Introduction,importance and scope of horticulture.pptx
In Silico discovery of Metabotropic Glutamate Receptor-3 (mGluR-3) inhibitors Presentation
1. In Silico discovery of Metabotropic
Glutamate Receptor-3 (mGluR-3)
inhibitors.
Juan E. Maldonado Weng1
Walter I. Silva, PhD.2
Héctor M. Maldonado, PhD.3
1University of Puerto Rico, Cayey
2University of Puerto Rico, Medical Science Campus
3Universidad Central del Caribe, Medical School
4. • The metabotropic glutamate receptor 3 (mGluR3) has been found to be associated
to an increased risk of bipolar disorders, schizophrenia, alcoholism, anxiety
disorders, an a variety of other mental disorders.
• Chemical compounds with potential to exert pharmacological actions as agonists,
antagonists, or allosteric modulators of this receptor are currently been evaluated
for clinical applications.
• Examples include agonists like LY354740 with potential in the treatment of anxiety
and drug addiction (PMID 9046344), and LY-341495 an antagonist with
antidepressant properties (PMID 18164691).
• Clearly, the number and variety of chemical compounds with potential to interact
with this receptor suggested that this receptor belongs to the limited family of
highly “druggable” targets.
• With this in mind we decided to test the hypothesis that: Selective and high
affinity inhibitors of mGluR-3 can be found using our Drug Discovery
Strategy based on an In Silico approach.
Background, Significance and Hypothesis:
5. • 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”.
Objectives
6. 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
Drug-like
Databases
(17 million
drug-like
compounds)
Benzene
Mapping
Identification of
chemical features
from Inhibitor: LY341495
11. 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 BE per Model
Model A Model b Model C
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
Results:
12. Conclusions
• 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)
Future Studies:
Establish in our laboratory a bioassay for mGluR3 activity in
order to test some of the small chemical compounds identified in
our in silico study.
14. In Silico discovery of Metabotropic
Glutamate Receptor-3 (mGluR-3)
inhibitors.
Juan E. Maldonado Weng1
Walter I. Silva, PhD.2
Héctor M. Maldonado, PhD.3
1University of Puerto Rico, Cayey
2University of Puerto Rico, Medical Science Campus
3Universidad Central del Caribe, Medical School
