Integrated computational tools for identification of CCR5 antagonists as potential HIV-1 entry inhibitors: homology modeling, virtual screening, molecular dynamics simulations and 3D QSAR analysis

Abstract

Using integrated in-silico computational techniques, including homology modeling, structure-based and pharmacophore-based virtual screening, molecular dynamic simulations, per-residue energy decomposition analysis and atom-based 3D-QSAR analysis, we proposed ten novel compounds as potential CCR5-dependent HIV-1 entry inhibitors. Via validated docking calculations, binding free energies revealed that novel leads demonstrated better binding affinities with CCR5 compared to maraviroc, an FDA-approved HIV-1 entry inhibitor and in clinical use. Per-residue interaction energy decomposition analysis on the averaged MD structure showed that hydrophobic active residues Trp86, Tyr89 and Tyr108 contributed the most to inhibitor binding. The validated 3D-QSAR model showed a high cross-validated rcv2 value of 0.84 using three principal components and non-cross-validated r2 value of 0.941. It was also revealed that almost all compounds in the test set and training set yielded a good predicted value. Information gained from this study could shed light on the activity of a new series of lead compounds as potential HIV entry inhibitors and serve as a powerful tool in the drug design and development machinery.

Figure 1

2D Structure of the known FDA-approved CCR5 antagonist maraviroc.

Figure 2

Maraviroc structure used as a template for pharmacophore-based and structural similarity-based compound library generation. Pharmacophore selection criteria—green depicts hydrophobicity, purple depicts aromatic and white depicts hydrogen donor. Arrows indicate that constraints have been imposed.

Figure 3

The 2D structures for the oxamino-piperidino-piperidine amide analogs used in the 3D QSAR of this work.

Figure 4

Molecular alignments used in the present study.

Figure 5

(A) Superimposed structures of 3ODU [16] and modeled CCR5 enzyme (blue) with CCR5 antagonist, maraviroc [24]; (B) The 2D sequence alignment of 3ODU and the homology model generated for our study. Yellow highlighting represents α-helices and green highlighting represents β-sheets. Sequences outlined in red lack 3D crystal structure.

Figure 6

The top 10 ranked ZINC compounds from both the 2D similarity-based and pharmacophore-based libraries.

Figure 7

Docking conformations of the known CCR5 antagonist (maraviroc) and the top 10 ranked docked compounds from both the pharmacophore-based and structure-based libraries determined in this study, all-complexed with the CCR5 enzyme.

Figure 8

The binding energies determined in our study and were compared against IC₅₀ values for the compounds assayed. The higher the binding affinity, the higher the IC₅₀.

Figure 9

The highest ranked virtual screening hit lead complexes with CCR5 subjected to MD simulations. Structure-based compound (ZINC71849459) in complex with CCR5 (Black). Pharmacophore-based compound (ZINC00634884) with CCR5 (Blue).

Figure 10

Per-residue interactions for the highest ranked compounds with the best binding energy from the structure-based and pharmacophore-based libraries.

Figure 11

Pharmacophore-based compound (ZINC00634884) with CCR5 (A) and Structure-based compound (ZINC71849459) in complex with CCR5 (B), respectively, showing the hydrogen bonding and electrostatic interactions with the enzyme’s active site using MOE.

Figure 12

Correlation graph between the experimental 1/log IC50 and predicted 1/logIC50.