My Research Work and ClusteringOutlineCentral Dogma of Molecular BiologyAmino Acids, the subunit of proteinsProtein Primary, Secondary, and Tertiary StructureProtein 3D StructureProtein Sequence MotifSlide 8Goal of the our groupSlide 10Experiment setup: HSSP matrix: 1b25HSSP matrix: 1b25Representation of SegmentSlide 14Clustering AlgorithmsK-means ClusteringSlide 17Slide 18Slide 19Slide 20Fuzzy C-means ClusteringSlide 22Slide 23Slide 24Slide 25Slide 26Slide 27Granular Computing ModelMotivationReduce Space-complexityReduce Time-complexityHSSP-BLOSUM62 MeasureSlide 33Future WorksPART3: protein information extraction by Decision TreePART4: Clustering with association rule and graph theoryPART4: Super rule generation by DB-ScanPART5: Protein local tertiary structure predictionMy Research Work and Clustering Dr. Bernard Chen Ph.D.University of Central ArkansasFall 2010OutlineIntroductionExperimental SetupClusteringFuture WorksCentral Dogma of Molecular BiologyAmino Acids, the subunit of proteinsProtein Primary, Secondary, and Tertiary StructureProtein 3D StructureProtein Sequence MotifAlthough there are 20 amino acids, the construction of protein primary structure is not randomly choose among those amino acidsSequence Motif: A relatively small number of functionally or structurally conserved sequence patterns that occurs repeatedly in a group of related proteins.Protein Sequence MotifThese biologically significant regions orresidues are usually:Enzyme catalytic siteProstethic group attachment sites (heme, pyridoxal-phosphate, biotin…)Amino acid involved in binding a metal ionCysteines involved in disulfide bondsRegions involved in binding a molecule (ATP/ADP, GDP/GTP, Ca, DNA…)Goal of the our groupThe main purpose is trying to obtain and extract protein sequence motifs which are universally conserved and across protein family boundaries. Discuss the relation between Protein Primary structure and Tertiary structureOutlineIntroductionExperimental SetupClusteringFuture WorksExperiment setup: HSSP matrix: 1b25HSSP matrix: 1b25Representation of SegmentSliding window size: 9Each window corresponds to a sequence segment, which is represented by a 9 × 20 matrix plus additional nine corresponding secondary structure information obtained from DSSP. More than 560,000 segments (413MB) are generated by this method. DSSP: Obtain 2nd Structure informationOutlineIntroductionExperimental SetupClusteringFuture WorksClustering AlgorithmsThere are two clustering algorithms we used in our approach:K-means ClusteringFuzzy C-means ClusteringK-means ClusteringK-means ClusteringK-means ClusteringK-means ClusteringK-means ClusteringFuzzy C-means ClusteringFuzzy C-means ClusteringFuzzy C-means ClusteringFuzzy C-means ClusteringFuzzy C-means ClusteringFuzzy C-means ClusteringFuzzy C-means ClusteringGranular Computing Model Original datasetFuzzy C-Means ClusteringInformation Granule 1Information Granule MK-means Clustering K-means Clustering Join InformationFinal Sequence Motifs Information......MotivationReduce Space-complexity Number of MembersNumber of ClustersData SizeGranule 0 136112 151 99.9MBGranule 1 68792 76 50.5MBGranule 2 86094 95 63.2MBGranule 3 65361 72 47.9MBGranule 4 63159 70 46.3MBGranule 5 120130 133 88.2MBGranule 6 128874 143 94.6MBGranule 7 4583 5 3.3MBGranule 8 43254 48 31.7MBGranule 9 5032 6 3.7MBTotal 721390 799 529MBOriginal dataset562745 800 413MBTable 1 summary of results obtained by FCMReduce Time-complexityWei’s method: 1285968 sec (15 days) * 6 = 7715568 sec (90 days) Granular Model: 154899 sec + 231720 sec * 6 = 1545219 sec (18 days) (FCM exe time) (2.7 Days)HSSP-BLOSUM62 MeasureOutlineIntroductionExperimental SetupClusteringFuture WorksPart1Bioinformatics Knowledge and Dataset CollectionPart2Discovering Protein Sequence Motifs Part3Motif Information ExtractionPart4Mining the Relations between Motifs and MotifsPart5Protein Local Tertiary Structure PredictionFutureWorksPART3: protein information extraction by Decision TreePART4: Clustering with association rule and graph theoryPART4: Super rule generation by DB-ScanApply DB scan to build up super-rules among all motifsPART5: Protein local tertiary structure prediction ByDecision TreeNaïve Bayesian Association rule algorithms and