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Nothing in ( computational ) biology makes sense except in the light of evolution PowerPoint Presentation
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Nothing in ( computational ) biology makes sense except in the light of evolution

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Nothing in ( computational ) biology makes sense except in the light of evolution - PowerPoint PPT Presentation


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Using (and abusing) sequence analysis to make biological discoveries . Nothing in ( computational ) biology makes sense except in the light of evolution. after Theodosius Dobzhansky (1970). Significant sequence similarity is evidence of homology.

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Nothing in ( computational ) biology makes sense except in the light of evolution


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    1. Using (and abusing) sequence analysis to make biological discoveries Nothing in (computational) biology makes sense except in the light of evolution after Theodosius Dobzhansky (1970)

    2. Significant sequence similarity is evidence of homology Only a small fraction of amino acid residues is directly involved in protein function (including enzymatic); the rest of the protein serves largely as structural scaffold Conserved sequence motifs are determinants of conserved ancestral functions

    3. Pre-sequencing era (before 1978-80) Study biological function Pre-genomic era (1980-1996) Study biological function Clone/sequence gene Analyze/interpret sequence Post-genomic era (1996- Analyze/interpret sequences of all genes Sequence genome Study biological function Prioritize targets The evolving roles of computational analysis in biology

    4. Sequence complexity Measure of the randomness of a sequence Random sequence - highest complexity (entropy) - globular protein domains Homopolymer - lowest complexity (entropy) - non-globular structures Algorithmic complexity QQQQQQQQQQQQQ = (Q)n KRKRKRKRKRKR = (KR)n ASDFGHKLCVNM - random sequence - no algorithm to derive from a simpler one

    5. seg BRCA1 45 3.4 3.7 > BRCA1.seg >gi|728984|sp|P38398|BRC1_HUMAN Breast cancer type 1 susceptibility protein 1-388 MDLSALRVEEVQNVINAMQKILECPICLEL IKEPVSTKCDHIFCKFCMLKLLNQKKGPSQ CPLCKNDITKRSLQESTRFSQLVEELLKII CAFQLDTGLEYANSYNFAKKENNSPEHLKD EVSIIQSMGYRNRAKRLLQSEPENPSLQET SLSVQLSNLGTVRTLRTKQRIQPQKTSVYI ELGSDSSEDTVNKATYCSVGDQELLQITPQ GTRDEISLDSAKKAACEFSETDVTNTEHHQ PSNNDLNTTEKRAAERHPEKYQGSSVSNLH VEPCGTNTHASSLQHENSSLLLTKDRMNVE KAEFCNKSKQPGLARSQHNRWAGSKETCND RRTPSTEKKVDLNADPLCERKEWNKQKLPC SENPRDTEDVPWITLNSSIQKVNEWFSR sdellgsddshdgesesnakvadvldvlne 389-458 vdeysgssekidllasdphealickservh sksvesnied 459-526 KIFGKTYRKKASLPNLSHVTENLIIGAFVT EPQIIQERPLTNKLKRKRRPTSGLHPEDFI KKADLAVQ ktpeminqgtnqteqngqvmnitnsghenk 527-635 tkgdsiqneknpnpieslekesafktkaep isssisnmelelnihnskapkknrlrrkss trhihalelvvsrnlsppn 636-995 CTELQIDSCSSSEEIKKKKYNQMPVRHSRN LQLMEGKEPATGAKKSNKPNEQTSKRHDSD TFPELKLTNAPGSFTKCSNTSELKEFVNPS LPREEKEEKLETVKVSNNAEDPKDLMLSGE RVLQTERSVESSSISLVPGTDYGTQESISL LEVSTLGKAKTEPNKCVSQCAAFENPKGLI HGCSKDNRNDTEGFKYPLGHEVNHSRETSI EMEESELDAQYLQNTFKVSKRQSFAPFSNP GNAEEECATFSAHSGSLKKQSPKVTFECEQ KEENQGKNESNIKPVQTVNITAGFPVVGQK DKPVDNAKCSIKGGSRFCLSSQFRGNETGL ITPNKHGLLQNPYRIPPLFPIKSFVKTKCK knlleenfeehsmsperemgnenipstvst 996-1089 isrnnirenvfkeasssninevgsstnevg ssineigssdeniqaelgrnrgpklnamlr lgvl 1090-1238 QPEVYKQSLPGSNCKHPEIKKQEYEEVVQT VNTDFSPYLISDNLEQPMGSSHASQVCSET PDDLLDDGEIKEDTSFAENDIKESSAVFSK SVQKGELSRSPSPFTHTHLAQGYRRGAKKL ESSEENLSSEDEELPCFQHLLFGKVNNIP sqstrhstvateclsknteenllslknsln 1239-1312 dcsnqvilakasqehhlseetkcsaslfss qcseledltantnt 1313-1316 QDPF Non-globular regions Globular domains

    6. 1422-1513 GSQPSNSYPSIISDSSALEDLRNPEQSTSE KAVLTSQKSSEYPISQNPEGLSADKFEVSA DSSTSKNKEPGVERSSPSKCPSLDDRWYMH SC sgslqnrnypsqeelikvvdveeqqleesg 1514-1616 phdltetsylprqdlegtpylesgislfsd dpesdpsedrapesarvgnipsstsalkvp qlkvaesaqspaa 1617-1863 AHTTDTAGYNAMEESVSREKPELTASTERV NKRMSMVVSGLTPEEFMLVYKFARKHHITL TNLITEETTHVVMKTDAEFVCERTLKYFLG IAGGKWVVSYFWVTQSIKERKMLNEHDFEV RGDVVNGRNHQGPKRARESQDRKIFRGLEI CCYGPFTNMPTDQLEWMVQLCGASVVKELS SFTLGTGVHPIVVVQPDAWTEDNGFHAIGQ MCEAPVVTREWVLDSVALYQCQELDTYLIP QIPHSHY

    7. 1422-1513 GSQPSNSYPSIISDSSALEDLRNPEQSTSE KAVLTSQKSSEYPISQNPEGLSADKFEVSA DSSTSKNKEPGVERSSPSKCPSLDDRWYMH SC sgslqnrnypsqeelikvvdveeqqleesg 1514-1616 phdltetsylprqdlegtpylesgislfsd dpesdpsedrapesarvgnipsstsalkvp qlkvaesaqspaa 1617-1863 AHTTDTAGYNAMEESVSREKPELTASTERV NKRMSMVVSGLTPEEFMLVYKFARKHHITL TNLITEETTHVVMKTDAEFVCERTLKYFLG IAGGKWVVSYFWVTQSIKERKMLNEHDFEV RGDVVNGRNHQGPKRARESQDRKIFRGLEI CCYGPFTNMPTDQLEWMVQLCGASVVKELS SFTLGTGVHPIVVVQPDAWTEDNGFHAIGQ MCEAPVVTREWVLDSVALYQCQELDTYLIP QIPHSHY

    8. Paradigm shift in database searching Traditional PSI-BLAST Set of homologs Query sequence Sequence database PSSM RPS-BLAST New Query sequence Domain architecture PSSM database

    9. DOMAIN ARCHITECTURE OF SELECTED BRCT PROTEINS BRCT RING BRCA1 BARD1 PHD-l BRCA1/BARD homolog plant CMP-trans REV1 yeast DPB11 yeast AZF PARP vertebrates PARP DNA ligase III ATP-dep ligase human HhH TdT eukaryotes polX RFC1 eukaryotes ATP and PCNA-binding DNA ligase bacteria NAD-dep ligase

    10. Use of profile libraries to examine domain representation in individual proteomes yeast 6,200 Detect domains using PSI-BLAST, IMPALA Compare domain distributions Profile library worm ~20,000 Chervitz SA, Aravind L, Sherlock G, Ball CA, Koonin EV, Dwight SS, Harris MA, Dolinski K, Mohr S, Smith T, Weng S, Cherry JM, Botstein D. 1998. Comparison of the complete protein sets of worm and yeast: orthology and divergence. Science 282: 2022-8

    11. Normalized domain counts in worm and yeast 1.Hormone receptor; 2.POZ; 3.EGF; 4.MATH; 5.PTPase; 6.Cation Channels; 7.PDZ; 8.SH2; 9.FNIII; 10.Homeodomain; 11.LRR; 12.EF hands; 13.Ankyrin; 14.RING finger; 15.C2H2 finger; 16.small GTPase; 17.RRM; 18.AAA+; 19.C6 finger

    12. Searching a domain library is often easier and more informative • than searching the entire sequence database. However, the latter • yields complementary information and should not be skipped • if details are of interest. • Varying the search parameters, e.g. switching composition-based statistics • on and off, can make a difference. • Using subsequences, preferably chosen according to objective criteria, • e.g. separation from the rest of the protein by a low-complexity linker, • may improve search performance. • Trying different queries is a must when analyzing protein (super)families. • Even hits below the threshold of statistical significance often are worth • analyzing, albeit with extreme care. Transferring functional information • between homologs on the basis of a database description alone is dangerous. • Conservation of domain architectures, active sites and other features • needs to be analyzed (hence automated identification of protein families is • difficult and automated prediction of functions is extremely error-prone). • Always do a reality check!