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Ligand similarity guided receptor selection enhances docking accuracy and recall for non-nucleoside HIV reverse transcriptase inhibitors

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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.

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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Acknowledgments

This work was supported in part by Public Health Service grants 5P30-AI-50409 (CFAR), and by the Department of Veterans Affairs. Pipeline Pilot and Discovery Studio software were received as Academic Achievement awards from Accelrys Corporation. The authors would like to thank Dr. James J. Kohler and Dr. Bryan D. Cox for their critical reading of the manuscript.

Conflicts of interest

The authors declare no conflict of interest.

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Authors and Affiliations

  1. Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, 30322, USA

    Richard A. Stanton, James H. Nettles & Raymond F. Schinazi

  2. Veterans Affairs Medical Center, Atlanta, GA, 30033, USA

    Raymond F. Schinazi

Authors
  1. Richard A. Stanton
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  2. James H. Nettles
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  3. Raymond F. Schinazi
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Corresponding author

Correspondence to Raymond F. Schinazi.

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Fig. S1

A comparison of receptor selection by three MOE descriptors available in both 2D and 3D formats. The atom type distance (2D: TGD/3D: TAD), atom type triangle (TGT/TAT), and pharmacophore triangle (gpiDAPH3/piDAPH3) descriptors were used (JPEG 781 kb)

Fig. S2

ROC curves showing recall of accurate poses of 87 solved NNRTI from cross-docking into all 87 available receptors using the docking score from AutoDock Vina (JPEG 959 kb)

Table S1

Variability of NNRTI-RT solved structures (DOC 71 kb)

Table S2

Cross-docking accuracy per PDB (DOC 139 kb)

Table S3

Cross-docking accuracy into receptors selected by similarity descriptors (non-statistically significant) (DOC 98 kb)

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Stanton, R.A., Nettles, J.H. & Schinazi, R.F. Ligand similarity guided receptor selection enhances docking accuracy and recall for non-nucleoside HIV reverse transcriptase inhibitors. J Mol Model 21, 282 (2015). https://doi.org/10.1007/s00894-015-2826-7

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