Hydrophobic Residue Patterning in β-Strands and Implications for β-Sheet Nucleation Brent Wathen...

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Transcript of Hydrophobic Residue Patterning in β-Strands and Implications for β-Sheet Nucleation Brent Wathen...

  • Hydrophobic ResiduePatterning in -Strands and Implications for -SheetNucleationBrent WathenDept. of BiochemistryQueens University

  • OutlinePart I: IntroductionProteinsProtein Folding

    Part II: Protein Structure PredictionGoals, ChallengesTechniquesState of the Art

    Part III: Residue Patterning on -Strands-Sheet NucleationHydrophobic/Hydrophilic Patterning

  • OutlinePart I: IntroductionProteinsProtein Folding

    Part II: Protein Structure PredictionGoals, ChallengesTechniquesState of the Art

    Part III: Residue Patterning on -Strands-Sheet NucleationHydrophobic/Hydrophilic Patterning

  • Proteins Some BasicsWhat Is a Protein?

    Part I: Introduction

  • Proteins Some BasicsWhat Is a Protein?Linear Sequence of Amino Acids...

    Part I: Introduction

  • Proteins Some BasicsWhat Is a Protein?Linear Sequence of Amino Acids...

    What is an Amino Acid?

    Part I: Introduction

  • Proteins Some BasicsWhat Is a Protein?Linear Sequence of Amino Acids...

    What is an Amino Acid?

    Part I: Introduction

  • Proteins Some BasicsHow many types of Amino Acids?Part I: Introduction

  • Proteins Some BasicsHow many types of Amino Acids?20 Naturally Occurring Amino AcidsDiffer only in SIDE CHAINS

    IsoleucineArginine TyrosinePart I: Introduction

  • Proteins Some BasicsAmino Acids connect via PEPTIDE BONDPart I: Introduction

  • Proteins Some BasicsBackbone can swivel:DIHEDRAL ANGLES

    2 per Amino AcidProteins can be 100s of Amino Acids in length!Lots of freedom of movementPart I: Introduction

  • Protein FunctionsWhat do proteins do?

    Part I: Introduction

  • Protein FunctionsWhat do proteins do?EnzymesCellular SignalingAntibodiesPart I: Introduction

  • Protein FunctionsWhat do proteins do?EnzymesCellular SignalingAntibodiesWHAT DONT THEY DO!

    Part I: Introduction

  • Protein FunctionsWhat do proteins do?EnzymesCellular SignalingAntibodiesWHAT DONT THEY DO!

    Comes from Greek Work Proteios PRIMARYFundamental to virtually all cellular processes

    Part I: Introduction

  • Protein FunctionsHow do proteins do so much?Part I: Introduction

  • Protein FunctionsHow do proteins do so much?Proteins FOLD spontaneouslyAssume a characteristic 3D SHAPEShape depends on particular Amino Acid SequenceShape gives SPECIFIC functionPart I: Introduction

  • Protein StructureSTRUCTURE FUNCTION relationshipDetermining structure is often critical in understanding what a protein does2 main techniquesX-ray crystallographyNMR0.5 RMSD accuracyBoth are very challengingMonths to years of workMany proteins dont yield to these methodsPart I: Introduction

  • Protein StructureLevels of organizationPrimary SequenceSecondary Structure (Modular building blocks)-helices-sheetsTertiary StructureQuartenary StructureHydrophobic/Hydrophilic OrganizationHydrophobics ON INSIDEHydrophobic CoresPart I: Introduction

  • Protein StructurePart I: Introduction

  • Protein StructurePart I: Introduction

  • Protein FoldingWhat we DO know...Protein folding is FAST!!Typically a couple of secondsFolding is CONSISTENT!!Involves weak forces Non-CovalentHydrogen Bonding, van der Waals, Salt BridgesMostly, 2-STATE systemsVERY FEW INTERMEDIATESMakes it hard to study BLACK BOXPart I: Introduction

  • Protein FoldingWhat we DONT know...Mechanism...?Forces...?Relative contributions?Hydrophobic Force thought to be criticalPart I: Introduction

  • Intro SummaryProteins are central to all living thingsCritical to all biological studiesFolding process is largely unknownSequence Structure MappingStructure Function relationshipDetermining Protein Structure Experimentally is HARD WORKPart I: Introduction

  • OutlinePart I: IntroductionProteinsProtein Folding

    Part II: Protein Structure PredictionGoals, ChallengesTechniquesState of the Art

    Part III: Residue Patterning on -Strands-Sheet NucleationHydrophobic/Hydrophilic Patterning

  • The Prediction Problem

    Can we predict the final 3D protein structure knowing only its amino acid sequence?

    Part II: Structure Prediction

  • The Prediction Problem

    Can we predict the final 3D protein structure knowing only its amino acid sequence?

    Studied for 4 DecadesHoly Grail in Biological SciencesPrimary Motivation for BioinformaticsBased on this 1-to-1 Mapping of Sequence to StructureStill very much an OPEN PROBLEMPart II: Structure Prediction

  • PSP: GoalsAccurate 3D structures. But not there yet.Good guessesWorking models for researchersUnderstand the FOLDING PROCESSGet into the Black BoxOnly hope for some proteins25% wont crystallize, too big for NMRBest hope for novel protein engineeringDrug design, etc.Part II: Structure Prediction

  • PSP: Major HurdlesEnergeticsWe dont know all the forces involved in detailToo computationally expensive BY FAR!

    Conformational search impossibly large100 a.a. protein, 2 moving dihedrals, 2 possible positions for each diheral: 2200 conformations!Levinthals ParadoxLonger than time of universe to searchProteins fold in a couple of seconds??

    Multiple-minima problem

    Part II: Structure Prediction

  • Tertiary Structure PredictionMajor TechniquesTemplate ModelingHomology ModelingThreading

    Template-Free Modelingab initio MethodsPhysics-BasedKnowledge-BasedPart II: Structure Prediction

  • Template ModelingHomology ModelingWorks with HOMOLOGS~ 50% of new sequences have HOMOLOGSBLAST or PSI-BLAST search to find good modelsRefine:Molecular DynamicsEnergy Minimization

    Part II: Structure Prediction

  • Template-Free ModelingModeling based primarily from sequenceMay also use: Secondary Structure Prediction, analysis of residue contacts in PDB, etc.Advantages:Can give insights into FOLDING MECHANISMSAdaptable: Prions, Membrane, Natively UnfoldedDoesnt require homologsOnly way to model NEW FOLDSUseful for de novo protein designDisadvantages: HARD!Part II: Structure Prediction

  • Template-Free ModelingPhysics-BasedUse ONLY the PRIMARY SEQUENCETry to model ALL FORCESEXTREMELY EXPENSIVE computationally

    Knowledge-BasedInclude other knowledge: SSP, PDB AnalysisStatistical Energy PotentialsNot so interested in folding processHot area of researchPart II: Structure Prediction

  • Template-Free ModelingAll methods SIMPLIFY problemReduced Atomic RepresentationsC-s only; C- + C-; etc.Simplify Force FieldsOnly van der Waals; only 2-body interactionsReduced Conformational SearchesLattice ModelsDihedral Angle RestrictionsPart II: Structure Prediction

  • Template-Free ModelingBasic Approach:

    1. Begin with an unfolded conformation2. Make small conformational change3. Measure energy of new conformationAccept based on heuristic: SA, MC, etc.4. Repeat until ending criteria reached

    Underlying Assumption:Correct Conformation has LOWEST ENERGY

    Part II: Structure Prediction

  • Diverse EffortsData MiningPattern ClassificationNeural Networks, HMMs, Nearest Neighbour, etc.Packing AlgorithmsSearch OptimizationTraveling Salesman ProblemContact Maps, Contact OrderConstraint Logic, etc.

    Combinations of the above!Part II: Structure Prediction

  • ROSETTAPioneered by Baker Group (U. of Washington)Fragment Based MethodGuiding Assumption:Fragment Conformations in PDB approximate their structural preferencesPre-build fragment libraryAlleviates need to do local energy calculationsLowest energy conformations should already be in libraryPart II: Structure Prediction

  • ROSETTAPre-build fragment library3-mers and 9-mers200 structural possibilities for eachBuild conformations from the libraryRandomly assign 3-mers, 9-mers along chainDuring conformational search, reassign a 3-mer or a 9-mer to a new conformation at randomScore using energy functionAdaptive: Coarse grain at first, detailed at endAccept changes based on Monte Carlo methodPart II: Structure Prediction

  • Diverse EffortsData MiningPattern ClassificationNeural Networks, HMMs, Nearest Neighbour, etc.Packing AlgorithmsSearch OptimizationTraveling Salesman ProblemContact Maps, Contact OrderConstraint Logic, etc.

    Combinations of the above!Part II: Structure Prediction

  • State of the ArtCASP CompetitionCritical Assessment of Structure PredictionBlind Competition Every 2 yearsCASP6 in 2004 - CASP7 just completed~75 proteins whose structures have not been published as yetEasy homologs examplesDistant homologs availableDe novo structures: no homologs known

    Part II: Structure Prediction

  • State of the ArtTemplate ModelingCASP6 Target 266 (green), and best model (blue) Moult, J. (2005) Cur. Opin. Struct. Bio. 15:285-289Part II: Structure Prediction

  • State of the ArtTemplate ModelingAlignment still not easy, and often requires multiple templatesAccurate core models (within 2-3 RMSD)Still not good at modeling regions missing from templateSide-chain modeling not too goodMolecular dynamics not able to improve models as hoped

    Part II: Structure Prediction

  • State of the ArtTemplate-Free ModelingCASP6 target 201, and best model.Vincent, J.J. et. al (2005) Proteins 7:67-83.Part II: Structure Prediction

  • State of the ArtTemplate-Free ModelingCASP6 target 241, and 3 best models.Vincent, J.J. et. al (2005) Proteins 7:67-83.Part II: Structure Prediction

  • State of the ArtHow Good are Current Techniques?CASP6 Summary:

    The disappointing results for [hard new fold] targets suggest that the prediction community as a whole has learned to copy well but has not really learned how proteins fold.Vin