Search for non-nucleoside inhibitors of HIV-1 reverse transcriptase using chemical similarity, molecular docking, and MM-GB/SA scoring

Abstract

A virtual screening protocol has been applied to seek non-nucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) and its K103N mutant. First, a chemical similarity search on the Maybridge library was performed using known NNRTIs as reference structures. The top-ranked molecules obtained from this procedure plus 26 known NNRTIs were then docked into the binding sites of the wild-type reverse transcriptase (HIV-RT) and its K103N variant (K103N-RT) using Glide 3.5. The top-ranked 100 compounds from the docking for both proteins were post-scored with a procedure using molecular mechanics and continuum solvation (MM-GB/SA). The validity of the virtual screening protocol was supported by (i) testing of the MM-GB/SA procedure, (ii) agreement between predicted and crystallographic binding poses, (iii) recovery of known potent NNRTIs at the top of both rankings, and (iv) identification of top-scoring library compounds that are close in structure to recently reported NNRTI HTS hits. However, purchase and assaying of selected top-scoring compounds from the library failed to yield active anti-HIV agents. Nevertheless, the highest-ranked database compound, S10087, was pursued as containing a potentially viable core. Subsequent synthesis and assaying of S10087 analogues proposed by further computational analysis yielded anti-HIV agents with EC50 values as low as 310 nM. Thus, with the aid of computational tools, it was possible to evolve a false positive into a true active.

Figure 1

The 26 known non-nucleoside HIV-RT inhibitors used in the virtual screening.

Figure 2

Comparison between the docked (light gray) and the observed crystal structures for six NNRTIs. The RMSd values are noted. All NNRTIs were docked into the 1rt4 structure.

Figure 3

Docking poses for three additional known NNRTIs, TMC-125, S-DABO-3w, and DPC-083.

Figure 4

MM-GB/SA Score (left) and Glide XP function (right) versus pIC50 for 11 NNRTIs.

Figure 5

ΔΔG_bind versus XP docking score. S-DABO-3w, the ligand with the most favorable binding energy (ΔG_bind), was used as reference to obtain ΔΔG_bind.

Figure 6

Top 20 compounds from the MM-GB/SA post-scoring after docking into wild-type HIV-RT.

Figure 7

Energy-minimized complexes between HIV-RT and S10087, NRB03328, and KM06633.

Figure 8

(a) Overlay of the energy-minimized complexes for the best scoring 2-pyrimidines (23p (yellow), 23h (green), and 23o (magenta)). (b) Hydrophobic interactions between the arene pocket (CPK) and unsaturated groups; 3,3-dimethylallyloxy in UC-781 (dark green), methyl-pyridinyl in nevirapine (blue), benzyl in MKC-442 (magenta), 3,3-dimethylallyl in 9-Cl TIBO (light green), ethynylcyclopropyl in Sustiva (yellow), and phenoxy in TMC-125 (light blue).

Figure 9

Top 20 compounds ranked by the MM-GB/SA post-scoring after docking into the K103N variant of HIV-RT.

Figure 10

Energy-minimized complexes between K103N-RT and S-DABO-3w, DPC-963, 23o, and KM06633. All four ranked among the best 20 compounds for both HIV-RT and K103N-RT.

Figure 11

Modifications of S10087 that generated the anti-HIV agent JL0203.