ArticleJournal of molecular modeling2015
Ligand similarity guided receptor selection enhances docking accuracy and recall for non-nucleoside HIV reverse transcriptase inhibitors.
Article in Journal of molecular modeling, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates.Structure (London, England : 1993) · 2018Article
- Discovery, characterization, and lead optimization of 7-azaindole non-nucleoside HIV-1 reverse transcriptase inhibitors.Bioorganic & medicinal chemistry letters · 2016Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Non-nucleoside reverse transcriptase inhibitors (NNRTI) are allosteric inhibitors of human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT), a viral polymerase essential to infection. Despite the availability of >150 NNRTI-bound RT crystal structures, rational design of new NNRTI remains challenging because of the variability of their induced fit, hydrophobic binding patterns. Docking NNRTI yields inconsistent results that vary markedly depending on the receptor structure used, as only 27% of the >20k cross-docking calculations we performed using known NNRTI were accurate. In order to determine if a hospitable receptor for docking could be selected a priori, we evaluated more than 40 chemical descriptors for their ability to pre-select a best receptor for NNRTI cross-docking. The receptor selection was based on similarity scores between the bound- and target-ligands generated by each descriptor. The top descriptors were able to double the probability of cross-docking accuracy over random receptor selection. Additionally, recall of known NNRTI from a large library of similar decoys was increased using the same approach. The results demonstrate the utility of pre-selecting receptors when docking into difficult targets. Graphical Abstract Cross-docking accuracy increases when using chemical descriptors to determine the NNRTI with maximum similarity to the new compound and then docking into its respective receptor.
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Registered trials
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