The Award winning poster presented in ABRCMS 2015

1. 0
5
10
15
20
25
30
35
40
45
-10.4 -10.3 -10.2 -10.1 -10 -9.9 -9.8 -9.7 -9.6 -9.5
Compounds with Leading “Binding Energy” per Model
Model A Model B Model C
Glutamate is an excitatory neurotransmitter associated with many important brain functions. The
metabotropic glutamate receptor 3 (mGluR3) is an inhibitory auto-receptor that regulates glutamate
presynaptic release through the use of G proteins. Although still a controversial topic, a large number of
scientific reports have suggested that this receptor is associated with many neurological disorders,
including a variety of psychiatric conditions. Discovery of highly selective mGluR3 (small chemical
compounds) antagonists could lead to more conclusive evidence since most of the currently available
inhibitors target both mGluR2 and mGluR3. Moreover, drug-like compounds with high affinity and
selectivity for this receptor will have broad potential as psychopharmacological agents that can be useful
for treatment of several psychiatric conditions. In the other hand, advances in computer hardware and
software have allowed for the rapid development of computer-aid “In Silico” methodologies for the
screening of large databases of small chemical compounds. Therefore, as part of this research project we
are testing the hypothesis that: “Selective and high affinity inhibitors of mGluR-3 can be found using our
Drug Discovery Strategy based on our novel “In Silico” approach”. We employed this innovative In Silico
methodology for the screening of a massive quantity of drug-like small chemical compounds for possible
candidates with high affinity for the target receptor. To that end, the 3D structure of the target receptor was
analyzed for potential for chemical interactions or features. A pharmacophore model was created (Ligand
Scout software) based on those predicted features and used to filter (ZincPharmer pharmacophore search
software; zincpharmer.csb.pitt.edu/) a large (>18 million) drug-like compounds (ZINC drug-like database;
www.zinc.org), and only compounds fulfilling all requirements imposed by the model where selected for
further analysis. Docking of the selected group of compounds where performed in a high performance
computer facility (UPR-HPCf; www.hpcf.upr.edu/) with the aid of Autodock Vina software. Results from this
part of the study where organized and compounds ranked according to their predicted binding energy.
Over three million compounds where tested with >130 compounds found to have a predicted binding
energy below -9.6 kcal/mol. From this group we have selected the top 18 compounds (binding energies
below -10.0 kcal/mol) for further analysis in a bioassay for potency and selectivity for mGluR3 receptor.
Based on these preliminary results we can conclude that our In Silico approach has resulted in the
identification of several compounds as candidates for metabotropic glutamate receptor 3 inhibitors.
Potency and selectivity of these compounds remains to be determined in future studies employing an
appropriate bioassay.
In Silico discovery of Metabotropic Glutamate Receptor-3 (mGluR-3) inhibitors
Juan E. Maldonado Weng1, Walter I. Silva, PhD.2 and Héctor M. Maldonado, PhD.3
Universidad de Puerto Rico1, Cayey, Puerto Rico; 2University of Puerto Rico, Medical Science Campus 3Universidad Central del Caribe, Medical School
Introduction
Fig. A: Glutamate and Neuron Synapse. Glutamate is an excitatory neurotransmitter utilized widely throughout the brain. All
neurons have a significant amount of glutamate to carry out various forms of signals to other neurons and cells. Once released
in the synapse, glutamate interacts with a series of receptors within the brain. Due to this reason, it is complicated to assign a
particular function to glutamate. Furthermore, glutamate is considered to have a key role in every major behavioral and
physiological function.
Fig. B: Glutamate Receptors. The family of glutamate receptors divided by categories: ionotropic and metabotropic. Ionotropic
glutamate receptors are fast acting signaling receptors; metabotropic receptors have slower signaling due to a second
messenger system.
“Selective and high affinity inhibitors of mGluR-3 can be found using our Drug Discovery Strategy
based on an In Silico approach.”
Methodology
3D Structure
www.pdb.org
PyMol
3SM9
BioAssay
Secondary Screening: (AutoDock)
Primary Screening:
Pharmacophore Model
(ZincPharmer)
High Affinity
Lead
Compounds
Compounds selected
by the model
Identification of
Lead Compounds.
(Ranking of binding energies)
Pharmacophore identification
and Pharmacophore Model
Generation (LigandScout)
Therapeutically
relevant protein
Target:
mGluR3
Biological Problem
mGluR3 associated disorders
Benzene
Mapping
Identification of
chemical features
from Inhibitor: LY341495
Results
Conclusion
Future Works
 Hot-Spots were identified using benzene mapping and combined with additional chemical features
found in previous reported inhibitors in a new hybrid pharmacophore model.
 A large group of compounds (194) with predicted high binding energy (≤ -9.5 kcal/mol) were identified in
our first In Silico campaign.
 Use of Pharmacophore model A resulted in a larger number of compounds with predicted Binding
Energy below -9.5 (142 compounds)
Acknowledgements
Continue altering search parameters to find more variant compounds with high binding energy. This would
provide a broader perspective in finding suitable candidates for mGluR3 inhibitors. Establish a bioassay
for mGluR3 activity in order to test some of the small chemical compounds identified in our in silico study.
UPR-Cayey RISE Program
Instituto de Investigaciones Interdisciplinarias
Receptor Locations Function
mGluR2 Widespread in neurons Inhibition of Adenylyl cyclase
Activation of K+ channels
Inhibition of Ca++ channels
mGluR3 Widespread in neurons, astrocytes
Table A: Group II mGluR. The metabotropic glutamate receptors 2 and 3 are said to have similar functions. Most inhibitors
target both receptors without selectivity. This lack of selective inhibitors hinders the ability to differentiate and fully characterize
these receptors.
Specific Aims
• Create a pharmacophore model that combine the chemical features obtained from the analysis of currently known inhibitor (LY341495) and
the benzene mapping.
• Perform a virtual pre-screening (filtering) of ZINC Drug Database (>20 million drug-like compounds) with our pharmacophore model using the
web based resource ZincPharmer (http://zincpharmer.csb.pitt.edu/).
• Perform a secondary screening (virtual docking) to identify “top-hits” or potential lead compounds (AutoDock Vina).
• Initiate validation of “top-hits” with bioassay, followed by drug development phase with in silico modification/optimization and re-testing of “top-
hits”.
Abstract Hypothesis Results
Model
Compounds with
Leading BE
A B C
-10.4 3 0 0
-10.3 0 0 0
-10.2 2 0 0
-10.1 1 1 1
-10 8 0 0
-9.9 11 3 4
-9.8 18 2 1
-9.7 17 4 9
-9.6 40 1 7
-9.5 42 1 18
Total number of
compounds
142 12 40
ZincPharmer
http://zincpharmer.csb.pitt.edu/pharmer.html
Model
Number of Compounds
fulfilling pharmacophore
models
conditions
A 2,989,147
B 197,655
C 988,798
LY-341495
Benzene
LY-341495
Pharmacophore features
Benzene 1
Pharmacophore features
A B C
Fig. 1: Tri-dimensional structure of mGluR3 in different representations. The tridimensional structure of the glutamate receptor (3SM9.pdb) was obtained from the
Protein Data Base (www.pdb.org). Illustrated in this figure are three different representations of the receptor: (a) the unaltered form, (b) ribbons and cartoon, and (c) surface.
The surface representation is better suited for the visualization of the receptor and potential area of interactions with drugs.
Figure 2: Benzene Docking and Drug Interactions Pharmacophore Model. This figure illustrates the process of generating an hybrid pharmacophore model. In part B,
the interactions of either LY341495 (top) or localization of the best four benzene clusters (obtained by in silico docking) are represented (bottom). In part C, the
pharmacophore features obtained by the drug-receptor interaction (top) and benzene features (bottom) are depicted. Finally, part D illustrates examples of pharmacophore
models obtained from interactions of either drug (top) or benzene molecules with the receptor.
Figure 3: Hybrid Pharmacophore models for mGluR3. This figure illustrates the three hybrid pharmacophore models created from the analysis presented in Fig 2 and
used in the present study.
Figure 4: The results from screening in the ZINCPharmer database. The following parameters were utilized in this initial
screening: [0≤Molecular Weight≤450; 0≤Rotatable Bonds≤5] with no repeating structures. The table presents the total number of
compounds fulfilling the model conditions.
Figure 5: In silico docking screening of selected compounds. This figure illustrates the (A) grid created for the virtual
docking of the selected compounds and (B) results from screening with Autodock Vina software.
Figure 6: Example of results obtained by virtual docking with Autodock Vina. This figure represents a visual comparison
of a candidate obtained from the virtual screening (shown in right) interacting with mGluR3 in a similar fashion as the inhibitor
LY341495 (shown in left).