Chemistry Reference
In-Depth Information
analysis of a family of target proteins allows determination of structural differences, considering the receptor,
important to selectivity, without the dependence on appropriate ligands for a QSAR analysis.
An interesting method based on MIFs developed to predict protein-ligands binding sites is implemented in the Q-
SiteFinder server [30]. Q-SiteFinder uses the interaction energy values between a protein 3D structure and a simple
van der Waals probe (methyl probe) within a 3D grid to determine binding clefts. The probe sites with the most
favorable binding energies are retained and clustered according to their spatial proximity. The probe clusters are
ranked according to their total interaction energies whereas binding sites can be identified and predicted. According
to the results presented in the validation of the method at least one successful prediction in the top three predicted
binding sites is accomplished in 90% of proteins complexes from the PDB dataset used. In Fig.
2
we observe a
successful prediction of the most favorable binding site region in the active site of β-secretase enzyme
Figure 2:
(A) Complex between human β-secretase and a potent inhibitor (PDB code: 2VJ6) in phase with the most favorable
virtual binding site predicted with Q-SiteFinder. (B) Structure of the inhibitor in complex with β-secretase.
GRIND DESCRIPTORS
Pastor
et al.
[19] described the development of a mathematical description of molecules known as GRIND (Grid-
INdependent Descriptors). The innovation of this approach was based on the computation of descriptors with
independence regarding the orientation of the molecular structures in the space. The initial alignment of the series
compounds is widely recognized as one of the most difficult and time-consuming steps in 3D-QSAR . Hence,
considering that the GRIND approach does not require this step, the GRIND descriptors can be obtained easily, even
for a large series of compounds.
The GRIND descriptors have been widely applied in drug Discovery projects usually in 3D-QSAR. The GRIND
descriptors represent the most important GRID-interactions as a function of the distance [31-36]. GRIND encodes
the geometrical relationships between the virtual receptor sites in such a way that they are no longer dependent upon
their positions in the 3D space. The encoding is an auto- and cross-correlation transformation. The GRIND can be
used in chemometric analysis performed with standard analysis methods such as PCA (Principal Component
Analysis) and PLS [19].
Another applicability of GRIND descriptors was described by Li
et al
. [37] combining the use of GRIND
descriptors and support vector machine in order to study a library of 495 compounds with the objective of
discriminating between hERG blockers and nonblockers, since compounds that can inhibit the hERG channel are
prone to develop cardiac toxicity. In this case the GRIND descriptors were used to assess toxicity of compounds in
the early stages of drug discovery.
VIRTUAL SCREENING
Virtual screening (VS) is a strategy for computationally screening a database in search for novel drug leads and has
become a new branch of medicinal chemistry. The basic goal of the virtual screening is the reduction of the
enormous virtual chemical space of small organic molecules, to synthesize and screen against a specific target
