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Original Research

Per-residue energy decomposition pharmacophore model to enhance virtual screening in drug discovery: a study for identification of reverse transcriptase inhibitors as potential anti-HIV agents

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Pages 1365-1377 | Published online: 11 Apr 2016

Figures & data

Figure 1 Ribbon representation of HIV-1 RT-GSK952 complex.

Note: HIV-1 RT-GSK952 complex (PDB code 2YNI) with finger (blue), palm (magenta), thumb (cyan), connection (forest green) and RNase H (orange red) of p66 subunit and GSK952 (green).
Abbreviations: RT, reverse transcriptase; PDB, Protein Data Base.
Figure 1 Ribbon representation of HIV-1 RT-GSK952 complex.

Figure 2 The 2D structure of ligand GSK952 used to generate the pharmacophore model.

Abbreviation: 2D, two dimensional.
Figure 2 The 2D structure of ligand GSK952 used to generate the pharmacophore model.

Figure 3 A schematic representation of the VS workflow used in the current study.

Abbreviations: VS, virtual screening; PDB, Protein Database; MD, molecular dynamic; MMPBSA, molecular mechanics/Poisson–Boltzmann surface area; PRED, per-residue energy decomposition; HCAAR, highly contributing amino acid residues; FBE, free binding energy.
Figure 3 A schematic representation of the VS workflow used in the current study.

Figure 4 A diagrammatic representation of the pharmacophore model.

Notes: (A) PRED contributions, (B) 2D ligand interaction plot, and (C) pharmacophore features responsible for FBE contributions.
Abbreviations: PRED, per-residue energy decomposition; FBE, free binding energy; vdW, van der Waals; Elec, electrostatic; 2D, two dimensional.
Figure 4 A diagrammatic representation of the pharmacophore model.

Table 1 Validation of molecular docking approach

Figure 5 Validation of molecular docking: docking score vs half maximal inhibitory concentration (IC50).

Figure 5 Validation of molecular docking: docking score vs half maximal inhibitory concentration (IC50).

Table 2 Representation of the top ten compounds displaying 3D shapes, HBD, HBA, xlogP, MW, and calculated DS and RB

Table 3 A comparison of GSK952’s binding affinity with that of the top two hits ZINC54359621 and ZINC46849657

Figure 6 The per residue graphs showing FBE contribution for both ZINC54359621 and ZINC46849657.

Notes: (A) Top hit 1, ZINC54359621 with the highest total binding energy. (B) Top hit 2, ZINC46849657 with the second highest total binding energy.
Abbreviations: FBE, free binding energy; vdW, van der Waals; Elec, electrostatic.
Figure 6 The per residue graphs showing FBE contribution for both ZINC54359621 and ZINC46849657.

Figure 7 Binding mode of compounds.

Notes: (A) ZINC54359621 and (B) ZINC46849657 to HIV-1 RT enzyme, respectively.
Abbreviation: RT, reverse transcriptase.
Figure 7 Binding mode of compounds.

Figure S1 Two key binding interactions exist between GSK952 and the backbone NH and C=O groups of Lys103 of HIV-1 RT.

Abbreviation: RT, reverse transcriptase.
Figure S1 Two key binding interactions exist between GSK952 and the backbone NH and C=O groups of Lys103 of HIV-1 RT.

Figure S2 PRED-based pharmacophore model.

Abbreviations: PRED, per-residue energy decomposition; HPI, hydrophobic interaction; HA, hydrogen acceptor.
Figure S2 PRED-based pharmacophore model.

Figure S3 MD simulation results of ZINC54359621 and ZINC46849657.

Notes: (A) RMSD (average of 3.12 Ǻ and 2.58 Ǻ, respectively), (B) RMSF (average of 1.87 Ǻ, and 1.53 Ǻ, respectively), and (C) Rg (average of 51.2 Ǻ and 51.2 Ǻ, respectively).
Abbreviations: MD, molecular dynamic; RMSD, root-mean-square deviation; RMSF, root-mean-square fluctuation; Rg, radius of gyration.
Figure S3 MD simulation results of ZINC54359621 and ZINC46849657.

Table S1 PRED of highly interacting residues