In-Silico Discovery of Influenza PA and PB-2 Polymerase Complex InhibitorDr. Héctor Maldonado1, Carla Figueroa García 2,3, Crystal Colón Ortiz2, 31 Universidad Central del Caribe Medical School, 2 University of Puerto Rico at Cayey, 3 RISE ProgramAbstract:Introduction:Influenza is a contagious respiratoryillness that spreads from person to personthrough air via coughs or sneezes. This iscaused by a group of viruses namedinfluenza viruses.(http://www.bcm.edu/molvir/influenza).They pertained to the Orthomyxovirusfamily. In the 20thcentury influenza viruseshad cause the threemajor pandemics in theworld (Korteweg and Gu, 2010). Of thesethree, the death toll range up to 50 millionworldwide in the 1918.Current treatments options arelimited to the antivirals Tamiflu andRelenza. Because influenza kills more than50,000 people yearly in the United States,(structural insight.) an important question isleft to answer: what component of the DNAvirus can be targeted to develop newalternatives in order to reduce this cipher?This virus contains a polymeraseprotein composed of PA, PB1, and PB2 withmultiple enzymatic activities for catalyzingviral RNA (vRNA) transcription andreplication (structure function etc). Eachcomponent has a different function in thereplication and transcription of the vRNA.PA is the key protein in the polymerase andis required for replication and transcriptionof vRna and as endonuclease of the cap ofthe RNA primer. PB1 is essential in order tobind the viral promoter and is responsiblefor elongation and cap cleavage activities ofthe vRNA. PB2 is in charged fortranscription of vRNA and can bind to themethylated cap-1 for cleavage by the PB1subunit.( STRUCTURE FUNCTION) Sincethis complex is indispensable for the viralreplication it makes it a good target for drugdevelopment.Therefore the inhibition of the PB2complex will stop the replication of thevirus. The use of In-Silico for new drugdevelopment will provide possible novelalternatives. To discover potential drugs thatcan fulfill the PB2 space the design of apharmocophore model that establishes thespatial arrangement of the chemicalcharacteristics is essential.
Materials and Methods:After recognizing a biological problem(Influenza) and a therapeutically relevantprotein target (Polymerase), several stepsshould be follow to identify optimalcompounds.Identification of optimal target for drugdevelopment: benzene mappingThe target protein was downloaded from theProtein Data Bank(http://www.pdb.org/pdb/home/home.do)and its receptor/ target interaction wasprepared. A grid that covered the area ofinterplay was generated. After the file wasconfigured the benzenes were docked andthe results were analyzed in PyMol(http://www.pymol.org ). At the end thebenzene clusters that showed the bestaffinity were selected.Pharmacophore identification and modelgenerationThe benzene clusters were combined withthe protein in order to analyze theinteraction with Ligand Scout. At the end,using these characteristics a Pharmacophoremodel was developed.Primary Screening: Filtering of drugdatabaseThe pharmacophore model was screenedand the results were transformed from .sdf to.mol using Ligan scout(http://www.pymol.org ) and then to .pdbqtutilizing Racoon.Secondary Screening: Docking screeningThe results from the first screening werescreend again with AutodockVina software(http://vina.scripps.edu/). The top hits wereranked based on the analysis of the resultsthat were organized in affinity.ResultsThe 3D structure downloaded(Figure1.)from the Protein Data Bank had the PDB ID:3AG1.The grid options for the Benzene mapwereX dimension- 48, Y dimension-50, andZ dimension-42. The center grid optionsselected were: Center X- 16.342, Center Y-0.396, and Center Z-0.497. The “hot spots”selected from the docking of the benzenewere :1, 36, 41, 79, and 93. Thepharmacophore model created using thesele. Then, ZincPharmer was used in(http://zinc.docking.org/) a primaryscreening in order to identify top hits thatcould lead to a better identification ofcompounds. Finally a secondary screeningwas run to identify the compounds that hadthe highest binding energy and affinity.Using the Benzenes that presented thehighest affinity ( 1, 36, 41, 79, and 93), apharmacophore model was created, which isshown in Figure 2. Based on thepharmacophore model, ZincPharmeridentified 160,972 hits. After the molecularweight was restricted to ≤ 500, only 80,231hits were founded. These were divided in sixFigure2. Final Pharmacophore model
groups that shared similar molecular weightsand can be observed in Figure 3.Molecular Weight HitsGroup I ≤ 375 13,379Group II 376 ≤ MW ≤ 400 11,752Group III 401≤ MW ≤430 17,405Group IV 431 ≤ MW ≤455 16,007Group V 456 ≤ MW ≤48 5,998Top 25 hits of the secondary screening wereorganized in binding energy. These can beobserved in Figure 4.Results http://zinc.docking.org/Data for resultsFigure3. Division of hits based on molecularweight
1. The model used was 3A1G2. Phenilanina 699A ( interacción principal) Crea un ambiente hidrofóbico3. 3ª1 g grados Armstrong 1.70 posicion 1-374. Gridoptions- X dimenssion 48- Y dimenssion 50- Z dimenssion 42- Spacing 1.005. Center Grid Box- Center x -16.342- Center y 0.396- Center z 0.4976. num_modes 2007. exhouvtiness 5008. Benzene used for the creation of the model- 1- 36- 41- 79- 939. CompuestosEncontrados: 160,972 hits10. Filtración:11. Grupo I ≤ 375 (13,379)12. Grupo II 376 ≤ MW ≤ 400 ( 11,752)13. Grupo III 401≤ MW ≤430 (17,405)14. Grupo IV431 ≤ MW ≤455 (16,007)15. Grupo V456 ≤ MW ≤48 (5,998)