Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge

Abstract

As part of the SAMPL4 blind challenge, filtered AutoDock Vina ligand docking predictions and large scale binding energy distribution analysis method binding free energy calculations have been applied to the virtual screening of a focused library of candidate binders to the LEDGF site of the HIV integrase protein. The computational protocol leveraged docking and high level atomistic models to improve enrichment. The enrichment factor of our blind predictions ranked best among all of the computational submissions, and second best overall. This work represents to our knowledge the first example of the application of an all-atom physics-based binding free energy model to large scale virtual screening. A total of 285 parallel Hamiltonian replica exchange molecular dynamics absolute protein-ligand binding free energy simulations were conducted starting from docked poses. The setup of the simulations was fully automated, calculations were distributed on multiple computing resources and were completed in a 6-weeks period. The accuracy of the docked poses and the inclusion of intramolecular strain and entropic losses in the binding free energy estimates were the major factors behind the success of the method. Lack of sufficient time and computing resources to investigate additional protonation states of the ligands was a major cause of mispredictions. The experiment demonstrated the applicability of binding free energy modeling to improve hit rates in challenging virtual screening of focused ligand libraries during lead optimization.

Fig. 1

10 % enrichment factors for all of the HIV integrase SAMPL4 submission as computed by the SAMPL4 organizers. The docking and binding free energy-based submission described here corresponds to id 135, second from left

Fig. 2

Early ROC curve for the classification of binders and non-binders to the LEDGF site of integrase. The ROC curve based on binding free energy rankings is in red (top), the ones based on binding energy rankings and reorganization free energy rankings are in green (middle) and blue (bottom) respectively. The straight black line is the 1:1 line corresponding to random picking. Results for sorting reorganization free energies from low to high are shown here, the reverse rankings yielded poorer performance

Fig. 3

Examples of LEDGF integrase binders characterized by binding energies and reorganization free energies of small magnitude

Fig. 4

Reorganization free energies for a subset of complexes versus, a number of rotatable bonds of the ligand and, b root mean square fluctuation of the residues of the LEDGF binding site. The correlation coefficients are 0.59 and 0.53, respectively

Fig. 5

Chemical scaffolds of the integrase LEDGF binders in SAMPL4

Fig. 6

The dominant binding mode adopted by the LEDGF inteegrase binders

Fig. 7

Modeled structures of the complexes of two non-binders (AVX40910_0 and AVX62730_0) with the LEDGF site of integrase. These examples illustrate that occlusion of the P1 hydrophobic site is necessary for strong binding

Fig. 8

Modeled structures of the complexes of AVX17284_0 (a non-binder) and AVX17285_0 (a binder) with the LEDGF site of integrase. The buried amide group is responsible for the poor binding of the former

Fig. 9

Examples of nonbinders that have both correlated hydrogen bonds and occupied P1 sub-pockets. The binding of these ligands is severely hindered by the large reorganization free energy penalties, which are not clearly evident from the structures alone