Mechanistic Enzymology MCB 221B
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This 37 page Class Notes was uploaded by Alvena Wilkinson on Tuesday September 8, 2015. The Class Notes belongs to MCB 221B at University of California - Davis taught by Staff in Fall. Since its upload, it has received 42 views. For similar materials see /class/187612/mcb-221b-university-of-california-davis in Molecular, Cellular And Developmental Biology at University of California - Davis.
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Date Created: 09/08/15
MOB221 b lecture 11 Protein Modeling da ta ba 555 and software At the beginning there were thoughts and g observation httpkoehllabgenomecentenucdavisedu From hypothesisdriven to exploratory data analysis 0 Data are used to formulate new hypotheses 0 Data are stored and disseminated via databases of information allowing open access to the records held within them bioinformalics designing novel algorithms and methods of analyses help solve biological problems computational biology Statistics on Genome Databases Sept 2007 Breakdown by entry pa Iy En Type quotmes Nucleotides Standard 76829335 80196199160 Constructed mom 1302501 11 Thlrd Party Annotation WA 337 018855 Whale Gename Shotgun W63 253431115 101998 758037 EMEL Database Growth Top organisms Ely nucleotide calm Vear I Hume saprens I Mus museums I Eastaurus I Rams nawegicus I manne metagenome I Fanlrugtudnes I Canismpusramrlrarrs I Zeamays I Macaca mulatta Munudelpmsdcmeshca D Other Current state of the PDB mm mm rm Vearly Growth of Total Structures res an In View mnusa n numhu nf suuuu Is there a danger in molecular biology that the accumulation of data will get so far ahead of its assimilation into a conceptual amework that the data will eventually prove an encumbrance John Maddox 1988 se uencin q g crystallization NMR linear I Genome DBS I Protein t t DB 3 rue ure s predlctlons CASP validations I I A algor1thms class1 catlons CATH etc Protein Structure Classification Classical tool biology It is easier to think about a representative than to embrace the information of all individuals Aristotle Plants and Animal Linnaeus binomial system Darwin systematic classi cation that reveals phylogeny Clustering Domain De nition 3 Major classi cations SCOP CATH DDD Differences Clustering is a hard problem Many possibilities What is best clustering 2 clusters easy Hierarchical Clustering To cluster a set of data DP1 P2 PN hierarchical clustering proceeds through a series of partitions that runs from a single cluster containing all data points to N clusters each containing 1 data points Two forms of hierarchical clustering Agglomerative Divisive Delineating protein domains Looking at secondary structure Authors Sowdhamini and Blundel Protein Science 4506 1996 De nition of a domain a cluster of secondary structure Method clustering of the secondary structures in a protein Distance between secondary structures Delineating protein domain a bottomup procedure Authon WR Taylor Protein Engineering 12 203216 1999 Idea classical methods for de ning protein domains starts from an hypothesis de nition of what a domain is and check how the data verify that hypothesis Protein Structural Domains 1 2 3 4 5 Protein Domain various definitions exist Regions that display significant levels of sequence similarity The minimal part of a gene that is capable of performing a function A region of a protein with an experimentally assigned function Region of a protein structure that recurs in different contexts and proteins A compact spatially distinct region of a protein Web services for domain identification Program Web access DIAL httpWWWncbsresinfacultyminiddbasedialhtml DomainParser httpcompbi00rn1 govstructuredomainparser DOMAK httpWWWcompbi0dundeeacukS oftwareDomakdomakhtml PDP http 123dncifcrfg0Vpdphtml Protein Structure Primary structure Secondary Structure Tertiary Structure Sequence of Amino acids Local interactions Native protein Why do proteins fold Unfolded Stale Protein backbone is a linear chain Chain is selfavoiding Protein is closely packed Amino Acid preferences inside hydrophobic outside hydrophilic Specific interactions Interactions with solvent Interactions with ions concentration of proteins Folded Stale D s solvent Protein tertiary structure Packing Helixhelix packing Sheetsheet packing 0 angle between the 2 axes r F i 20 degree between sheets orthogonal BaCkb me SidEChai Parallel sheets tend to be covered by helices on both sides Antiparallel sheets tend to have one side covered by a sheet sandwichtype structure Two types ofpacking aligned or orthogonal Helixsheet packing Because the periodicities of helices and strands are different there is not regular packing patterns Helices tend to be on both sides ofparallel beta sheets Protein tertiary structure Architecture cIases alpha folds lone helix helixturnhelix WW 6 Glucagon RNA binding protein Myohemerythn39n RNA binding protein dimer beta folds Fi Greek key topology J ellyroll topology a greek key with extra swirl 5 13 strands beta sandwich FA binding protein beta helix Protein tertiary structure Architecture classes alphabeta folds 7 r39 W I Rossman fold I horseshoe rlght handed alternate 0c 3 motlf w C barrel TIM barrel Molecular Graphics Rasmol httpWWWurnassedumicrobiOrasmolindeXZhtm Pymol httpme01sourcef0rgenet Pymol tutorials httpWwwmicr0biologvubccaeltispvmol httpWwwebiacukgarethpvm01 Chime httpwwwumassedumicrobiochimegetchimehtm Classi cation of Protein Structure CATH Mixed Alpha Beta a Beta C Super Roll A Other Barrel T httpWWWcathdbinfoIatest ndexhtml Classi cation of Protein Structure SCOP ttpscop mrclmb cam ac ukscop SCOP is organized into 4 hierarchical layers httpySCOpberkeeyedu 1 Classes similar to CA TH alpha beta alphabeta alphabeta multidomain proteins walpha and beta membrane and cell surface proteins small proteins coiled coils low resolution prot 2 Folds Major structural similarity Proteins are de ned as having a common fold if they have the same major secondary structures in the same arrangement and with the same topological connections 3 Superfamily Probable common evolutionary origin Proteins that have low sequence identities but whose structural and functional features suggest that a common evolutionary origin is probable are placed together in superfamilies 4 Family Clear evolutionarily relationship Proteins clustered together into families are clearly evolutionarily related Generally this means that pairwise residue identities between the proteins are 30 and greater Classification of Protein Structure SCOP SCOP Structural Classi cation of Proteins 169 release 25973 PDB Entries 1 Oc12004 70859 Domains 1 Literature Reference excluding nucleic acids and theoretical models 500R Structural Classi cation ofProta39ns 171 release 39 17m 7 h 39l ivl l 39 excluding nucleic acids and theoretical models The DALI Domain Dictionary Dali Fold Classification r ciassmcalmn and alignmenls are aulumalicaiiy mainlame arm veguiariy uDdaled using me Dali Search engine m can enlev me i m an mum in t Lzsl Undale Mammus a Keyword Search Emeer menli en pmlem name m kaywnm b View Complete Fold Classi ca oll 1 FoannEx The cumplele lislulslmduval dumams m Pusan umeredhysimilari39y 1 nme Alree mime slmdural dumams m Pusan m Duslscrlvllurmal Resnurees 1 nowmmnsnursequencemes mysqiaumpmes amineuanmeslannaiuneapplicalmn 1 HELP usng and linkmglu me Dali Dalzhase explanaliun ullevms all velerences Reference Hulm L SanderCUQQBWaDDmglheDvuleinumverse Semen 595503 We no any darkens mammal s e 12 m mm w cme WW e u m leey we httpekhidna biocenter helsinki fIVdaIVstan Domain classifications Classification is an important part of biology protein structures are not exempt Prior to being classified proteins are cut into domains Whiea structural biologists agree that proteins are usually a collection of domains there IS no consensus on how to delineate the domains There are three main protein structure classification SCOP manual source of evolutionaiy information CATH semiautomatic source of geometric information Dali automatic source of raw data Protein Structure Comparison The protein structure is a 3D shape the goal is to nd algorithms that nd the optimal match between two shapes Global versus local alignment Measuring protein shape similarity Protein structure superposition Protein structure alignment Global Alignment i M Global align ent Local Alignment 111 0 t1 f g 2 Local alignm nt Protein Structure Comparison software ROC receiveroperator curves Rate of true positives IM I 7 Structlal 93 III N m Fasta 056 5 a i II A I I I I I I I I quotn m 2 so an in m in 541 Rate of true negatives 1393 W10 Writhe performs better than C 5 Curvature Structure Prediction Available servers JPRED httpwwwcompbiodundeeacukwwwipred PHD httpcubicbioccoumbiaedupredictprotein PSIPRED httpbioinfcsuclacukpsipred NNPREDICT httpwwwcmpharmucsfedunominnpredicthtm Chou and Fassman httpfastabiochvirqiniaedufasta wwwchofashtm lnteresting paper Rost and Eyrich E VA Largescale analysis of secondary structure prediction Proteins 5 192199 2001 Protein Structure Prediction One popular model for protein folding assumes a sequence of events Hydrophobic collapse Local interactions stabilize secondary structures Secondary structures interact to form motifs Motifs aggregate to form tertiary structure Protein Structure Prediction A physicsbased approach find conformation of protein corresponding to a thermodynamics minimum free energy minimum cannot minimize internal energy alone Needs to include solvent simulate foldinga very long process Folding time are in the ms to second time range Folding simulations at best run 1 ns in one day The CASP experiment CASP Critical Assessment of Structure Prediction Started in 1994 Moult Pederson Judson Fidelis Proteins 2325 1995 First run in 1994 now runs regularly every second year CASP6 was held last December 1 Sequences of target proteins are made available to CASP participants in JuneJuly of a CASP year the structure of the target protein is known but not yet released in the PDB or even accessible 2 CASP participants have between 2 weeks and 2 months over the summer of a CASP year to generate up to 5 models for each of the target they are interested in 3 Model structures are assessed against experimental structure 4 CASP participants meet in December to discuss results CASP Three categories at CASP Homology or comparative modeling Fold recognition Ab initio prediction CA SP dynamics Real deadlines pressure positive or negative Competition Influence on science Venclovas Zemla Fidelis Moult Assessment of progress overthe CASP experiments Proteins 53585595 2003 CASP overall quality homology I 00 ACASPI Best 6 groups a CASP 80 ICASP3 7n CASP4 ICASPS 5quot on m l 50 I n E I U 10 a I d I 30 e I I39l A n 20 e e 533 39 In x 5 U CQ6L b wb39k1x IV I 5 AVG a Q bxLS aw a a o 43 s 6 e sags a x60 Q e wow lt Target difficulty Easy mam Venclavas Zemla Fidelis Moult Assessment of progress over the CASP experiment Proteins 53585595 2003 Homology Modeling Practical guide Approach 1 manualy BLAST then a range of steps you d need to learn Approach 2 Submit target sequence to automatic servers Fully automatic 3DJigsaw httpwwwbmmicnetuklserversl3diiqsawl EsyPred3D httpwwwfundpacbeurbmbioinfoesvpred SwissModel httpswissmodelexpasvorqlSWISSMODELhtml Fold recognition PHYRE httpwwwsbqbioicacuklphyrel Useful sites Meta server httgzllbioinfopIMeta PredictProtein httpcubicbioccoumbiaedupredictprotein Small proteins can be de novo predicted at least about 50 at lt 5A 480 E srzuo a 5220 2 7240 5 lo 15 2a 2 rmsd i Freeenergy landscape for the small g r m 39 ue 0 nts or a n f cu e r de new models black points The neeenergy CASPE blue with the trystel structure red 39 39 39 o e w z shawlng more side chains This the salvation free energy but not the cun gura figure was generated in PyMOL 22 tiunal entropv Poor 7 caught in local free energy minimum
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