Virtual Screening for RNA-Interacting Small Molecules - Biophysical Approaches to Translational Control of Gene Expression - page 236

TAR RNA ligands (Filikov et al. 2000 ; Lind et al. 2002 ) . To circumvent the limitation
of incorporating RNA flexibility for structure-based virtual screening, and to find
novel scaffolds for TAR-Tat inhibitors, ligand-based virtual screening was con-
ducted. The SQUID fuzzy pharmacophore search method successfully identified a
novel heterocyclic compound with an order of magnitude improved activity com-
pared to known phenothiazine compounds (Renner et al. 2005 ) .
In 2011, Al-Hashimi's group developed the most outstanding technology for
RNA-targeted virtual screening through intensive generation of an ensemble of
TAR RNA conformers and a robust re-docking validation test. Their strategy was
effectively applied to virtual screening of a relatively small-sized chemical library
containing 51,000 compounds, and they identified netilmicin, a selective HIV-1
TAR RNA binder, that inhibited HIV-1 replication in vivo (Stelzer et al. 2011 ) .
Details of their study are described below.

12.4.1.1 Validation of Docking Program

To test the accuracy of docking, a total of 96 small molecule-bound RNA structures
downloaded from the PDB were assessed for docking performance. All docking
performances were carried out using the ICM docking program (Abagyan and Totrov
1994 ) and results were evaluated by ICM Score and RMSD between native ligand
(extracted from RNA structures) and predicted orientation after docking. The bind-
ing energies based on the ICM Score were predicted with high accuracy ( R = 0.71).
In more than half of cases (53%), the predicted conformations matched the X-ray or
NMR structures within 2.5 Å RMSD.

12.4.1.2 Preparation of HIV-1 TAR RNA Ensemble

To decide on a suitable RNA ensemble, the accuracies of docking with two sets of
RNA ensembles of HIV-1 TAR were compared. One ensemble, named TAR NMR-MD ,
consisted of 20 conformers generated by SAS selection (select-and-sample strategy,
Frank et al. 2009 ) . To construct TAR NMR-MD , HIV-1 TAR structure (PDB id: 1ANR,
Aboul-ela et al. 1996 ) was downloaded and molecular dynamics simulations were
performed by measuring NMR residual dipolar couplings (RDC) in elongated RNA.
The other RNA ensemble, named TAR MD , consisted of 20 randomly selected snap-
shots from an 80-ns MD simulation of apo-TAR with backbone RMSDs ranging
from 3 to 80 Å. The X-ray structure (PDB id: 397D, Ippolito and Steitz 1998 ) and
20 NMR structures (PDB id: 1ANR, Aboul-ela et al. 1996 ) of apo-TAR were down-
loaded from the PDB. A test set ligand library containing 38 known ligands for TAR
RNA was obtained from the published literatures. To test the accuracy of the RNA
ensemble, virtual screening of the test set ligands targeting the RNA ensembles,
TAR NMR-MD and TAR MD , was conducted, respectively. Virtual screening was conducted
using ICM after binding pockets were predicted by the ICM PocketFinder module
based on the calculated surface area and volume of cavities on the receptor surface.