcal/mol). Overall, this investigation suggested that Estradiol benzoate possesses favorable binding free energies, indicating a potential inhibitory mechanism against the VP35 protein of the Marburg virus. The study proposes that these natural compounds could serve as a therapeutic option for preventing Marburg virus infection. However, experimental validation is required to further corroborate these findings.
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Ferreira, Glaucio Monteiro, Thales Kronenberger, Vinicius Gonçalves Maltarollo, Antti Poso, Fernando de Moura Gatti, Vitor Medeiros Almeida, Sandro Roberto Marana, et al. "Trypanosoma cruzi Sirtuin 2 as a Relevant Druggable Target: New Inhibitors Developed by Computer-Aided Drug Design." Pharmaceuticals 16, no. 3 (March 10, 2023): 428. http://dx.doi.org/10.3390/ph16030428.
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Trypanosoma cruzi, the etiological agent of Chagas disease, relies on finely coordinated epigenetic regulation during the transition between hosts. Herein we targeted the silent information regulator 2 (Sir2) enzyme, a NAD+-dependent class III histone deacetylase, to interfere with the parasites’ cell cycle. A combination of molecular modelling with on-target experimental validation was used to discover new inhibitors from commercially available compound libraries. We selected six inhibitors from the virtual screening, which were validated on the recombinant Sir2 enzyme. The most potent inhibitor (CDMS-01, IC50 = 40 μM) was chosen as a potential lead compound.
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Di Stefano, Miriana, Salvatore Galati, Gabriella Ortore, Isabella Caligiuri, Flavio Rizzolio, Costanza Ceni, Simone Bertini, et al. "Machine Learning-Based Virtual Screening for the Identification of Cdk5 Inhibitors." International Journal of Molecular Sciences 23, no. 18 (September 13, 2022): 10653. http://dx.doi.org/10.3390/ijms231810653.
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Cyclin-dependent kinase 5 (Cdk5) is an atypical proline-directed serine/threonine protein kinase well-characterized for its role in the central nervous system rather than in the cell cycle. Indeed, its dysregulation has been strongly implicated in the progression of synaptic dysfunction and neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), and also in the development and progression of a variety of cancers. For this reason, Cdk5 is considered as a promising target for drug design, and the discovery of novel small-molecule Cdk5 inhibitors is of great interest in the medicinal chemistry field. In this context, we employed a machine learning-based virtual screening protocol with subsequent molecular docking, molecular dynamics simulations and binding free energy evaluations. Our virtual screening studies resulted in the identification of two novel Cdk5 inhibitors, highlighting an experimental hit rate of 50% and thus validating the reliability of the in silico workflow. Both identified ligands, compounds CPD1 and CPD4, showed a promising enzyme inhibitory activity and CPD1 also demonstrated a remarkable antiproliferative activity in ovarian and colon cancer cells. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent Cdk5 inhibitors.
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S. Pai, K. Usha, Yadav D. Bodke, Suman Manandhar, and K. Sreedhara Ranganath Pai. "in silico-Based Virtual Screening and Molecular Docking Analysis of Phytochemicals obtained from Methanolic Extract of Cleome viscosa Linn. by GC-MS Method for its Anticancer Activity." Asian Journal of Chemistry 33, no. 12 (2021): 2943–52. http://dx.doi.org/10.14233/ajchem.2021.23384.
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Cleome viscosa belonging to the family Capparidaceae, is a weed with ethano-botanical value found in India. In the present investigation, methanolic extract of Cleome viscosa was analyzed by gas chromatography-mass spectrometry (GC-MS) to identify the important phytochemical constituents. The GC-MS analysis of methanol from whole plant of Cleome viscosa detected the presence of 78 phytochemical compounds. Quantitative phytochemical evaluation of the methanolic extract of Cleome viscosa was performed. These identified compounds were analyzed for their anticancer activity through in silico molecular docking studies. Computation based in silico docking studies were done using maestro interface. Three protein, poly (ADP-ribose) polymerase-1 (PARP-1), epidermal growth factor receptor (EGFR), human papilloma virus (HPV) specific to different cancers were selected for screening of these phytochemicals. Phytomolecules with better activity and binding were shortlisted after XP mode of docking. The dock score, glide energy and 2D binding interactions of the top five phytochemicals with three selected proteins have been discussed. The identified hit could be a potent inhibitor these proteins that further requires experimental validation.
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Bajrai, Leena H., Azzah S. Alharbi, Mai M. El-Day, Abrar G. Bafaraj, Vivek Dhar Dwivedi, and Esam I. Azhar. "Identification of Antiviral Compounds against Monkeypox Virus Profilin-like Protein A42R from Plantago lanceolata." Molecules 27, no. 22 (November 9, 2022): 7718. http://dx.doi.org/10.3390/molecules27227718.
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Infections caused by the monkeypox virus (MPXV) have continued to be transmitted significantly in recent years. However, understanding the transmission mechanism, risk factors, and consequences of infection are still limited. Structure-based drug design for MPXV is at an early stage due to the availability of protein structures that have been determined experimentally. However, the structure of the A42R profilin-like protein of MPXV has been solved and submitted to the structure database. This study illustrated an in silico structure-based approach to identify the potential hit compound against A42R of MPXV. Here, 65 Plantago lanceolata compounds were computationally screened against A42R of MPXV. Virtual screening identified top five hits (i) Luteolin 7,3′-Diglucuronide (PubChem ID: 44258091), (ii) Luteolin 7-Glucuronide-3′-Glucoside (PubChem ID: 44258090), (iii) Plantagoside (PubChem ID: 174157), (iv) Narcissoside (PubChem ID: 5481663), and (v) (AlphaE,8S,9R)-N-(3,4-Dihydroxyphenethyl)-8-[(3,4-Dihydroxyphenethyl)Carbamoyl]-9-(1,3-Benzodioxole-5-Yl)-3aalpha,7aalpha-Ethano-1,3-Benzodioxole-5-Acrylamide (PubChem ID: 101131595), with binding energy <−9.0 kcal/mol that was further validated by re-docking and molecular dynamic (MD) simulation. Interaction analysis of re-docked poses confirmed the binding of these top hits to the A42R protein as reported in the reference compound, including active residues ARG114, ARG115, and ARG119. Further, MD simulation and post-simulation analysis support Plantagoside and Narcissoside for substantial stability in the binding pocket of viral protein contributed by hydrogen and hydrophobic interactions. The compounds can be considered for further optimisation and in vitro experimental validation for anti-monkeypox drug development.
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Islam, Md Ataul, Shovonlal Bhowmick, and Achintya Saha. "Identification of Selective Receptor Modulators Using Pharmacoinformatics Approaches for Therapeutic Application in Estrogen Therapy." International Journal of Quantitative Structure-Property Relationships 4, no. 2 (April 2019): 52–81. http://dx.doi.org/10.4018/ijqspr.2019040103.
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Pharmacoinformatics strategies have been applied to explore promising selective estrogen receptor (ER) modulators (SERMs). A set of non-steroidal ligands was considered for both ERα and ERβ subtypes. Best pharmacophore models revealed with importance of hydrogen bond acceptor and hydrophobicity for both subtypes, along with an aromatic ring and hydrogen bond donor for α and β subtypes, respectively. Both models were validated, and further considered for virtual screening of National Cancer Institute database. Initial hits were sorted with a number of criteria, and finally the molecules have been proposed as promising SERMs. A molecular docking study explained that screened ligands formed a number of binding interactions with both ERs. The subtype receptors in complex with active and screened compounds were considered for molecular simulations to compare stability of the complexes. An analysis of binding energy found that screened ligands hold a strong affinity towards the selective receptor cavity. The proposed ligands might be promising leads for estrogen therapy after experimental validation tests.
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Islam, Md Ataul, V. P. Subramanyam Rallabandi, Sameer Mohammed, Sridhar Srinivasan, Sathishkumar Natarajan, Dawood Babu Dudekula, and Junhyung Park. "Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches." International Journal of Molecular Sciences 22, no. 20 (October 17, 2021): 11191. http://dx.doi.org/10.3390/ijms222011191.
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Cardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. β-Adrenergic receptors (β1-AR and β2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning (ML), and a ligand-based similarity search were conducted for the PubChem database against both β1- and β2-AR. Initially, all docked molecules were screened using the threshold binding energy value. Molecules with a better binding affinity were further used for segregation as active and inactive through ML. The pharmacokinetic assessment was carried out on molecules retained in the above step. Further, similarity searching of the ChEMBL and DrugBank databases was performed. From detailed analysis of the above data, four compounds for each of β1- and β2-AR were found to be promising in nature. A number of critical ligand-binding amino acids formed potential hydrogen bonds and hydrophobic interactions. Finally, a molecular dynamics (MD) simulation study of each molecule bound with the respective target was performed. A number of parameters obtained from the MD simulation trajectories were calculated and substantiated the stability between the protein-ligand complex. Hence, it can be postulated that the final molecules might be crucial for CDs subjected to experimental validation.
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Nasar, Nazeer Mohamed, Michael Samuel, Porkodi Jayaraman, Freeda Selva Sheela, and Natarajan Raman. "Virtual and in vitro, in vivo Screening of Transition Metal Complexes of N,N-Chelating Ligand: Experimental and Theoretical Investigations." Asian Journal of Chemistry 35, no. 3 (2023): 639–48. http://dx.doi.org/10.14233/ajchem.2023.27565.
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Several transition metal complexes [ML(phth)], where M = Cu(II), Zn(II), Co(II) and Ni(II), X = phthalic acid and L = Schiff base generated from benzene-1,2,diamine and 4-chlorobenzaldehyde, were synthesized and characterized by IR, UV-Vis, 1H NMR, 13C NMR and mass spectra. According to the physico-chemical studies, all the synthesized metal(II) complexes have a square planar geometry. The DNA nuclease activity of the synthesized metal complexes was investigated using UV absorption assay and viscosity, validating the intercalative mechanism of binding. Antimicrobial activity of the ligand and its metal(II) complexes on various microorganisms was also investigated. The optimal form and biological accessibility of the metal complexes were examined by the Gaussian 09W algorithm. These compounds were screened for drug-like activity and pharmacokinetic studies using the free SWISS ADME online software. The positive outcomes of molecular docking studies on the COVID-19 virus and cancer DNA are interesting.
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Shahid, Fareena, Noreen, Roshan Ali, Syed Lal Badshah, Syed Babar Jamal, Riaz Ullah, Ahmed Bari, Hafiz Majid Mahmood, Muhammad Sohaib, and Siddique Akber Ansari. "Identification of Potential HCV Inhibitors Based on the Interaction of Epigallocatechin-3-Gallate with Viral Envelope Proteins." Molecules 26, no. 5 (February 26, 2021): 1257. http://dx.doi.org/10.3390/molecules26051257.
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Hepatitis C is affecting millions of people around the globe annually, which leads to death in very high numbers. After many years of research, hepatitis C virus (HCV) remains a serious threat to the human population and needs proper management. The in silico approach in the drug discovery process is an efficient method in identifying inhibitors for various diseases. In our study, the interaction between Epigallocatechin-3-gallate, a component of green tea, and envelope glycoprotein E2 of HCV is evaluated. Epigallocatechin-3-gallate is the most promising polyphenol approved through cell culture analysis that can inhibit the entry of HCV. Therefore, various in silico techniques have been employed to find out other potential inhibitors that can behave as EGCG. Thus, the homology modelling of E2 protein was performed. The potential lead molecules were predicted using ligand-based as well as structure-based virtual screening methods. The compounds obtained were then screened through PyRx. The drugs obtained were ranked based on their binding affinities. Furthermore, the docking of the topmost drugs was performed by AutoDock Vina, while its 2D interactions were plotted in LigPlot+. The lead compound mms02387687 (2-[[5-[(4-ethylphenoxy) methyl]-4-prop-2-enyl-1,2,4-triazol-3-yl] sulfanyl]-N-[3(trifluoromethyl) phenyl] acetamide) was ranked on top, and we believe it can serve as a drug against HCV in the future, owing to experimental validation.
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Bajrai, Leena Hussein, Aiah M. Khateb, Maha M. Alawi, Hashim R. Felemban, Anees A. Sindi, Vivek Dhar Dwivedi, and Esam Ibraheem Azhar. "Glycosylated Flavonoid Compounds as Potent CYP121 Inhibitors of Mycobacterium tuberculosis." Biomolecules 12, no. 10 (September 23, 2022): 1356. http://dx.doi.org/10.3390/biom12101356.
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Due to the concerning rise in the number of multiple- and prolonged-drug-resistant (MDR and XDR) Mycobacterium tuberculosis (Mtb) strains, unprecedented demand has been created to design and develop novel therapeutic drugs with higher efficacy and safety. In this study, with a focused view on implementing an in silico drug design pipeline, a diverse set of glycosylated flavonoids were screened against the Mtb cytochrome-P450 enzyme 121 (CYP121), which is established as an approved drug target for the treatment of Mtb infection. A total of 148 glycosylated flavonoids were screened using structure-based virtual screening against the crystallized ligand, i.e., the L44 inhibitor, binding pocket in the Mtb CYP121 protein. Following this, only the top six compounds with the highest binding scores (kcal/mol) were considered for further intermolecular interaction and dynamic stability using 100 ns classical molecular dynamics simulation. These results suggested a considerable number of hydrogen and hydrophobic interactions and thermodynamic stability in comparison to the reference complex, i.e., the CYP121-L44 inhibitor. Furthermore, binding free energy via the MMGBSA method conducted on the last 10 ns interval of MD simulation trajectories revealed the substantial affinity of glycosylated compounds with Mtb CYP121 protein against reference complex. Notably, both the docked poses and residual energy decomposition via the MMGBSA method demonstrated the essential role of active residues in the interactions with glycosylated compounds by comparison with the reference complex. Collectively, this study demonstrates the viability of these screened glycosylated flavonoids as potential inhibitors of Mtb CYP121 for further experimental validation to develop a therapy for the treatment of drug-resistant Mtb strains.
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Feng, Dongyan, Le Ren, Jiaqi Wu, Lingling Guo, Zhitao Han, Jingjing Yang, Wei Xie, et al. "Permethrin as a Potential Furin Inhibitor through a Novel Non-Competitive Allosteric Inhibition." Molecules 28, no. 4 (February 16, 2023): 1883. http://dx.doi.org/10.3390/molecules28041883.
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Furin is a potential target protein associated with numerous diseases; especially closely related to tumors and multiple viral infections including SARS-CoV-2. Most of the existing efficient furin inhibitors adopt a substrate analogous structure, and other types of small molecule inhibitors need to be discovered urgently. In this study, a high-throughput screening combining virtual and physical screening of natural product libraries was performed, coupled with experimental validation and preliminary mechanistic assays at the molecular level, cellular level, and molecular simulation. A novel furin inhibitor, permethrin, which is a derivative from pyrethrin I generated by Pyrethrum cinerariifolium Trev. was identified, and this study confirmed that it binds to a novel allosteric pocket of furin through non-competitive inhibition. It exhibits a very favorable protease-selective inhibition and good cellular activity and specificity. In summary, permethrin shows a new parent nucleus with a new mode of inhibition. It could be used as a highly promising lead compound against furin for targeting related tumors and various resistant viral infections, including SARS-CoV-2.
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Afantitis, Antreas, Andreas Tsoumanis, and Georgia Melagraki. "Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME." Current Medicinal Chemistry 27, no. 38 (November 12, 2020): 6523–35. http://dx.doi.org/10.2174/0929867327666200727114410.
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Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.
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Yang, Qiangzhen, Xuemin Jian, Ali Alamdar Shah Syed, Aamir Fahira, Chenxiang Zheng, Zijia Zhu, Ke Wang, et al. "Structural Comparison and Drug Screening of Spike Proteins of Ten SARS-CoV-2 Variants." Research 2022 (February 1, 2022): 1–20. http://dx.doi.org/10.34133/2022/9781758.
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SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has evolved many variants with stronger infectivity and immune evasion than the original strain, including Alpha, Beta, Gamma, Delta, Epsilon, Kappa, Iota, Lambda, and 21H strains. Amino acid mutations are enriched in the spike protein of SARS-CoV-2, which plays a crucial role in cell infection. However, the impact of these mutations on protein structure and function is unclear. Understanding the pathophysiology and pandemic features of these SARS-CoV-2 variants requires knowledge of the spike protein structures. Here, we obtained the spike protein structures of 10 main globally endemic SARS-CoV-2 strains using AlphaFold2. The clustering analysis based on structural similarity revealed the unique features of the mainly pandemic SARS-CoV-2 Delta variants, indicating that structural clusters can reflect the current characteristics of the epidemic more accurately than those based on the protein sequence. The analysis of the binding affinities of ACE2-RBD, antibody-NTD, and antibody-RBD complexes in the different variants revealed that the recognition of antibodies against S1 NTD and RBD was decreased in the variants, especially the Delta variant compared with the original strain, which may induce the immune evasion of SARS-CoV-2 variants. Furthermore, by virtual screening the ZINC database against a high-accuracy predicted structure of Delta spike protein and experimental validation, we identified multiple compounds that target S1 NTD and RBD, which might contribute towards the development of clinical anti-SARS-CoV-2 medicines. Our findings provided a basic foundation for future in vitro and in vivo investigations that might speed up the development of potential therapies for the SARS-CoV-2 variants.
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Alam, Zenifer, Md Nazmul Islam Bappy, Abida Sultana, Fayeza Sadia Laskar, Kawsar Miah, Kazi Md Ali Zinnah, and Sudeb Saha. "In-Silico Exploration of Plant Metabolites as Potential Remedies of Norovirus." Advances in Virology 2022 (October 20, 2022): 1–13. http://dx.doi.org/10.1155/2022/8905962.
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Research is still being carried out to develop specific medications or vaccinations to fight norovirus, a key contributor to foodborne illness. This study evaluated certain plant-based active chemicals as prospective candidates for such treatments using virtual screening techniques and other computer assessments. Twenty (20) plant metabolites were tested against the norovirus VP1, VP2, P48, and P22 protein domains using the molecular docking method. In terms of the lowest global binding energy, Asiatic acid, avicularin, guaijaverin, and curcumin exhibited the highest binding affinity with all selected proteins. Each viral protein’s essential binding sites with the potential drugs and drug surface hotspots were uncovered. The ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis was used to further analyze the pharmacological profiles of the top candidates. According to the results, none of the substances showed any adverse consequences that would reduce their drug-like properties. According to the analysis of the toxicity pattern, no detectable tumorigenic, mutagenic, irritating, or reproductive effects of the compounds were discovered. However, among the top four alternatives, curcumin exhibited the highest levels of cytotoxicity and immunotoxicity. These discoveries may open the way for the development of effective norovirus therapies and safety measures. Due to the positive outcomes, we strongly propose more in vivo experiments for the experimental validation of our findings.
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Ambrose, Jenifer Mallavarpu, Malathi Kullappan, Shankargouda Patil, Khalid J. Alzahrani, Hamsa Jameel Banjer, Fadi S. I. Qashqari, A. Thirumal Raj, et al. "Plant-Derived Antiviral Compounds as Potential Entry Inhibitors against Spike Protein of SARS-CoV-2 Wild-Type and Delta Variant: An Integrative in SilicoApproach." Molecules 27, no. 6 (March 8, 2022): 1773. http://dx.doi.org/10.3390/molecules27061773.
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The wild-type SARS-CoV-2 has continuously evolved into several variants with increased transmissibility and virulence. The Delta variant which was initially identified in India created a devastating impact throughout the country during the second wave. While the efficacy of the existing vaccines against the latest SARS-CoV-2 variants remains unclear, extensive research is being carried out to develop potential antiviral drugs through approaches like in silico screening and drug-repurposing. This study aimed to conduct the docking-based virtual screening of 50 potential phytochemical compounds against a Spike glycoprotein of the wild-type and the Delta SARS-CoV-2 variant. Subsequently, molecular docking was performed for the five best compounds, such as Lupeol, Betulin, Hypericin, Corilagin, and Geraniin, along with synthetic controls. From the results obtained, it was evident that Lupeol exhibited a remarkable binding affinity towards the wild-type Spike protein (−8.54 kcal/mol), while Betulin showed significant binding interactions with the mutated Spike protein (−8.83 kcal/mol), respectively. The binding energy values of the selected plant compounds were slightly higher than that of the controls. Key hydrogen bonding and hydrophobic interactions of the resulting complexes were visualized, which explained their greater binding affinity against the target proteins—the Delta S protein of SARS-CoV-2, in particular. The lower RMSD, the RMSF values of the complexes and the ligands, Rg, H-bonds, and the binding free energies of the complexes together revealed the stability of the complexes and significant binding affinities of the ligands towards the target proteins. Our study suggests that Lupeol and Betulin could be considered as potential ligands for SARS-CoV-2 spike antagonists. Further experimental validations might provide new insights for the possible antiviral therapeutic interventions of the identified lead compounds and their analogs against COVID-19 infection.
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Lu, Pinyi, Raquel Hontecillas, Monica Viladomiu, Mireia Pedragosa, Adri Carbo, David Bevan, Stephanie Lewis, and Josep Bassaganya-Riera. "Immunomodulatory actions of lanthionine synthetase C-like protein 2-based drugs (165.9)." Journal of Immunology 188, no. 1_Supplement (May 1, 2012): 165.9. http://dx.doi.org/10.4049/jimmunol.188.supp.165.9.
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Abstract Current inflammatory bowel disease (IBD) treatments are associated with significant side-effects. We identified lanthionine synthetase component C-like protein 2 (LANCL2) as a target for abscisic acid, a naturally occurring compound with potent anti-inflammatory effects. The goal of this study was to determine the role of LANCL2 as a therapeutic target for developing novel anti-inflammatory drugs. Structure-based virtual screening was performed using a compound library from National Cancer Institute Diversity Set II. To validate the anti-inflammatory efficacy of the top-ranking compound 61610, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate-induced colitis. Our findings showed that oral administration of 61610 (20 mg/kg/day) ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. We also investigated the cell specificity and molecular targets underlying the anti-inflammatory mechanism of 61610. Our in vivo findings indicate that anti-inflammatory efficacy of 61610 depends on macrophage expression of peroxisome proliferator-activated receptor γ, a receptor downstream of LANCL2. In summary, we used an integrated drug discovery platform consisting of molecular modeling approaches followed by experimental validation to confirm LANCL2 as a novel therapeutic target against IBD and demonstrated that 61610 is a novel anti-inflammatory drug.
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Ma, Chongyang, Mengpei Zhao, Yuqiong Du, Shuang Jin, Xiaoyi Wu, Haiyan Zou, Qiuyun Zhang, and Lianyin Gao. "Network Pharmacology-Based Study on the Molecular Biological Mechanism of Action for Qingdu Decoction against Chronic Liver Injury." Evidence-Based Complementary and Alternative Medicine 2021 (March 3, 2021): 1–12. http://dx.doi.org/10.1155/2021/6661667.
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Background. Qingdu Decoction (QDD) is a traditional Chinese medicine formula for treating chronic liver injury (CLI). Materials and methods. A network pharmacology combining experimental validation was used to investigate potential mechanisms of QDD against CLI. We firstly screened the bioactive compounds with pharmacology analysis platform of the Chinese medicine system (TCMSP) and gathered the targets of QDD and CLI. Then, we constructed a compound-target network and a protein-protein interaction (PPI) network and enriched core targets in Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways. At last, we used a CLI rat model to confirm the effect and mechanism of QDD against CLI. Enzyme-linked immunosorbent assay (ELISA), western blot (WB), and real-time quantitative polymerase chain reaction (RT-qPCR) were used. Results. 48 bioactive compounds of QDD passed the virtual screening criteria, and 53 overlapping targets were identified as core targets of QDD against CLI. A compound-CLI related target network containing 94 nodes and 263 edges was constructed. KEGG enrichment of core targets contained some pathways related to CLI, such as hepatitis B, tumor necrosis factor (TNF) signaling pathway, apoptosis, hepatitis C, interleukin-17 (IL-17) signaling pathway, and hypoxia-inducible factor (HIF)-1 signaling pathway. Three PPI clusters were identified and enriched in hepatitis B and tumor necrosis factor (TNF) signaling pathway, apoptosis and hepatitis B pathway, and peroxisome pathway, respectively. Animal experiment indicated that QDD decreased serum concentrations of alanine aminotransferase (ALT), aspartate aminotransferase (AST), endotoxin (ET), and IL-17 and increased prothrombin time activity (PTA) level. WB and RT-qPCR analyses indicated that, compared with the model group, the expression of cysteinyl aspartate specific proteinase-9 (caspase-9) protein, caspase-3 protein, B-cell lymphoma-2 associated X protein (Bax) mRNA, and cytochrome c (Cyt c) mRNA was inhibited and the expression of B-cell lymphoma-2 (Bcl-2) mRNA was enhanced in the QDD group. Conclusions. QDD has protective effect against CLI, which may be related to the regulation of hepatocyte apoptosis. This study provides novel insights into exploring potential biological basis and mechanisms of clinically effective formula systematically.
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Karkoutly, Omar, Anupam Dhasmana, Kyle Doxtater, Sudhir Kotnala, Kristopher Ezell, Sophia Leslie, Adithya Anilkumar, Samantha Lopez, Subhash Chauhan, and Manish Tripathi. "Abstract 3357: Identification and validation of novel molecular inhibitors from the DrugBank drug library." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3357. http://dx.doi.org/10.1158/1538-7445.am2022-3357.
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Abstract Background: The 3D crystal structure of a protein determines its overall function, and when the structure of a protein is known, small drug molecules can be designed to bind to it and inhibit its function. Target-based drug discovery, specifically for genetic products that cause a higher risk of disease (genetic targets), takes advantage of this fact in particular. This type of drug discovery is essential for combating various cancer protein targets, including ones responsible for multidrug- resistance in liver cancer, like YB-1. Methods: The RCSB protein data bank (PDB) was used to retrieve the crystal structure of YB-1, while the DrugBank database was used to obtain a list of experimental and approved drugs. A multiple sequence alignment (MSA) of YB-1 and Lin28, a known transcription factor, was then done by Clustal Omega to validate a conserved domain. Biovia Discovery Studio 2020 was used to visualize 3D models and perform a High-Throughput Virtual Screening (HTVS), including rigid docking via the LibDock extension and flexible docking via the CDocker extension pharmacokinetic profiling via an ADMET analysis. A literature search was conducted to finalize a list of potential cancer protein inhibitors. The most promising compounds were then tested in vitro on associated liver cancer cell lines and checked for expression of YB-1 related downstream target genes (including those related to multiple-drug resistance) via real-time PCR, protein expression via western blot analysis, and YB-1 translocation via immunofluorescence. Results: Utilizing this approach, we obtained a protein model with a 97.3 percentage in the most favorable region of a Ramachandran plot. Twenty-two drug candidates were identified through HTVS as potential inhibitors of a specific cancer protein target from a list of over 10,000 compounds in the DrugBank library. The best six show a decent binding ability in both rigid and flexible dockings and have been previously tested in different cancer types to some extent. The data on YB-1 stability and function and translocation efficiency modulated by the identified drugs will be presented. Conclusions: Studying protein-drug interactions is of particular importance for understanding how structural protein elements affect overall ligand affinity. By taking a bioinformatics approach to analyzing drug-protein interactions, we can drastically increase the speed with which we identify potential inhibitors for cancer protein targets. Citation Format: Omar Karkoutly, Anupam Dhasmana, Kyle Doxtater, Sudhir Kotnala, Kristopher Ezell, Sophia Leslie, Adithya Anilkumar, Samantha Lopez, Subhash Chauhan, Manish Tripathi. Identification and validation of novel molecular inhibitors from the DrugBank drug library [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3357.