ArticleJournal of computer-aided molecular design2016
Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge.
Article in Journal of computer-aided molecular design, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4.Journal of computer-aided molecular design · 2022Article
- Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.Journal of chemical information and modeling · 2020Article
- Cross-docking benchmark for automated pose and ranking prediction of ligand binding.Protein science : a publication of the Protein Society · 2020Article
- Defining the Kv2.1-syntaxin molecular interaction identifies a first-in-class small molecule neuroprotectant.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- A network-centric approach to drugging TNF-induced NF-κB signaling.Nature communications · 2019Article
- Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.Journal of computer-aided molecular design · 2019Article
- Predicting protein targets for drug-like compounds using transcriptomics.PLoS computational biology · 2018Article
- A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor.Future medicinal chemistry · 2018Article
- Improving small molecule virtual screening strategies for the next generation of therapeutics.Current opinion in chemical biology · 2018Review
- Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges.Journal of computer-aided molecular design · 2018Article
- Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2.Journal of computer-aided molecular design · 2018Article
- A cross docking pipeline for improving pose prediction and virtual screening performance.Journal of computer-aided molecular design · 2018Article
- Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2.Journal of computer-aided molecular design · 2018Article
- Improved pose and affinity predictions using different protocols tailored on the basis of data availability.Journal of computer-aided molecular design · 2016Article
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4 authors.
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Abstract
Induced fit or protein flexibility can make a given structure less useful for docking and/or scoring. The 2015 Drug Design Data Resource (D3R) Grand Challenge provided a unique opportunity to prospectively test optimal strategies for virtual screening in these type of targets: heat shock protein 90 (HSP90), a protein with multiple ligand-induced binding modes; and mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4), a kinase with a large flexible pocket. Using previously known co-crystal structures, we tested predictions from methods that keep the receptor structure fixed and used (a) multiple receptor/ligand co-crystals as binding templates for minimization or docking ("close"), (b) methods that align or dock to a single receptor ("cross"), and (c) a hybrid approach that chose from multiple bound ligands as initial templates for minimization to a single receptor ("min-cross"). Pose prediction using our "close" models resulted in average ligand RMSDs of 0.32 and 1.6 Å for HSP90 and MAP4K4, respectively, the most accurate models of the community-wide challenge. On the other hand, affinity ranking using our "cross" methods performed well overall despite the fact that a fixed receptor cannot model ligand-induced structural changes,. In addition, "close" methods that leverage the co-crystals of the different binding modes of HSP90 also predicted the best affinity ranking. Our studies suggest that analysis of changes on the receptor structure upon ligand binding can help select an optimal virtual screening strategy.
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