Abhishek seminar

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Abhishek seminar

  1. 1. PROJECT NAMEPROJECT NAMEQSAR ANALYSIS ANDQSAR ANALYSIS ANDVALIDATION STUDIES ONVALIDATION STUDIES ONAMINOQUINOLINE DERIVATIVESAMINOQUINOLINE DERIVATIVESAS MELANIN CONCENTRATINGAS MELANIN CONCENTRATINGHORMONE-1R INHIBITORSHORMONE-1R INHIBITORSBYBYM.ABHISHEKM.ABHISHEK
  2. 2. INTRODUCTION TO DRUGINTRODUCTION TO DRUGDESIGNINGDESIGNING DEFFINITIONDEFFINITION Designing of drug molecules basing upon theirDesigning of drug molecules basing upon theirbiological targets.biological targets. It is mainly of three typesIt is mainly of three types Structure based drugdesignStructure based drugdesign Denovo based drugdesignDenovo based drugdesign Analog based drugdesignAnalog based drugdesign Analog based drug design alsi called QSARAnalog based drug design alsi called QSARanalysis.analysis.
  3. 3. About QSAR STUDIESAbout QSAR STUDIESQSAR is qualitative structure activityQSAR is qualitative structure activityrelation shiprelation shipUses of qsar analysisUses of qsar analysisMainly useful for the determinationMainly useful for the determinationphysiochemical properties.physiochemical properties.Useful to predict the biological value of theUseful to predict the biological value of themolculesmolcules
  4. 4. CONTENTSCONTENTSINTRODUCTIONINTRODUCTIONMATERIALS AND METHODSMATERIALS AND METHODSRESULTS AND DISCUSSIONRESULTS AND DISCUSSIONCONCLUSIONCONCLUSIONREFERENCESREFERENCES
  5. 5. INTRODUCTIONINTRODUCTION ABOUT MCHABOUT MCH The underlying causes of obesity are poorly understood butThe underlying causes of obesity are poorly understood butprobably involve complex interactions between manyprobably involve complex interactions between manyneurotransmitter and neuropeptide systems involved in theneurotransmitter and neuropeptide systems involved in theregulation of food intake and energy balance. Three pieces ofregulation of food intake and energy balance. Three pieces ofevidence indicate that the neuropeptide melanin-concentratingevidence indicate that the neuropeptide melanin-concentratinghormone (MCH) is an important component of this system.hormone (MCH) is an important component of this system. Melanin-concentrating hormone (MCH) is a cyclic neuropeptideMelanin-concentrating hormone (MCH) is a cyclic neuropeptide(human/rat 19 aa) that regulates a variety of functions in mammalian(human/rat 19 aa) that regulates a variety of functions in mammalianbrain, in particular feeding behavior .brain, in particular feeding behavior . MCH is synthesized in mainly in the lateral hypothalamus and zonaMCH is synthesized in mainly in the lateral hypothalamus and zonaincerta. MCH stimulates feeding,incerta. MCH stimulates feeding, Recently, an orphan G-protein coupled receptor (SLC-1, GPR24)Recently, an orphan G-protein coupled receptor (SLC-1, GPR24)has been identified as the receptor of MCH. MCH receptor ishas been identified as the receptor of MCH. MCH receptor ispredicted to contain 7 transmembrane domains, a feature typical ofpredicted to contain 7 transmembrane domains, a feature typical ofG-protein coupled receptorsG-protein coupled receptors
  6. 6.  Recently, a novel second human MCH receptorRecently, a novel second human MCH receptor(MCH2R) has been cloned and characterized. MCH2R(MCH2R) has been cloned and characterized. MCH2Rgene encodes a 340 aa protein with 38% identity withgene encodes a 340 aa protein with 38% identity withMCH1RMCH1R MOLECULAR CHARACTERIZATIONMOLECULAR CHARACTERIZATION Orphan G-protein-coupled receptors (GPCRs) areOrphan G-protein-coupled receptors (GPCRs) arecloned proteins with structural characteristics common tocloned proteins with structural characteristics common tothe GPCRs but that bind unidentified ligands. Orphanthe GPCRs but that bind unidentified ligands. OrphanGPCRs have been used as targets to identify novelGPCRs have been used as targets to identify noveltransmitter moleculestransmitter molecules We demonstrate that nanomolar concentrationsWe demonstrate that nanomolar concentrationsof MCH strongly activate SLC-1-relatedof MCH strongly activate SLC-1-relatedpathways through G(alpha)i and/or G(alpha)qpathways through G(alpha)i and/or G(alpha)qproteinsproteins
  7. 7. STRUCTURE OF MCHSTRUCTURE OF MCH
  8. 8. FUNCTION OF MCHFUNCTION OF MCH Melanin-concentrating hormone (MCH) is a cyclic neuropeptide, whichMelanin-concentrating hormone (MCH) is a cyclic neuropeptide, whichcentrally regulates food intake and stress. MCH induces food intake incentrally regulates food intake and stress. MCH induces food intake inrodents and, more generally, acts as an anabolic signal in energyrodents and, more generally, acts as an anabolic signal in energyregulation.regulation. Two receptors for MCH in humans have very recently been characterised,Two receptors for MCH in humans have very recently been characterised,namely, MCH-R1 and MCH-R2. MCH-R1 has received considerablenamely, MCH-R1 and MCH-R2. MCH-R1 has received considerableattention, as potent and selective antagonists acting at that receptor displayattention, as potent and selective antagonists acting at that receptor displayanxiolytic, antidepressant and/or anorectic properties.anxiolytic, antidepressant and/or anorectic properties. ACTIVE SITE AND INACTIVE SITE OF MCHACTIVE SITE AND INACTIVE SITE OF MCH Human melanin-concentrating hormone (hMCH) and many of its analoguesHuman melanin-concentrating hormone (hMCH) and many of its analoguesare potent but nonspecific ligands for human melanin-concentratingare potent but nonspecific ligands for human melanin-concentratinghormone receptors 1 and 2 (hMCH-1R and hMCH-2R). To differentiatehormone receptors 1 and 2 (hMCH-1R and hMCH-2R). To differentiatebetween the physiological functions of these receptors, selectivebetween the physiological functions of these receptors, selectiveantagonists are needed. In this study, analogues of Ac-Arg(6)-cyclo(S-S)antagonists are needed. In this study, analogues of Ac-Arg(6)-cyclo(S-S)(Cys(7)-Met(8)-Leu(9)-Gly(10)-Arg(11)-Val(12)-Tyr(13)-Arg(14)-Pro(15)-(Cys(7)-Met(8)-Leu(9)-Gly(10)-Arg(11)-Val(12)-Tyr(13)-Arg(14)-Pro(15)-Cys(16))-NH(2), a high affinity but nonselective agonist at hMCH-1R andCys(16))-NH(2), a high affinity but nonselective agonist at hMCH-1R andhMCH-2R, were prepared and tested in binding and functional assays onhMCH-2R, were prepared and tested in binding and functional assays oncells expressing these receptorscells expressing these receptors
  9. 9. MATERIALS&METHODSMATERIALS&METHODS Tsar (Tools for Structure Activity Relationship) is a program used toTsar (Tools for Structure Activity Relationship) is a program used toinvestigates quantitative structure activity relationships (QSAR).investigates quantitative structure activity relationships (QSAR).Tsar is an integrated analysis package for interactive investigationTsar is an integrated analysis package for interactive investigationof Quantitative Structure-Activity Relationship (QSARs )of Quantitative Structure-Activity Relationship (QSARs ) The major functional areas of Tsar and their significance in theThe major functional areas of Tsar and their significance in theinvestigation of quantitative structure-activity relationship (QSARs)investigation of quantitative structure-activity relationship (QSARs)and is intended to provide all the function require to carry out anyand is intended to provide all the function require to carry out anyQSAR investigation,QSAR investigation, TSAR uses an integrated approach to provide all componentsTSAR uses an integrated approach to provide all componentstogether.together. It uses a chemically aware spreadsheet to store and manipulateIt uses a chemically aware spreadsheet to store and manipulatedifferent type of data, including:different type of data, including: Molecular descriptionMolecular description 3D structures3D structures Activity dataActivity data Computed dataComputed data
  10. 10. SOFTWARE USED INSOFTWARE USED INANALYSIS:ANALYSIS: The software are: TSAR software and ISIS/DRAW softwareThe software are: TSAR software and ISIS/DRAW software TSAR software: TSAR software of version 3.3 was used to study theTSAR software: TSAR software of version 3.3 was used to study theQSAR derivatives. It has TSAR project window, to which molecularQSAR derivatives. It has TSAR project window, to which moleculardata is entered through import/export file system. Multipledata is entered through import/export file system. Multipleregression analysis is done by taking physiochemical propertiesAregression analysis is done by taking physiochemical propertiesAdescription of the basic operation of Tsar and fundamental aspectsdescription of the basic operation of Tsar and fundamental aspectsof the application with which you need to familiar, including the Tsarof the application with which you need to familiar, including the Tsarinterface, how to work with projects, data and views. When you startinterface, how to work with projects, data and views. When you startwith Tsar graphical interface, the first screen that is displayed is thewith Tsar graphical interface, the first screen that is displayed is themain Tsar window and biological activity. Then a graph was plottedmain Tsar window and biological activity. Then a graph was plottedin between actual values and predicted values.in between actual values and predicted values.CORINACORINA: The 3D structure of a molecule is closely related to a: The 3D structure of a molecule is closely related to alarge variety of chemical, physical and biological propertlarge variety of chemical, physical and biological propert This introduction to CORINA contains the following topics:This introduction to CORINA contains the following topics:Automatic generation of high quality 3D molecular models providesAutomatic generation of high quality 3D molecular models providesan introduction to the use of predicting a 3D structurean introduction to the use of predicting a 3D structure
  11. 11.  ISIS/DRAWISIS/DRAW: This software has several tools,: This software has several tools,which are used to draw the chemical structure ofwhich are used to draw the chemical structure ofQSAR derivatives. About 88 molecules wereQSAR derivatives. About 88 molecules weredrawn using ISIS Draw 2.3 software and thedrawn using ISIS Draw 2.3 software and thedescriptors were calculated using Tsar 3.3descriptors were calculated using Tsar 3.3software.software. QSAR regression analysis for this set ofQSAR regression analysis for this set ofmolecules was carried out by considering allmolecules was carried out by considering allmolecules as complete set and removing outliermolecules as complete set and removing outliercomponent from complete set to generatecomponent from complete set to generatetraining set and test set respectively.training set and test set respectively.
  12. 12. STRUCTURE OF SOMESTRUCTURE OF SOMEMOLECULESMOLECULESNHOFFFN NNHOFFFN NNHOFFFN NNHOFFFN NNHOFFFN NNHOFFFN NNHOFFFN NCompound s_11_9 Compound s_11_10Compound s_11_11 Compound s_11_12Compound s_11_13 Compound s_11_14Compound s_11_15
  13. 13. RESULTS ANDRESULTS ANDDISCUSSIONDISCUSSION MOLECLE ANALYSISMOLECLE ANALYSIS:: To cover the whole activity range, the data set was randomly divided into training setTo cover the whole activity range, the data set was randomly divided into training setand test set .QSAR model was constructed based on training set and then validatedand test set .QSAR model was constructed based on training set and then validatedinternally using Leave One Out (LOO) technique and extremely by predicting theinternally using Leave One Out (LOO) technique and extremely by predicting theactivity of test set. The relationship between dependent variable (-log 1/C) andactivity of test set. The relationship between dependent variable (-log 1/C) andindependent variable (physiochemical properties) was established by using linearindependent variable (physiochemical properties) was established by using linearmultiple regression analysis using TSAR 3.3 software. Then significant descriptorsmultiple regression analysis using TSAR 3.3 software. Then significant descriptorsare chosen based on the statistical data analysis.are chosen based on the statistical data analysis. COMPLETE SET:COMPLETE SET: 70 molecules are appended to multiple regression analysis.70 molecules are appended to multiple regression analysis. EquationsEquationsOriginal Data : Y = 0.055812515*X6 - 2.7095358*X35 - 0.75705647*X39 -Original Data : Y = 0.055812515*X6 - 2.7095358*X35 - 0.75705647*X39 -1.8342798*X44 - 2.0094008Standardized Data : Y = 0.81183285*S6 -1.8342798*X44 - 2.0094008Standardized Data : Y = 0.81183285*S6 -0.35177225*S35 - 0.45452115*S39 - 0.61900699*S44 - 1.21242860.35177225*S35 - 0.45452115*S39 - 0.61900699*S44 - 1.2124286CalculationCalculationInformationInformation70 rows included in model0 rows excluded because of missing data4970 rows included in model0 rows excluded because of missing data49independent variables considered0 independent variables excluded because ofindependent variables considered0 independent variables excluded because ofmissing data0 independent variables in initial model4 variables included in final modelmissing data0 independent variables in initial model4 variables included in final modelusing F-test steppingStandardized by mean/SDCross validated leaving out one rowusing F-test steppingStandardized by mean/SDCross validated leaving out one rowrandomly over 2 random trialsCorrelation limit of 0.9 applied4 steps to generate finalrandomly over 2 random trialsCorrelation limit of 0.9 applied4 steps to generate finalmodelF to enter = 4, F to leave = 4modelF to enter = 4, F to leave = 4Variance AnalysisVariance AnalysisRegression: 4 degrees ofRegression: 4 degrees offreedom, sum of squares = 63.947Residual: 65 degrees of freedom, sum of squaresfreedom, sum of squares = 63.947Residual: 65 degrees of freedom, sum of squares= 13.901Total: 69 degrees of freedom, sum of squares = 77.847= 13.901Total: 69 degrees of freedom, sum of squares = 77.847Statistical TestsStatistical Tests
  14. 14.  QSAR EQUATION:QSAR EQUATION: log (1/IC50) =log (1/IC50) = + 0.055205099* Inertia Moment 1+ 0.055205099* Inertia Moment 1LengthLength - 2.6556225* Balaban Topological index- 2.6556225* Balaban Topological index- 0.7120384* ADME H-bond Acceptors- 0.7120384* ADME H-bond Acceptors - 1.8028219* VAMP LUMO- 1.8028219* VAMP LUMO - 2.0724609- 2.0724609 r = 0.890, r2 = 0.793, cvr2 = 0.700, F = 50.7138, n = 58,r = 0.890, r2 = 0.793, cvr2 = 0.700, F = 50.7138, n = 58,PRESS = 19.4898, Residual sum = 13.4604.PRESS = 19.4898, Residual sum = 13.4604. Once the multiple regression analysis is performed onOnce the multiple regression analysis is performed onthe complete set and a statistically significant result isthe complete set and a statistically significant result isobtained, the next step is to perform multiple regressionobtained, the next step is to perform multiple regressionanalysis on training set and test set data.analysis on training set and test set data.
  15. 15.  TEST SET:TEST SET: The test set consists of 12 compounds that are separated from theThe test set consists of 12 compounds that are separated from thecomplete set of 58 compounds. The test set compounds arecomplete set of 58 compounds. The test set compounds areselected based on the hierarchical clustering data so that the totalselected based on the hierarchical clustering data so that the totalbiological activity range of the complete set is covered.biological activity range of the complete set is covered. The regression equation obtained from the training set is appendedThe regression equation obtained from the training set is appendedto the test set. Thus the activity of the test set is predicted. Theto the test set. Thus the activity of the test set is predicted. Thepredictive ability of the model is estimated from the graph plottedpredictive ability of the model is estimated from the graph plottedfrom these values. The predicted values and their correspondingfrom these values. The predicted values and their correspondingactual value is given below in a table:actual value is given below in a table: Molecule No.Actual ValuePredicted ValueMolecule No.Actual ValuePredicted Value 1-2.38-2.1105-1-2.38-2.1105-1.94-1.9768-1.25-1.12216-2.9-2.54118-1.08-0.95219-0.7-0.51022-1.94-1.9768-1.25-1.12216-2.9-2.54118-1.08-0.95219-0.7-0.51022-0.48-0.33533-1.65-1.53944-3.6-3.19850-0.9-0.6930.48-0.33533-1.65-1.53944-3.6-3.19850-0.9-0.693
  16. 16. CONCLUSIONCONCLUSION QSAR analysis was performed on 70 aminoquinoline MCH 1RQSAR analysis was performed on 70 aminoquinoline MCH 1Rmolecules.Training set (58 molecules) , test set (12 molecules) and outliersmolecules.Training set (58 molecules) , test set (12 molecules) and outliers(18 molecule ) was generated from the complete set of 70 molecules, each(18 molecule ) was generated from the complete set of 70 molecules, eachcontaining a set of active ,moderately active and inactive molecules. Acontaining a set of active ,moderately active and inactive molecules. Aregression equation was generated using multiple regression analysis onregression equation was generated using multiple regression analysis ontraining set. This regression equation was applied on the test set to predicttraining set. This regression equation was applied on the test set to predictbiological activity of test set molecules. The predicted activity was obtainedbiological activity of test set molecules. The predicted activity was obtainedthrough the regression equation. The QSAR equation generated bythrough the regression equation. The QSAR equation generated byconsidering training set molecules resulted identifying Inertia Moment 1considering training set molecules resulted identifying Inertia Moment 1Length , Balaban Topological Index , ADME H-bond acceptors , VAMPLength , Balaban Topological Index , ADME H-bond acceptors , VAMPLUMO .LUMO . Eq. 1 accounts for the significant correlation of the descriptors withEq. 1 accounts for the significant correlation of the descriptors withbiological activity and displayed good internal predictivity as shown by q2biological activity and displayed good internal predictivity as shown by q2value of 0.700 and was able to explain 79.3% variance of inhibitory activitiesvalue of 0.700 and was able to explain 79.3% variance of inhibitory activitiesof MCH-1R inhibitors. The predictive ability of QSAR model illustrated theof MCH-1R inhibitors. The predictive ability of QSAR model illustrated theaccuracy and robustness of QSAR model on test set molecules. Therefore,accuracy and robustness of QSAR model on test set molecules. Therefore,considering the contributions of these descriptors on aminoquinolineconsidering the contributions of these descriptors on aminoquinolinederivatives would help in designing novel compounds that enhance MCH-1Rderivatives would help in designing novel compounds that enhance MCH-1Rinhibitioninhibition
  17. 17. REFERENCESREFERENCES REFERENCESREFERENCES Chambers J, Ames RS, Bergsma D, Muir A, Fitzgerald LR, HervieuChambers J, Ames RS, Bergsma D, Muir A, Fitzgerald LR, HervieuG, Dytko GM, Foley JJ, Martin J, Liu WS, Park J, Ellis C, GangulyG, Dytko GM, Foley JJ, Martin J, Liu WS, Park J, Ellis C, GangulysS, Konchar S, Cluderay J, Leslie R, Wilson S, Sarau HM. Melanin-sS, Konchar S, Cluderay J, Leslie R, Wilson S, Sarau HM. Melanin-concentrating hormone is the cognate ligand Nature. 1999 Julconcentrating hormone is the cognate ligand Nature. 1999 Jul15;400(6741):261-515;400(6741):261-5 http://www.4adi.com/flr/mchrflr.htmlhttp://www.4adi.com/flr/mchrflr.html Saito Y, Nothacker HP, Wang Z, Lin SH, Leslie F, Civelli O.Saito Y, Nothacker HP, Wang Z, Lin SH, Leslie F, Civelli O.Molecular characterization of the melanin-concentrating-hormoneMolecular characterization of the melanin-concentrating-hormonereceptor. Nature. 1999 Jul 15;400(6741):265-9.receptor. Nature. 1999 Jul 15;400(6741):265-9. Kawauchi H, Kawazoe I, Tsubokawa M, Kishida M, Baker BI.Kawauchi H, Kawazoe I, Tsubokawa M, Kishida M, Baker BI.Characterization of melanin-concentrating hormone in chum salmonCharacterization of melanin-concentrating hormone in chum salmonpituitaries. Nature. 1983 Sep 22-28;305(5932):321-3.pituitaries. Nature. 1983 Sep 22-28;305(5932):321-3. Guillaume HervieuGuillaume Hervieu Melanin-concentrating hormone functions in theMelanin-concentrating hormone functions in thenervous system: food intake and stressnervous system: food intake and stresshttp://www.expertopin.com/doi/abs/10.1517/14728222.7.4.495http://www.expertopin.com/doi/abs/10.1517/14728222.7.4.495
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