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

Pei, Fen; Jin, Hongwei; Zhou, Xin; Xia, Jie; Sun, Lidan; Liu, Zhenming; Zhang, Liangren

doi:10.1111/cbdd.12590

Cited by 11 publications

(8 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, MUV relies on the availability of experimental data and is restricted to well-studied targets. The authors subsequently proposed the Maximum Unbiased Benchmarking Data sets (MUBD, see section Benchmarking Databases) that was applied to GPCRs (Xia et al, 2014 ), HDACs (Xia et al, 2015 ; Hu et al, 2017 ) and Toll-like receptor 8 (Pei et al, 2015 ). The MUBD-DecoyMaker algorithm relies on both a minimal and required topological dissimilarity ( sims ) between decoy and active compounds, but makes use of an additional criterion that minimizes the simsdiff parameter, i.e., ensures that decoy and active compounds are as similar as possible.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

“…New databases were designed with an increasing complexity in the decoys selection methodologies (see section Benchmarking Databases). Nowadays, benchmarking databases are widely used to evaluate various VS tools (Kellenberger et al, 2004 ; Warren et al, 2006 ; McGaughey et al, 2007 ; von Korff et al, 2009 ; Braga and Andrade, 2013 ; Ibrahim et al, 2015a ; Pei et al, 2015 ) and to support the identification of hit/lead compounds using LBVS and SBVS (Allen et al, 2015 ; Ruggeri et al, 2015 ).…”

Section: The History Of Decoys Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

et al. 2018

View full text Add to dashboard Cite

Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.

show abstract

Section: Discussion and Recommendationsmentioning

confidence: 99%

Section: The History Of Decoys Selectionmentioning

confidence: 99%

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Five compounds were finally selected for biological studies and one compound (code MolPort-001-796-266, Table 2 ) was confirmed as TLR2 antagonist. For TLR8 [ 59 ], while LigandScout was also used to obtain pharmacophore models within a sophisticated protocol to optimize VS of TLR8 agonists (see Section 4.5 ).…”

Section: Virtual Screening Protocols and Techniquesmentioning

confidence: 99%

“…Six new compounds with three new chemical scaffolds were identified as TLR7 antagonists with activities within the µM range ( Table 2 ). In the case of TLR8 [ 59 ], in order to generate ROCS queries, the authors performed an alignment of six crystal ligands as initial molecules. The results were ranked using the “ShapeTanimoto” and the “TanimotoCombo” scores.…”

Section: Virtual Screening Protocols and Techniquesmentioning

confidence: 99%

Virtual Screening Approaches towards the Discovery of Toll-Like Receptor Modulators

Pérez-Regidor

Zarioh

Ortega

et al. 2016

IJMS

View full text Add to dashboard Cite

Abstract:This review aims to summarize the latest efforts performed in the search for novel chemical entities such as Toll-like receptor (TLR) modulators by means of virtual screening techniques. This is an emergent research field with only very recent (and successful) contributions. Identification of drug-like molecules with potential therapeutic applications for the treatment of a variety of TLR-regulated diseases has attracted considerable interest due to the clinical potential. Additionally, the virtual screening databases and computational tools employed have been overviewed in a descriptive way, widening the scope for researchers interested in the field.

show abstract

“…All the constructed models along with multiple scoring functions were evaluated for their screening power by retrospective small-scale virtual screening. Specifically, the evaluation was based on a benchmarking set generated by our in-house MUBD-DecoyMaker (Xia et al, 2014), a tool that had effectively facilitated VS campaigns against multiple targets (Huang et al, 2016;Pei et al, 2015;Xia et al, 2015). Apart from the screening power, the binding mode proposed by the optimal model was explored.…”

Section: Introductionmentioning

confidence: 99%

A unique ligand‐steered strategy for CC chemokine receptor 2 homology modeling to facilitate structure‐based virtual screening

et al. 2021

Self Cite

View full text Add to dashboard Cite

CC chemokine receptor 2 (CCR2) antagonists that disrupt CCR2/MCP-1 interaction are expected to treat a variety of inflammatory and autoimmune diseases. The lack of CCR2 crystal structure limits the application of structure-based drug design (SBDD) to this target. Although a few three-dimensional theoretical models have been reported, their accuracy remains to be improved in terms of templates and modeling approaches. In this study, we developed a unique ligand-steered strategy for CCR2 homology modeling. It starts with an initial model based on the X-ray structure of the closest homolog so far, that is, CXCR4. Then, it uses Elastic Network Normal Mode Analysis (EN-NMA) and flexible docking (FD) by AutoDock Vina software to generate ligand-induced fit models. It selects optimal model(s) as well as scoring function(s) via extensive evaluation of model performance based on a unique benchmarking set constructed by our in-house tool, that is, MUBD-DecoyMaker. The model of 81_04 presents the optimal enrichment when combined with the scoring function of PMF04, and the proposed binding mode between CCR2 and Teijin lead by this model complies with the reported mutagenesis data. To highlight the advantage of our strategy, we compared it with the only reported ligand-steered strategy for CCR2 homology modeling, that is, Discovery Studio/Ligand Minimization. Lastly, we performed prospective virtual screening based on 81_04 and CCR2 antagonist bioassay. The identification of two hit compounds, that is, E859-1281 and MolPort-007-767-945, validated the efficacy of our model and the ligand-steered strategy. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

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

Cited by 11 publications

References 56 publications

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

Virtual Screening Approaches towards the Discovery of Toll-Like Receptor Modulators

A unique ligand‐steered strategy for CC chemokine receptor 2 homology modeling to facilitate structure‐based virtual screening

Contact Info

Product

Resources

About