5.3.1 Case study: HIV-1 RT RNase H Inhibition Screening
Many drugs or inhibitors potentially bind with metal ions in the catalytic site of
enzyme or receptors in order to exhibit the therapeutic effect, e.g. magnesium ions
containing enzymes such as HIV-1 integrase and RNase H [45, 46]. Thus, a good
scoring function should be able to accurately calculate the metal–inhibitor interaction which impacts an overall binding affinity of individual compounds. Although
the metal-binding term in the docking-scoring function is included (e.g. glide
score), it considers only the anionic or highly polar interactions; therefore, ranking
of actives is not appropriately achieved. On the other hand, it has been reported
previously that magnesium ions in the HIV-1 reverse transcriptase-associated
ribonuclease H (RNase H or RNH) play an essential role in binding and positioning
of RNA–DNA duplex (natural substrate) during digestion in the viral genome
reverse transcription process. Inhibition of this enzyme by chelation of magnesium
ions (active site binder) is provided as an attractive approach in anti-HIV RT
inhibition based drug design and discovery projects.
It is well known that the active site binder mechanism of inhibition is primarily
through chelation with magnesium ions; thus, binding affinity prediction model was
improved through the use of QM-based calculations by primarily considering the
chelation mechanism of inhibitors with the catalytically active magnesium ions.
This could be useful as a high-throughput filter in the virtual screening process.
The simplest possible model (scenario 1) to describe the binding of the ligand is
to only describe the chelation process between the magnesium ions and the ligand in
solution yielding the following approximation to Eq. 8. To further refine scenario 1,
we consider in scenario 2 geometry optimization of the protein–ligand complexes
using the Qsite module (version 5.0) of the Schrödinger suite. Here, the magnesium
ions and inhibitors were considered in the QM region (optimized with B3LYP and
the 6-31G(d) basis set). The rest of the protein and water molecules were considered
in the MM region (evaluated using the OPLS 2005 force-field) and kept frozen.
In general, docking methods could also be used for ranking compounds; however, the correlation between scoring functions and experimental values for binding
free energies is rather poor in this case, and one reason is the lack of protein
flexibility in the majority of the docking experiments. The correlation between
molecular docking using glide score and experimental activity was quite low
(n=7; R
2 = 0.098). However, when the atomic coordinates were used for chelation
energy calculations using the DFT-B3LYP method (scenario 2; n=7; R
2 = 0.93)
and FMO methods(R
2 = 0.80-0.94). [47].
As we have discussed above that an effective virtual screening of RNase H
inhibitors from large chemical databases could be achieved using the combination
of docking and QM-based refinement calculations. In order to identify a novel
chemotype for RNase H inhibition and to validate previously developed computational methods, the best models were used to screen the Specs database (containing 277,325 drug-like compounds for purchase) for HIV-1 RNase H inhibition
screening (Fig. 3). A set of 1205 compounds was obtained at the end of the
docking-based virtual screening, and these compounds were subsequently used for
234
N. A. Murugan et al.
Many drugs or inhibitors potentially bind with metal ions in the catalytic site of
enzyme or receptors in order to exhibit the therapeutic effect, e.g. magnesium ions
containing enzymes such as HIV-1 integrase and RNase H [45, 46]. Thus, a good
scoring function should be able to accurately calculate the metal–inhibitor interaction which impacts an overall binding affinity of individual compounds. Although
the metal-binding term in the docking-scoring function is included (e.g. glide
score), it considers only the anionic or highly polar interactions; therefore, ranking
of actives is not appropriately achieved. On the other hand, it has been reported
previously that magnesium ions in the HIV-1 reverse transcriptase-associated
ribonuclease H (RNase H or RNH) play an essential role in binding and positioning
of RNA–DNA duplex (natural substrate) during digestion in the viral genome
reverse transcription process. Inhibition of this enzyme by chelation of magnesium
ions (active site binder) is provided as an attractive approach in anti-HIV RT
inhibition based drug design and discovery projects.
It is well known that the active site binder mechanism of inhibition is primarily
through chelation with magnesium ions; thus, binding affinity prediction model was
improved through the use of QM-based calculations by primarily considering the
chelation mechanism of inhibitors with the catalytically active magnesium ions.
This could be useful as a high-throughput filter in the virtual screening process.
The simplest possible model (scenario 1) to describe the binding of the ligand is
to only describe the chelation process between the magnesium ions and the ligand in
solution yielding the following approximation to Eq. 8. To further refine scenario 1,
we consider in scenario 2 geometry optimization of the protein–ligand complexes
using the Qsite module (version 5.0) of the Schrödinger suite. Here, the magnesium
ions and inhibitors were considered in the QM region (optimized with B3LYP and
the 6-31G(d) basis set). The rest of the protein and water molecules were considered
in the MM region (evaluated using the OPLS 2005 force-field) and kept frozen.
In general, docking methods could also be used for ranking compounds; however, the correlation between scoring functions and experimental values for binding
free energies is rather poor in this case, and one reason is the lack of protein
flexibility in the majority of the docking experiments. The correlation between
molecular docking using glide score and experimental activity was quite low
(n=7; R
2 = 0.098). However, when the atomic coordinates were used for chelation
energy calculations using the DFT-B3LYP method (scenario 2; n=7; R
2 = 0.93)
and FMO methods(R
2 = 0.80-0.94). [47].
As we have discussed above that an effective virtual screening of RNase H
inhibitors from large chemical databases could be achieved using the combination
of docking and QM-based refinement calculations. In order to identify a novel
chemotype for RNase H inhibition and to validate previously developed computational methods, the best models were used to screen the Specs database (containing 277,325 drug-like compounds for purchase) for HIV-1 RNase H inhibition
screening (Fig. 3). A set of 1205 compounds was obtained at the end of the
docking-based virtual screening, and these compounds were subsequently used for
234
N. A. Murugan et al.