1) The document describes an in-silico drug discovery project to identify inhibitors of the influenza virus polymerase PB1-PB2 protein complex. 2) The researchers used computational modeling techniques like benzene mapping, pharmacophore modeling, and molecular docking to identify potential binding sites on the PB1-PB2 complex and screen databases of drug-like molecules to discover top hit compounds. 3) Their methodology identified 50 top hit compounds from an initial screening that were further analyzed in secondary screening, and the 4 compounds with highest binding energies were identified as potential leads for inhibiting the PB1-PB2 interaction and warrant further testing in viral replication assays.

1. In-Silico Drug Discovery and Development.
Project: In-Silico Discovery of Influenza
Virus Polymerase PB1-PB2 Protein
Complex Inhibitors
Carla J. Figueroa1,2, Crystal K. Colón1,2
Dr. Hector M. Maldonado3
1RISE Program, 2Univeristy of Puerto Rico at Cayey;
3Universidad Central del Caribe, Medical School

2. Outline of the Presentation
• Introduction
• Hypothesis and Objectives
• Methodology
• Results
• Conclusions
• Future Studies
2
In-Silico Discovery of Influenza Virus Polymerase PB1-PB2 Protein Complex Inhibitors

3. Introduction
• What is influenza?
– Influenza is a contagious respiratory illness that
spreads via air.
– It kills more than 50,000 people per year in the
United States.
• Current treatments
– Limited to Tamiflu and Relenza.
3http://www.ifpma.org/resources/influenza-
vaccines/the-influenza-virus/the-influenza-virus.html

4. Introduction
• Polymerase components and their functions
– PBA: replication of viral RNA
– PB1: essential to bind viral promoter and
responsible for elongation
– PB2: transcription of viral RNA
• Since all components are indispensable for
viral replication it makes a good target for
drug development.
4

5. 5
Problem
Hypothesis
Significance
Current treatment options are limited to Tamiflu and Relenza, with
many reported resistant viral strains.
Highly conserved protein-protein interaction interface, present in
Influenza A Virus Polymerase subunits (PB1 and PB2), represent
potencial new targets for antiviral drug development.
Influenza is an Infectous Disease caused by a virus that kills hundreds of
thousands each year (seasonal epidemics alone), with the influenza-A
serotype implicated in all INF-A related pandemics.

6. Objectives
1. Identification of potencial new targets for antiviral
drug development in the polymerase acidic proteins
(PB1-PB2).
2. Identification of “hot spots” for drug development in
these new targets.
3. Create Pharmacophore Model (LigandScout) for the
selected targets and perform a virtual pre-screening
of the ZINC Drug-Like and Drug-fragments Databases
against our model.
4. Perform secondary screening to identify “top-hits” or
potential lead compounds (AutoDock Vina).
6

7. 3D Structure
www.pdb.org
PyMol
3A1G
BioAssay
Secondary Screening: (AutoDock)
Primary Screening: Pharmacophore
Model (ZincPharmer)
High Affinity
Lead
Compounds
Identification of Top Hits
Identification of
Lead Compounds.
(Ranking of binding
energies)
Pharmacophore
identification and
Pharmacophore Model
Generation (LigandScout)
Therapeutically
relevant protein
Targets:
PB1 - PB2
Biological Problem
(Influenza Progression)
Drug-like
Databases
(17 million
drug-like
compounds)
Benzene
Mapping
Drug-like
Databases
(17 million
drug-like
compounds)

8. Methodology
8
Software Used:
• PyMOL Molecular Graphics System v1.3 http://www.pymol.org
• AutoDock (protein-protein docking software) http://autodock.scripps.edu/
• Auto Dock Tools: Graphical Interfase for AutoDock
http://mgltools.scripps.edu/downloads
• AutoDock Raccoon: an automated tool for preparing AutoDock virtual screening.
http://autodock.scripps.edu/resources/raccoon
• AutoDock Vina: improving the speed and accuracy of docking with a new scoring
function, efficient optimization and multithreading. http://vina.scripps.edu/
• LigandScout: Advanced Pharmacophore Modeling and Screening of Drug
Databases. http://www.inteligand.com/ligandscout/
Databases Used:
• SwissProt/TrEMBL; (Protein knowledgebase and Computer-annotated
supplement to Swiss-Prot) http://www.expasy.ch/sprot/
• National Center for Biotechnology Information (NCBI), Basic Local Alignment
Search Tool (BLAST) http://www.ncbi.nlm.nih.gov/blast/Blast.cgi
• Research Collaboratory for Structural Bioinformatics (RCSB) www.pdb.org
• ZINC: A free database for virtual screening: http://zinc.docking.org/

9. Results
9

10. 10
In-silico Discovery of Influenza Virus
Polymerase Complex Inhibitors
PB1 Fragment
PB2 Fragment
PB1-PB2
PDB ID: 3A1G

11. 11
B
E
N
Z
E
N
E
M
A
P

12. 12
Ben 01
Ben 36
Ben 41 Ben 79
Ben 93
Ben 131
1
36
41
79
93 131
Generation of Pharmacophore model: merged features from benzene mapping.
Final Pharmacophore model

13. Top 50 Hits of first round of screening of
Influenza A (PB1-PB2 target).
# Drug Code Binding energy (kcal/mol)
1 IPB1-1 -10.5
2 IPB1-2 -10.2
3 IPB1-3 -10.2
4 IPB1-4 -10
5 IPB1-5 -10
6 IPB1-6 -9.9
7 IPB1-7 -9.9
8 IPB1-8 -9.9
9 IPB1-9 -9.8
10 IPB1-10 -9.8
11 IPB1-11 -9.8
12 IPB1-12 -9.8
13 IPB1-13 -9.8
14 IPB1-14 -9.8
15 IPB1-15 -9.8
16 IPB1-16 -9.7
17 IPB1-17 -9.7
18 IPB1-18 -9.7
19 IPB1-19 -9.7
20 IPB1-20 -9.7
21 IPB1-21 -9.7
22 IPB1-22 -9.6
23 IPB1-23 -9.6
24 IPB1-24 -9.6
25 IPB1-25 -9.6
# Drug Code Binding energy (kcal/mol)
26 IPB1-26 -9.6
27 IPB1-27 -9.6
28 IPB1-28 -9.6
29 IPB1-29 -9.6
30 IPB1-30 -9.5
31 IPB1-31 -9.5
32 IPB1-32 -9.5
33 IPB1-33 -9.5
34 IPB1-34 -9.5
35 IPB1-35 -9.5
36 IPB1-36 -9.5
37 IPB1-37 -9.5
38 IPB1-38 -9.5
39 IPB1-39 -9.5
40 IPB1-40 -9.5
41 IPB1-41 -9.5
42 IPB1-42 -9.5
43 IPB1-43 -9.5
44 IPB1-44 -9.4
45 IPB1-45 -9.4
46 IPB1-46 -9.4
47 IPB1-47 -9.4
48 IPB1-48 -9.4
49 IPB1-49 -9.4
50 IPB1-50 -9.4
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14. 14
PB1 with benzene clusters
PB1 with IPB1-1
PB1 with IPB1-2
PB1 with IPB1-3
Potential PB1 & PB2interaction inhibiting drugs

15. Conclusions
• High affinity clusters or “Hot-spots” were identified
with benzene mapping
• With this information an initial Pharmacophore model
was developed
• This Pharmacophore model was used to perform a
primary screening (filtering) of a large database
• 80,231 compounds were identified in that initial
screening that fulfill all requirements of this model
• Secondary screening (docking) was performed
• The result were organized by binding energy ranking
and the top hits identified
15

16. Future Studies
• Use the top hits and test them in viral
replication bioassays.
16

17. References
• Baylor College of
Medicine[Internet].2013.Houston(Texas):Department of Molecular
Virology and Microbiology;[cited 2013 05 03].
• Kortweg C., Gu J.2010.Pandemic influenza A(H1N1) virus infection
and avian influenza A (H5N1) virus infection: a comparative analysis.
88(4):575-587.
• Sugiyama K., Obayashi E., Kawaguchi A., Suzuki Y., RH Tame J.,
Nagata K., Park S.2009.Structural insight into the essential PB1-PB2
subunit contact of the influenza virus RNA polymerase. EMBO
J.28(12):1803-1811.
• YingFang L., ZhiYoung L., Bartlam M.,Zihe R. 2009.Structure-function
studies of the influenza virus RNA polymerase PA subunit.
SCLS.52(5):450-458.
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