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Bioinformatics Methods

by: Nathanael Schowalter

Bioinformatics Methods BINF 630

Nathanael Schowalter
GPA 3.64

Iosif Vaisman

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Iosif Vaisman
Class Notes
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This 7 page Class Notes was uploaded by Nathanael Schowalter on Monday September 28, 2015. The Class Notes belongs to BINF 630 at George Mason University taught by Iosif Vaisman in Fall. Since its upload, it has received 66 views. For similar materials see /class/215256/binf-630-george-mason-university in BioInformatics at George Mason University.


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Date Created: 09/28/15
Introduction to Bioinformatics Iosil39 Vaisman Email ivaismanEgmuedu Knowledge Knowledge is apattern that exceeds certain threshold of interestingness Factors that contribute to interestingness coverage con dence statistical signi cance simplici unexpectedness acti onability Protein representation erarnbin Protein Modeling Methods Ab ini u methods solution of a protein folding problem Search in confonnational space Knowledgebased methods Finding patterns m knuwn structures Denvmg ruies usually in the farm of my Applying themles protein representation erarnbin my Neighbor identi cation in proteins 1o neighb or 7 m 3 o o o 39 o o 0 o I 39 I o Neighbor identi cation in proteins VoronoiDelaunay Tessellation in 2D Delaunay lessellation of Crambin 34 i Neighbor identi cation in proteins VoronoiD elaunay Tessellation in 2D Deiinniyrnnpnx annnnninim Vommi39l39essdlj39 dzun y 1155 an Neighbor identi cation in proteins VomnoiD elaunay Tessellation in 2D a 39 lt 396 f t Vommi Taselhlinn Dehmuy Tasdhlinn Delaunay tessellation of Crambin Dealunay Sirnplices Classi cation SEQUENCE SEQUENCE SEQUENCE 2 o a m l STRUCTURE STRUCTURE SequenceStructure structure sequence compatibility compatibili fold recognition inverse folding Homology Modeling Identi cation or structurally conserved regions using multiple alignment Backbone construction based on SCR chain restoration 1ml39olamel39 or Structure veri cation and evaluation Structure rermement energy minimization SEQUENCE Loop construction KB or conformational Search We Fold Recognition Pattern searching residue composition patterns Threading SequenceStructure compatibility cturesequence compatibility Threading 0nly the local cnvnonmcntis taken into account Nunrlucal contacts are assumed wrtlr gcncnc peptide Nu gaps are allowcdintnc alignment Homology Modeling Programs Modellet t t guilar rockefellel ettuinotleller SwissModel httpwwwexpasyChswissmod Whau39f littpWwwcmbiknn nUw natit E a Introduction to Bioinformatics Iosif Vaisman Email ivaismanE gmu edu Secondary Structure Computational Problems Secondary structure characterization Secondary structure predic ion Protein structure classification Secondary Structure Conformations H R Peptide H torsion I r Cam ang es 3 N phi pg C A C I omega 3W3 H Secondary Structure Computational Problems Secondary structure characterization Secondary structure assignment Secondary structure prediction Protein structure classification Protein Structure Hierarchy we 39Primary the sequence of amino addresidues Secondary orderedregions ofprimary sequence he1ices betasheets tums Tertiary the threedimensional fold of a protein subunit 39Quatemary the arrangement of subunits in oligomers The Ramachandran Plot Left handed alphahelix Right handed alphahelix o phi 130 Secondary Structure Conform ations w alphahellx 757 747 alpharL 57 47 3 ID helix 49 26 7t ellx 757 8D ype H helix 79 lSEI Bsheetpar lel 119 113 ashes anuparallel 139 135 Secondary Structure Assignment cmmmmwnw m a r4uualllmuw r l l Mamm Secondary Structure Computational Problems Secondary structure chamcterizatiorl Secondary structure assignment l4 4 Protein structure classi cation Secondary Structure Assignment W helix strand mil IDSSP ESTRIDE DDEHNE mugs omcmo omome propomon of semndary strunure m Secondary Structure Assignment mm mm lm Secondary Structure Prediction Threestate model helix strand coil Given a protein sequence NWVLSTAADMQGVVTDGMASGLDKD Predict a secondary 5mm sequence LLEEEELLLLHHHHHHHHHHLHHHL e o 5 statistical stereochemical Accuracy 5085 Statistical Methods Residue conformational preferences Glu Ala Leu Met Gln Lys Arg helix Val lle Tyr Cys Trp Phe Thr Gly Asn Pro Ser Asp ChouFasman algorithm Identification of helix and sheet quotnucleiquot Propagation until termination criteria met ChouFasman Algorithm Identification of helix and sheet quotnucleiquot 39 of6 residues with high helix propensity p gt 100 sheet 3 out of5 residues with high sheet propensity P gt100 Propagation until termination criteria met Turn prediction 1 10t gt 0000075 00 3 Pa lt Pturn gt Pb where 10t ff1f2f3 P v Chan G D Fannmmochamlmy 1974 112117222 Evolutionary Methods Taking into account related sequences helps in identi cation of structurally importan residues Algorithm find similar sequences construct multiple alignment use alignment pro le for secondary structure prediction Additional information used for prediction mu ions isics residue position in sequence sequence length ChouFasman Parameters Name Fla Plh Plturn fll fl11 fl1Z fl13 alanine i4z 83 ss uus uu7s uuas uueu minim as 93 95 u u7u u ius u use u use nspsrsis nerd iui e4 i4s u i47 u iiu H179 u D81 nspsrsgins s7 89 156 uisi uuse H191 ELDQI cysssins 7u 119 119 H149 uueu u ii7 H128 clussms nerd iei 37 74 uues uusu uu77 uus4 Glutaman in nu gs uu74 uugu uu37 uugu crysins 7e ies u iuz u uue n 19D u iez n semins iuu u7 95 ui4u uu47 uuga uue4 rs reams iuu isu 47 uu43 nn34 EIEI13 uues reams izi 13D 59 uusi uuze uuas uu7u Lysine ii4 74 JEIl u uee u iie u u7z u ugs nsshi 4e iue su uusu uuuz uui4 uuee phenylalanine 113 138 su u ueg u u4i u use u use Prulxn 7 ee iez uiuz u3ui nn34 uusu same 77 7e 143 uizu H139 u ize u ius rnrssnins 3 119 95 u uus u me u use u my rrypssphsn ms 137 95 uu77 EIEI13 uus4 u is7 was me 59 i47 ii4 uuuz uuse uii4 uize Van s ius i7u eu uusz uu4u uuzu uusa Likelihood ofa secondary structure state depends on the neighboring residues Window size Lis j8 residues Accuracy for a single sequence 60 Accuracy for an alignment 65 Perceptron Output layer 1 1f 2 Wili gt 9 y Input layer 0 otherw15e Learning process Aw Tp Ypipi Neural Networks Methods Helix Sheet Output layer 2 units Hidden layer 2 units 111th layer 7x21 units N MKFGNFLLTYQP PEL SQTE VMKRLVN39LGKASEGC Stereochemical Methods Hydropathic correlations in helices and sheem Stereochemical Methods Patterns of hydrophobic and hydrophilic residues in secondary structure elements 0 segregation of hydrophobic and hydrophilic residues 0 hydrophobic residues in the positions 125 and 145 0 oppositely charged polar residues in the positions 15 and 14 eg Glu i Lys i4 De nitions of hydrophobic and hydrophilic residues hydrophobicity scales are ambiguous lst helix in Palm face 3


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