DEKOIS: Demanding Evaluation Kits for Objective <i>in Silico</i> Screening — A Versatile Tool for Benchmarking Docking Programs and Scoring Functions

Vogel, Simon; Bauer, Matthias R.; Boeckler, Frank M.

doi:10.1021/ci2001549

Cited by 73 publications

(145 citation statements)

References 48 publications

(60 reference statements)

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“…Starting with a virtual database of 3.6 million ready-to-order molecules, we excluded compounds with undesirable properties and docked 87,430 molecules to the ensemble of four MDM4 crystal structures using the program GOLD (26). Subsequently, we applied post-docking filters based on pharmacophore information to eliminate unfavorable binding modes and further narrowed down the list using an expert system (6,19) (Fig. 1).…”

Section: Resultsmentioning

confidence: 99%

“…Accordingly, MDM4-selective and dual MDM4/MDM2 inhibitors are also being sought (7,17,18). We searched for MDM4 inhibitors by structure-based in silico screening of binding (6,19) and identified LCA as an endogenous inhibitor of both MDM4 and MDM2. LCA is a secondary bile acid formed by bacteria in the gut from its precursor chenodeoxycholic acid (CDCA, Fig S1).…”

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confidence: 99%

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Lithocholic acid is an endogenous inhibitor of MDM4 and MDM2

Vogel

Bauer

Joerger

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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The proteins MDM2 and MDM4 are key negative regulators of the tumor suppressor protein p53, which are frequently upregulated in cancer cells. They inhibit the transactivation activity of p53 by binding separately or in concert to its transactivation domain. MDM2 is also a ubiquitin ligase that leads to the degradation of p53. Accordingly, MDM2 and MDM4 are important targets for drugs to inhibit their binding to p53. We found from in silico screening and confirmed by experiment that lithocholic acid (LCA) binds to the p53 binding sites of both MDM2 and MDM4 with a fivefold preference for MDM4. LCA is an endogenous steroidal bile acid, variously reported to have both carcinogenic and apoptotic activities. The comparison of LCA effects on apoptosis in HCT116 p53 þ∕þ vs. p53 −∕− cells shows a predominantly p53-mediated induction of caspase-3/7. The dissociation constants are in the μM region, but only modest inhibition of binding of MDM2 and MDM4 is required to negate their upregulation because they have to compete with transcriptional coactivator p300 for binding to p53. Binding was weakened by structural changes in LCA, and so it may be a natural ligand of MDM2 and MDM4, raising the possibility that MDM proteins may be sensors for specific steroids.HDMX | virtual screening | natural product | cancer pathways | bile acid sensor T he tumor-suppressor protein p53 plays a pivotal role in cancer (1, 2). Often, its function is severely impaired by upregulation of its two key negative regulators, MDM2 and MDM4 (1-3). The N-terminal domains of MDM2 and MDM4 are structurally very similar and both bind to the same sequence in the intrinsically disordered N terminus of p53 (2, 4, 5). The binding cavities within MDM4 and MDM2 are important targets for drug therapy that releases them from p53 (6-10). Nutlins (11), for example, are potent MDM2 inhibitors and are potential therapeutics (12, 13) as well as being invaluable research tools for probing p53 pathways (12,14), as are spiro-oxindoles, which were found by in silico methods (15,16). MDM4 has a different specificity for small molecules, binding nutlins, for example, less tightly. Accordingly, MDM4-selective and dual MDM4/MDM2 inhibitors are also being sought (7,17,18). We searched for MDM4 inhibitors by structure-based in silico screening of binding (6, 19) and identified LCA as an endogenous inhibitor of both MDM4 and MDM2. LCA is a secondary bile acid formed by bacteria in the gut from its precursor chenodeoxycholic acid (CDCA, Fig S1). It has been variously reported to show both carcinogenic and apoptotic activities (20,21). LCA is a rare example of a toxic endobiotic that is efficiently detoxicated by conjugation with taurine or glycine, sulfation at C-3 by the sulfotransferase SULT2A1, or metabolism through cytochrome P450 CYP3A enzymes (20). It induces its own metabolism by activating nuclear receptors like the vitamin D receptor (22) (VDR) and the farnesoid X receptor (23) (FXR). Thereby, it inhibits the synthesis of bile acids and promotes the transcription of gene...

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Lithocholic acid is an endogenous inhibitor of MDM4 and MDM2

Vogel

Bauer

Joerger

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…Multiple research groups have been building decoy sets to address the apparent weaknesses in DUD [62–64, 92, 94]. Good and Oprea generated WOrld of Molecular BioAcTivity (WOMBAT) data sets at the same time in order to expand the limited number of diverse ligands in “DUD clusters” [92].…”

Section: Currently Available Benchmarking Setsmentioning

confidence: 99%

“…the SBVS-specific and the LBVS-specific. Datasets such as Directory of Useful Decoys (DUD) [57] and its recent DUD-Enhanced (DUD-E) [58], virtual decoy sets (VDS) [62], G protein-coupled receptors (GPCRs) ligand library (GLL) and GPCRs Decoy Database (GDD) [63], Demanding Evaluation Kits for Objective in Silico Screening (DEKOIS) [64] and DEKOIS 2.0 [65], Nuclear Receptors Ligands and Structures Benchmarking DataBase (NRLiSt BDB) belong to SBVS-specific benchmarking sets. By contrast, only 3 datasets, i.e.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking methods and data sets for ligand enrichment assessment in virtual screening

Xia

Tilahun

Reid

et al. 2015

Methods

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Retrospective small-scale virtual screening (VS) based on benchmarking data sets has been widely used to estimate ligand enrichments of VS approaches in the prospective (i.e. real-world) efforts. However, the intrinsic differences of benchmarking sets to the real screening chemical libraries can cause biased assessment. Herein, we summarize the history of benchmarking methods as well as data sets and highlight three main types of biases found in benchmarking sets, i.e. “analogue bias”, “artificial enrichment” and “false negative”. In addition, we introduced our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structure-based VS approaches, and its implementations to three important human histone deacetylase (HDAC) isoforms, i.e. HDAC1, HDAC6 and HDAC8. The Leave-One-Out Cross-Validation (LOO CV) demonstrates that the benchmarking sets built by our algorithm are maximum-unbiased in terms of property matching, ROC curves and AUCs.

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“…In recent years, there has been an increasing number of benchmarking sets developed by multiple research groups worldwide, from DUD/DUD-E, MUV, to DEKOIS 2.0 (30)(31)(32)(33)(34)(35)(36)(37). Unfortunately, none of them provide available benchmarking data set for the particular TLR8 agonists.…”

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confidence: 99%

Enrichment Assessment of Multiple Virtual Screening Strategies for Toll‐Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set

et al. 2015

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Toll-like receptor 8 agonists, which activate adaptive immune responses by inducing robust production of T-helper 1-polarizing cytokines, are promising candidates for vaccine adjuvants. As the binding site of toll-like receptor 8 is large and highly flexible, virtual screening by individual method has inevitable limitations; thus, a comprehensive comparison of different methods may provide insights into seeking effective strategy for the discovery of novel toll-like receptor 8 agonists. In this study, the performance of knowledge-based pharmacophore, shape-based 3D screening, and combined strategies was assessed against a maximum unbiased benchmarking data set containing 13 actives and 1302 decoys specialized for toll-like receptor 8 agonists. Prior structure-activity relationship knowledge was involved in knowledge-based pharmacophore generation, and a set of antagonists was innovatively used to verify the selectivity of the selected knowledge-based pharmacophore. The benchmarking data set was generated from our recently developed 'mubd-decoymaker' protocol. The enrichment assessment demonstrated a considerable performance through our selected three-layer virtual screening strategy: knowledge-based pharmacophore (Phar1) screening, shape-based 3D similarity search (Q4_combo), and then a Gold docking screening. This virtual screening strategy could be further employed to perform large-scale database screening and to discover novel toll-like receptor 8 agonists.

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DEKOIS: Demanding Evaluation Kits for Objective in Silico Screening — A Versatile Tool for Benchmarking Docking Programs and Scoring Functions

Cited by 73 publications

References 48 publications

Lithocholic acid is an endogenous inhibitor of MDM4 and MDM2

Lithocholic acid is an endogenous inhibitor of MDM4 and MDM2

Benchmarking methods and data sets for ligand enrichment assessment in virtual screening

Enrichment Assessment of Multiple Virtual Screening Strategies for Toll‐Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set

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