Virtual Screening on Natural Products for Discovering Active Compounds and Target Information

Shen, Jianhua; Xu, Xiaoying; Cheng, Feng; Liu, Hong; Luo, Xiaomin; Shen, Jingkang; Chen, Kaixian; Zhao, Weimin; Shen, Xu; Jiang, Hualiang

doi:10.2174/0929867033456729

Cited by 126 publications

(91 citation statements)

References 3 publications

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“…Virtual screening approaches by means of docking reflect the ligand-receptor binding process directly, and approaches by means of a pharmacophore as a query structure map the ligand-receptor recognition indirectly [45] . In principle, the effectiveness of docking-based approaches in retrieving active compounds from databases should be higher than that of pharmacophore-based methods.…”

Section: Pharmacophore Fitting Versus Molecular Dockingmentioning

confidence: 99%

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets

Chen

Zhang

et al. 2009

Acta Pharmacol Sin

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Aim: This study was conducted to compare the efficiencies of two virtual screening approaches, pharmacophore-based virtual screening (PBVS) and docking-based virtual screening (DBVS) methods. Methods: All virtual screens were performed on two data sets of small molecules with both actives and decoys against eight structurally diverse protein targets, namely angiotensin converting enzyme (ACE), acetylcholinesterase (AChE), androgen receptor (AR), D-alanyl-D-alanine carboxypeptidase (DacA), dihydrofolate reductase (DHFR), estrogen receptors α (ERα), HIV-1 protease (HIV-pr), and thymidine kinase (TK). Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes. Virtual screens were performed using four screening standards, the program Catalyst for PBVS and three docking programs (DOCK, GOLD and Glide) for DBVS. Results: Of the sixteen sets of virtual screens (one target versus two testing databases), the enrichment factors of fourteen cases using the PBVS method were higher than those using DBVS methods. The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS. Conclusion: The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery. discovery, especially in the cases when no three-dimensional (3D) structural information on the protein target of interest is available. Even when the 3D structures of targets are known, PBVS is also used as a complementary approach to DBVS for pre-processing databases (libraries) of small molecules to remove compounds not possessing features known to be essential for binding or for post-filtering compounds selected by docking approaches [5] . A recent case study by Muthas et al indicated that post-filtering with pharmacophores was shown to increase enrichment rates in their investigated targets compared with docking alone [6] . Several studies have been performed to assess various DBVS methods and compare which docking programs are the most successful in identifying active hits [7][8][9][10] . The conclusion is that no docking program may outperform other docking programs for all the tested targets, and the performance of each tested docking program is highly dependent on the nature of the target binding site [5] .Nevertheless, few case studies for a direct comparison on the performances between PBVS and DBVS have been reported [11] . To gain a general view for the discrimination between these two types of approach in prioritizing actives from a database with decoys, we performed a benchmark comparison between the performances of PBVS and DBVS. Eight structurally diverse protein targets were selected in this study. The pharmacophore models were generated from the ligand co-crystallized complex structures of these targets using the LigandScout program [12] , and each PBVS was performed using the program Catalyst [13,14] . To avoid the target dependency of docking pro...

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Section: Pharmacophore Fitting Versus Molecular Dockingmentioning

confidence: 99%

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets

Chen

Zhang

et al. 2009

Acta Pharmacol Sin

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“…More than 100 000 natural products were retrieved from the Reaxys, CNPD (Shen et al, 2003), HIM , and CamMedNP (Ntie- Kang et al, 2013) databases. First, we used daylight fingerprints to characterize these compound structures and then heuristic clustering was employed to analyze their structural diversity.…”

Section: Natural Product Librarymentioning

confidence: 99%

Stepwise high-throughput virtual screening of Rho kinase inhibitors from natural product library and potential therapeutics for pulmonary hypertension

Yan

et al. 2015

Pharmaceutical Biology

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(2015) Stepwise high-throughput virtual screening of Rho kinase inhibitors from natural product library and potential therapeutics for pulmonary hypertension, Pharmaceutical Biology, 53:8, 1201-1206, DOI: 10.3109/13880209.2014 ORIGINAL ARTICLEStepwise high-throughput virtual screening of Rho kinase inhibitors from natural product library and potential therapeutics for pulmonary hypertension Hao Su, Ji Yan, Jian Xu, Xi-Zhen Fan, Xian-Lin Sun, and Kang-Yu Chen Department of Cardiology, Anhui Provincial Hospital, Hefei, China Abstract Context: Pulmonary hypertension (PH) is a devastating disease characterized by progressive elevation of pulmonary arterial pressure and vascular resistance due to pulmonary vasoconstriction and vessel remodeling. The activation of RhoA/Rho-kinase (ROCK) pathway plays a central role in the pathologic progression of PH and thus the Rho kinase, an essential effector of the ROCK pathway, is considered as a potential therapeutic target to attenuate PH. Objective: In the current study, a synthetic pipeline is used to discover new potent Rho inhibitors from various natural products. Materials and methods: In the pipeline, the stepwise high-throughput virtual screening, quantitative structure-activity relationship (QSAR)-based rescoring, and kinase assay were integrated. The screening was performed against a structurally diverse, drug-like natural product library, from which six identified compounds were tested to determine their inhibitory potencies agonist Rho by using a standard kinase assay protocol. Results: With this scheme, we successfully identified two potent Rho inhibitors, namely phloretin and baicalein, with activity values of IC 50 ¼ 0.22 and 0.95 mM, respectively. Discussion and conclusion: Structural examination suggested that complicated networks of nonbonded interactions such as hydrogen bonding, hydrophobic forces, and van der Waals contacts across the complex interfaces of Rho kinase are formed with the screened compounds.

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“…The China natural products database (CNPD) [21], marine natural products database (MNPD) [22], and bioactive plant compounds database (BPCD) [15] only focus on the structures of the compounds in TCM and do not contain pertinent information on formulae and herbs. CNPD is built to meet the needs for drug discovery using natural products including TCM and collects the 2D and 3D structures of more than 45,055 compounds.…”

Section: Databases For Tcmmentioning

confidence: 99%

“…However, the molecular mechanisms responsible for their therapeutic effectiveness are still unclear. On one hand, experimental validation of new drug-target interactions still remains very limiting and expensive, and very few new drugs and targets are identified as clinical applications every (TCM-ID) 1197 formulae, 1313 herbs, ∼9000 compounds [18] TCM drugs information system 1712 formulae, 2738 herbs, 16,500 compounds, 868 dietotherapy prescription [19] Comprehensive herbal medicine information system for cancer (CHMIS-C) 203 formulae, 900 herbs, 8500 compounds [20] China natural products database (CNPD) 45,055 compounds [21] Marine natural products database (MNPD) 8078 compounds, 3200 with bioactivity data [22] Bioactive plant compounds database (BPCD) 2794 compounds [15] acupuncture.com.au TCM formulations http://www.acupuncture.com.au/ education/herbs/herbs.html/ Dictionary of Chinese herbs TCM formulae, toxicity, and side effects http://alternativehealing.org/ chinese herbs dictionary.htm/ Plants for a future Herb medical usage and potential side effects http://www.pfaf.org/ year [13,14]. On the other hand, the complex composition and polypharmacology of TCM make it even harder to conduct a full set of experiments between compounds and targets and elucidate the multitarget mode of action from the holistic view on the biological network level.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods and Applications

Zhao

Wei

2014

Advances in Experimental Medicine and Biology

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The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the "multicomponent-multitarget" and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

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Virtual Screening on Natural Products for Discovering Active Compounds and Target Information

Cited by 126 publications

References 3 publications

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets

Stepwise high-throughput virtual screening of Rho kinase inhibitors from natural product library and potential therapeutics for pulmonary hypertension

Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods and Applications

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