clustering the temporal sequences of 3d protein structure
DESCRIPTION
Clustering the Temporal Sequences of 3D Protein Structure. Mayumi Kamada +* , Sachi Kimura, Mikito Toda ‡ , Masami Takata + , Kazuki Joe +. +: Graduate School of Humanities and Science, Information and Computer Sciences, Nara Women’s University - PowerPoint PPT PresentationTRANSCRIPT
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Clustering the Temporal Sequences of 3D Protein StructureMayumi Kamada+*, Sachi Kimura, Mikito Toda, Masami Takata+, Kazuki Joe++Graduate School of Humanities and Science, Information and Computer Sciences, Nara Womens UniversityDepartments of physics, Nara Womens University
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OutlineMotivationFlexibility DockingFeature Extraction using MotionAnalysis Conclusions and Future Work
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MotivationProtein in biological molecules DockingTransform oneself and Combine with other materials
Prediction of Docking Prediction of resultant functions
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Existing Docking SimulationPredicted structuresfrom dockingstructureAstructureBDocking simulationPDB*Rigid structures* Protein Data BankFluctuating in living cells Low prediction accuracyDocking simulationConsidering fluctuations
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Flexibility DockingPredicted structuresfrom dockingstructureAstructureBDocking simulationPDBFlexibility handling Considering fluctuation of proteins in living cellsExtraction of fluctuated structuresConsideration ofstructural fluctuation of proteins
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Flexibility HandlingFlexibility handlingMDFilteroutputfileRepresentativestructureFiltering Selection of representative structures from similar structuresMolecular dynamic simulation(MD) Simulation of motion of molecules in a polyatomic systemoutputfileoutputfileoutputfileoutputfileRepresentativestructure
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Filters using RMSDRMSD(Root Mean Square Deviation)Comparison of the similarity of two structures
Propose two filtering algorithms Maximum RMSD selection filter Below RMSD 1 deletion filterResult Useful for the heat fluctuation conditionRMSD Unification of topology information Lapse of informationFeature extraction focusing on Protein Motion not Structure
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Capture Protein Motion MDWavelet transformClusteringContinuous wavelet transform: Morlet wavelet Clustering algorithm:Affinity PropagationSelection of representative motionsFeature extractionThe frequency may change momentarily!
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Target Protein1TIBResidue length: 269MD simulationSoftware: AMBERSimulation run time: 2 nsec Result data files: 200Space coordinates of C atoms
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Singular Value DecompositionSVD(Singular value decomposition)
Definition:
Unitary matrix U: Left-singular vectorsSpatial motionUnitary matrix V: Right-singular vectorsFrequency fluctuationmatrix-size of A: 807199
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Singular Value DecompositionSVD(Singular value decomposition)
Definition:
Unitary matrix U: Left-singular vectorsSpatial motionUnitary matrix V: Right-singular vectorsFrequency fluctuationmatrix-size of A: 807199
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Verification of ReproducibilitySingular values and principal components
Left Singular Vectors(Spatial motion)Right Singular Vectors(Frequency fluctuation)
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ReproducibilityUsing the eight principal components, the motion expressed by 199 componentscan be reproduced !Almost adjusted !
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Examination (1) Each of singular values
(2)The first singular valueAccounted for about 30% overExpression of the original motion Possible by the six singular valuesThe first singular value is useful
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Clustering AnalysisFocus on the first principal componentDefinitionSimilarities and Preference
Clustering by using the above values
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Similarities (1)For left singular vectorsDifference of spatial directs Inner products
Similarity : C
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Similarities (2)For right singular vectorsDifference between distributions of spectrum Hellinger Distance
Similarity:
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Clustering MethodAffinity propagation(AP)Brendan J. Frey and Delbert Dueck Clustering by Passing Messages Between Data Points. Science 315, 972976.2007Obtain Exemplars: cluster centers
PreferenceLeft singular vectorsAverage of similaritiesRight singular vectorsminimum of similaritiesmaximum of similaritiesminimum
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Similarities between Left Singular Vectors
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Clusteringof Left Singular Vectors
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Similarities between Right Singular Vectors
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Clustering of Right Singular Vectors
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DiscussionsEach of motionsSpatial motionRepetition of several similar spatial motions in time variationFrequency fluctuationRepetition of similar frequency patterns in time variation Relationship Characteristic Frequency fluctuation Group transition on spatial motion
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Conclusions and Future WorkFlexibility dockingFlexibility handling: MD and FilterFeature extraction based motionWavelet analysisAnalysis of motions ClusteringFuture workCollective motionRelationship Perform the docking simulation
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Conclusions and Future WorkFlexibility dockingFlexibility handling: MD and FilterFeature extraction based motionWavelet analysisAnalysis of motions ClusteringFuture workCollective motionRelationship Perform the docking simulation