Session ii g2 overview metabolic network modeling mcc


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Session ii g2 overview metabolic network modeling mcc

  1. 1. Case study (ref)PSI meta-server usedComputer drive drug design process
  2. 2. Step Input Tools Output / resultSelection of ligand compoundsequencePubChem structurestructure Open Babel required file formatADMET studies sequence Pre-ADMET Provide drug-likeliness, ADMEprofile and toxicity analysis for theligand.Receptorcharacterizationsequence GOR IV Text data resultSelect Template sequence BLAST Identity, similarity, expectationvalue & alignment scoresHomology modeling protein sequences Modeller pdb fileVisualizing pdb file PyMol visualized modelModel validation pdb filePROCHECK Parameters(covalent bonddistances and angles,stereochemicalvalidation and atomnomenclature)ERRAT The overall quality factor of non-bonded interactions betweendifferent atoms typesDaliLite mean square deviation (RMSD)between the set of targets andtemplate protein to checkdeviation of modelled proteinfrom the template proteinstructure.Docking studies PDB, PDBQ, PDBQT, SYBYLmol2 or PQR formatAutoDock 4.2 PDBQT format and include specialkeywords establishing thetorsional flexibility
  3. 3. Data Preparation and AnalysisThe world wide Protein Data Bank: The single archive of experimentalmarcomolecular structural data. [RCSB PDB] (USA); [PDBe] (Europe); [PDBj](Japan)CATH: A manually curated hierarchical domain classification of protein structuresin the Protein Data Bank.UniProt: Protein Knowledgebase. A comprehensive, high-quality and freelyaccessible database of protein sequence and functional information.RefSeq: NCBI Reference Sequence. A collection of curated, non-redundantgenomic DNA, transcript (RNA), and protein sequences produced by NCBI.SBKB: Structural Biology Knowledgebase. A portal to protein structures,sequences, functions and methods.
  4. 4. Structure ModellingTemplate Search/Fold RecognitionBLAST/PSI-BLAST: Local alignment searchtools.HHpred: Server for homology detectionand structure prediction by HMM-HMMcomparison.Homology ModelingNew predictionyesno
  5. 5. HHpred: Server for homology detection and structure prediction byHMM-HMM comparison.I-Tasser: I-TASSER is a server for protein structure and functionpredictions. 3D models are built based on multiple-threadingalignments by LOMETS and iterative TASSER assembly simulations.M4T: Comparative Modelling using a combination of multiple templatesand iterative optimization of alternative alignments.ModWeb: A web server for automated comparative modeling that relieson PSI-BLAST, IMPALA and MODELLER.SWISS-MODEL: Fully automated protein structure homology-modelingserver accessible via the ExPASy web server, or from the programDeepView (Swiss Pdb-Viewer).Homology ModelingStructure Modeling
  6. 6. Swiss-modelidentificationof structuraltemplatealignment of targetsequence &template structuremodelbuildingmodel qualityevaluationInterPro Domain Scan:InterPro, Pfam, TIGRFAMs,PROSITE, SUPERFAMILY,ProDom,PRINTS, SMART, PROSITEPsiPred Secondary StructurePrediction:PSIPREDDISOPRED DisorderPrediction:DISOPREDMEMSAT:MEMSATusing BLAST query against theExPDB template library extractedfrom PDB.When no suitable templates areidentified, using Iterative ProfileBlast. Which is the template libraryis searched with PSI-BLAST (Altschulet al.) using an iterativelygenerated sequence profile basedon NR (Wheeler et al.).HHSearch: To detect distantlyrelated template structures (Södinget al.)Display of template identificationresultsDeepView: Structure & Model AssessmentTools:ANOLEAQMEAN, The global QMEAN4 scoring function( Benkert et al. 2008), The global QMEAN6scoring function (Benkert et al. 2008), Thelocal version of the QMEAN scoring function(Benkert et al. 2009),DFIREGROMOSWhat CheckPROCHECKPROMOTIFDSSPQMEAN4 global scoresLocal Model Quality Estimation: Anolea / QMEAN / Gromos:AlignmentModelling LogTemplate Selection LogQuaternary Structure Modeling LogLigand Modeling LogStructure Modelling
  7. 7. The "automated mode" is suited for cases where the target-templatesimilarity is sufficiently high to allow for fully automated modeling.This submission requires only the amino acid sequence or the UniProtaccession code of the target protein as input data.Depending on the planned model application, it can be necessary toselect a different structural template than the one ranked first in theautomated process. Please make sure that this file contains only a singleprotein chain, and does not contain chemically modified amino acids,hereto atoms, ligands, etc.Automated Mode
  8. 8. If the three-dimensional structure is known for at least one of the members, thisalignment can be used as starting point for comparative modelling using the"alignment mode".The "alignment mode" allows the user to test several alternative alignments andevaluate the quality of the resulting models in order to achieve an optimal result.1. Prepare a multiple sequence alignment.2. Submit your alignment to the Workspace Alignment Mode.3. Select Target and Template.4. Check Alignment and Submit.The server pipeline will build the model purely based on this alignment. During themodeling process, implemented as rigid fragment assembly in the SWISS-MODEL(Schwede et al.) pipeline, the modeling engine might introduce minor heuristicmodifications to the placement of insertions and deletions.Alignment Mode
  9. 9. In difficult modeling situations, where the correct alignment betweentarget and template cannot be clearly determined by sequence basedmethods, visual inspection and manual manipulation of the alignmentcan significantly help improving the quality of the resulting model.Project files contain the superposed template structures, and thealignment between the target and template. Project files can begenerated inside the program DeepView (Swiss-PdbViewer Guex et al.),by the workspace template selection tools, and are also the defaultoutput format of the modeling pipeline. This allows analyzing anditeratively improving the models generated by the "Automated mode"and "Alignment mode" modeling approaches.Project Mode
  10. 10. M4TOutput:pdb filepir fileana fileEnergy profileStructure Modelling
  11. 11. I-TASSERtries retrieve templateproteins of similar foldsfrom the PDB libraryby LOMETSStructure assemblycontinuousfragmentsby replica-exchange MonteCarlo simulations with thethreading unaligned regions(mainly loops) built by abinitio modelinglow free-energy states areidentified by SPICKERStructure Re-assemblyRetrieve from clusterLOMETS & TM-alignlowestenergystructuresare full-atomic models(Remo H-Bondoptimization)function predictionsTM-alignsearchTM-scoreOutputs:Predicted Secondary StructurePredicted Solvent Accessibilitypdb fileTop 10 templatesProteins with highly similar structure in PDBFunction PredictionPredicted GO termsPredicted Binding SiteStructure Modellingsubmit an aminoacid sequence
  12. 12. HHpredSelect input formatMSA Generation MethodMore OptionsMax. MSA GenerationiterationsScore secondarystructureRealign with MACalgorithmAlignment modeSelect HMM databasesEntering a single query sequenceEntering a multiple alignmentProteomesPdb70,Scop70,CDD,InterPro,PfamA,SMART,PANTHER, TIGRFAMs,PIRSF, SUPERFAMILY,CATH/Gene3D,COG/KOG, PfamBHBlits : Download pdf filePSI-Blast : View ArticleE-value threshold for MSA GenerationMin. coverage of MSA hitsMin. sequence identity of MSA hits withqueryMAC realignment threshold (0.0:global,>=0.1:local)Compositional bias correctionShow sequences per HMMWidth of alignmentsMin. probability in hit listMax. number of hits in hit listOutput:pdb fileStructure Modelling
  13. 13. ModelerSearching forstructures related toTvLDHSelecting a templateModel evaluation Model buildingAligning TvLDF withthe templatetarget TvLDH fileStructure Modeling
  14. 14. New predictionWhen no suitable template structure can be identified, denovo (a.k.a. ab initio) structure prediction methods can beused to generate three-dimensional protein models withoutrelying on a homologus template structure:Robetta: Full-chain protein structure prediction server basedon the Rosetta method.Rosetta: De novo protein structure prediction software.Structure Modeling
  15. 15. Hybrid techniquesThe goal of hybrid techniques is to contribute to a comprehensivestructural characterization of biomolecules ranging in size andcomplexity from small peptides to large macromolecularassemblies. Detailed structural characterization of assemblies isgenerally impossible by any single existing experimental orcomputational method. This barrier can be overcome by hybridapproaches that integrate data from diverse biochemical andbiophysical experiments:CS-ROSETTA: System for chemical shifts based protein structureprediction using ROSETTA.IMP: software for a comprehensive structural characterization ofbiomolecules.Structure Modelling
  16. 16. Confidence EstimationValidation and Quality estimationExample: Verify3D
  17. 17. Tools Input OutputVerify 3D pdb 3D-1D average score, raw data, raw average data,Whatcheck pdb Detial text report, TeX fileProve pdb An pdf file with Z–score and analysis of residues, and antext fileErrat pdb An pdf file with overall quality factor, error value in eachresiduePROCHECK pdb comprise a number of plots, in PostScript formatMolProbity pdb can view in pdf or KiNG, can choose lots of outputProSA pdb Z-Score, knowledge based energy, sequence positionConfidence Estimation
  18. 18. ApplicationStructureVisualization &AnalysisPyMol: A Python based open-source viewer forvisualization of macromolecular structures.AutoDock: A suite ofautomated docking tools.Molecular InteractionsMolecular Motions DynDom: Protein Domain Motion Gallery of Molecular MovementsDatabase.