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In silico fixed

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  • 1. In-Silico Discovery of Influenza Virus Polymerase PB1-PB2 Protein Complex InhibitorsDr. Héctor Maldonado1, Carla Figueroa García 2,3, Crystal Colón Ortiz2, 31Universidad Central del Caribe Medical School, 2University of Puerto Rico at Cayey, 3RISEProgramAbstract: Influenza is a contagious illness caused by the influenza viruses. Treatments forthis malady are limited to several antivirals.These viruses contain a polymerase complexcomposed of PA, PB1 and PB2.Since this complex is indispensable for viral replication, it isa good target for drug development. Therefore, the inhibition of the PB2 complex will stopthe replication of the virus. The use of software programs like AutoDockVina andZincpharmer(In-Silico) provided possible novel alternatives for new drugdevelopment.High affinity clusters were identified to develop the pharmacophore model.This model established the spatial arrangement of the chemical characteristics. Theprimary screening identified 80,231 compounds that fulfilled the requirements of themodel. A secondary screening was performed and the results were organized by bindingenergy. Five top hits were identified which were. For future studies these top hits can besubmitted for bioassays and eventually can become potential drugs to treat Influenza.______________________________________________________________________________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 (BCM, 2009). They arepart of the Orthomyxovirus family. In the20thcentury influenza viruses hadcausedthreemajor pandemics in the world(Korteweg and Gu,2010). Of these three, thedeath toll ranged up to 50 million peopleworldwide by1918. Today, in 2010, over500 people in the United States for this ill(CDC,2013).Current treatmentoptions are limitedto the antivirals Tamiflu and Relenza.Influenza kills more than 50,000 peopleyearly in the United States,(Sugiyama,Obayashi, Kawaguchi, Suzuki,Tame, Nagata, Park,2009). An importantquestion is left to be answered,“Whatcomponent of the DNA virus can be targetedto develop new alternatives in order toreduce this cipher?”This virus contains a polymeraseprotein composed of PA, PB1, and PB2 withmultiple enzymatic activities for catalyzingviral RNA (vRNA) transcription andreplication (YingFang, ZhiYoung,Bartlam,Zihe, 2009). Each component has adifferent function in the replication andtranscription of the vRNA. PA is the keyprotein in the polymerase and it is requiredfor replication and transcription of vRna. Itis alsothe endonuclease of the cap of theRNA primer. PB1 is essential in order tobind the viral promoter and it is responsible
  • 2. for the elongation and cap cleavageactivities of the vRNA. PB2 is in chargeofthe transcription of vRNA, and it can bindto the methylated cap-1 for cleavage by thePB1 subunit (YingFang, ZhiYoung,Bartlam,Zihe, 2009).Since this complex is indispensablefor viral replication, it makes it a good targetfor drug development. If an inhibition forthe junction of the PB2 complex isdeveloped, then the replication of the viruswill be stopped.The In-Silico method for drugdevelopment is used to discover potentialdrugs that fulfill these requirements. Itprovides novel alternatives through benzenemapping thatis later turned into aPharmacophore model.The modelestablishes the spatial arrangement of thechemical characteristics for the selectedtarget. This research focuses on theinhibition of the junction of the PB1 andPB2 complexes contained in the Influenzavirus polymerase.Materials and Methods:After recognizing a biological problem(Influenza) and a therapeutically relevantprotein target (Polymerase), several stepsand processes were followed to identifyoptimal compounds that can becomepotential drugs to treat Influenza. Thesesteps are: Identification of optimal target fordrug development:benzenemapping,Pharmacophore identification andmodel generation,Primary Screening:Filtering of drug database and SecondaryScreening: Docking screening. They will beexplained below.Identification of optimal target for drugdevelopment: benzene mappingThe target protein was downloaded from theProtein Data Bank and its receptor/targetinteraction was prepared. A grid thatcovered the area of interplay was generated.After the file was configured, the benzeneswere docked and the results were analyzedin PyMol. At the end the benzene clustersthat showed the best affinity were selected.Pharmacophore identification and modelgenerationThe benzene clusters were then combinedwith the protein in order to analyze theinteraction with the Ligand Scoutprogram.These interactions showed the differentfeatures of the benzenes that were necessaryto develop the Pharmacophore model.Primary Screening: Filtering of drug databaseThispharmacophore model was thenscreened and the results were transformedfrom .sdf to .mol using Ligand Scout andthen transformedinto .pdbqtfiles utilizingtheRacoon program.Secondary Screening: Docking screeningThe results from the first screening werescreened for a second time withtheAutodockVina software.These resultswere then organized by affinity ranking andfrom an analysis the top hits were selected.ResultsThe 3D structure downloaded(Figure1.)from the Protein Data Bank had the PDB ID:3AG1.Figure 1. PB1 fragment of the Polymerase (3A1G).
  • 3. The grid options for the Benzenemap wereX dimension-48, Y dimension-50,and Z 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 theselected benzenes, is as shown in Figure2.The first filtering of the drugdatabase (Zinc Pharmer) with thepharmacophore model using Ligand Scoutshowed 160,972 hits. After restricting themolecular weight to ≤500 only 80,231compounds fitted the inhibition site. Thesecompounds were divided into six groupsbased on molecular weight. Figure 3 showsthe different groups and the amount ofcompounds that each contained.The second screening, performedwith AutoDockVina, showed the bindingaffinity of the compounds from the sixgroups. From the 80, 231 compounds onlyfive showed an optimal binding affinity tobe considered as a potential drug to treatinfluenza. The first 25 hits with the bestbinding affinity are shown in Figure 4.Figure 5 shows how one of thepotential drugs fits the interaction pocketbetween PB1 and PB2 inhibiting theirjunction. Therefore the replication ofthevirus can be stopped.Figure 3. Division of hits based on molecularweight.Figure 2. Final pharmacophore model and selected benzenes.Molecular Weight HitsGroup I MW ≤ 375 13,379Group II 376 ≤ MW ≤ 400 11,752Group III 401≤ MW ≤430 17,405Figure 4. Top 25 hits of secondary screening.Figure 5.PB1 with 1PB1-3.
  • 4. DiscussionAfter the top hits were selected andanalyzed our hypothesis was proven.Thathighly conserved protein-protein interactioninterface, present in InfluenzaA VirusPolymerase subunits (PB1 and PB2),represent potential new targets for antiviraldrug development.By developing apharmacophore model from a benzene map80,231 compounds were found to haveaffinity for the selected site.By selecting the whole protein forthis study, developing an accurate grid was atime consuming task. Not selecting thecorrect dimensions could have lead to notfinding the desired benzenes for the area.The benzenes are selected by the distancethat the clusters cover over the protein. Eachcluster may contain tens of thousands ofbenzenes, so the first that “pops-out” at adifferent and distant site is selected as a“hot-spot”. These “hot-spots” are then usedto develop the pharmacophore model. Thismodel presents the features that eachAfter the final compounds were selected, infuture studies, they are taken through theprocess of a Bio-Assay. During this processorganic compounds are directly tested in theprotein.Several details can be changed inorder to obtain compounds with a betterbinding energy and affinity. For example:the grid options that are selected during thebenzene map creation will determine morespecific benzenes for the area. Thespecificity of the benzenes will providebetter results. This can explain why onlyfive compounds showed an affinity of -10.ConclusionAfter completing the processapproximately five compounds showed abinding energy that can inhibit theinteraction between PB1 and PB2.Highaffinity “hot-spots” were identified withbenzene mapping.These “hot-spots” were agroup of benzenes selected (1, 36, 41, 79,and 93). A pharmacophore model wasdeveloped using them.This model was usedto perform a primary screening of a largedatabase. Approximately 80,231 compoundswere identified. The secondary screeningbenzene possesses and organizes them sothat compounds with similar structurescan be found.From the first screening all of thecompounds that fit the requirements ofthe model were detected. These were thendivided into smaller groups that madetheir management and file conversioneasier and less time consuming. Afterthey were all converted to .pdbqt, asecondary screening was performed andthe compounds were organized byaffinity ranking. Then the compoundswith the highest affinity were selectedand compared directly with the benzenemap in the protein.
  • 5. was performed and the results wereorganized by binding energy ranking.Approximately five compounds (top hits)showed a binging energy of -10.These top hits can be tested later inviral replication bioassay. Also for futurestudies, a grid box that covers the area ofinteraction better will improve thepharmacophore model. Therefore,compounds with a higher binding affinitywill be found.References1. Kortweg C., Gu J.2010.Pandemicinfluenza A(H1N1) virus infectionand avian influenza A (H5N1) virusinfection: a comparative analysis.What journal is it? 88(4):575-587.2. Sugiyama K., Obayashi E.,Kawaguchi A., Suzuki Y., RH TameJ., Nagata K., Park S.2009.Structuralinsight into the essential PB1-PB2subunit contact of the influenza virusRNA polymerase. EMBOJ.28(12):1803-1811.3. YingFang L., ZhiYoung L., BartlamM.,Zihe R. 2009.Structure-functionstudies of the influenza virus RNApolymerase PA subunit.SCLS.52(5):450-458.