Nothing in (computational) biology makessense except in the light of evolution

after Theodosius Dobzhansky (1970)

Using (and abusing) sequence analysis to make biological discoveries

Only a small fraction of amino acid residues is directlyinvolved in protein function (including enzymatic);the rest of the protein serves largely as structuralscaffold

Significant sequence similarity is evidence of homology

Conserved sequence motifs are determinants ofconserved ancestral functions

The evolving roles of computational analysis in biology

Pre-sequencing era (before 1978-80)

Study biological function

Study biological function Clone/sequence gene

Analyze/interpret sequence

Pre-genomic era (1980-1996)

Sequence genomeAnalyze/interpret sequences

of all genes

Prioritize targetsStudy biological function

Post-genomic era (1996-

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 derivefrom a simpler one

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 SENPRDTEDVPWITLNSSIQKVNEWFSRsdellgsddshdgesesnakvadvldvlne 389-458vdeysgssekidllasdphealickservh sksvesnied 459-526 KIFGKTYRKKASLPNLSHVTENLIIGAFVT EPQIIQERPLTNKLKRKRRPTSGLHPEDFI KKADLAVQktpeminqgtnqteqngqvmnitnsghenk 527-635tkgdsiqneknpnpieslekesafktkaepisssisnmelelnihnskapkknrlrrkss trhihalelvvsrnlsppn 636-995 CTELQIDSCSSSEEIKKKKYNQMPVRHSRN LQLMEGKEPATGAKKSNKPNEQTSKRHDSD TFPELKLTNAPGSFTKCSNTSELKEFVNPS LPREEKEEKLETVKVSNNAEDPKDLMLSGE RVLQTERSVESSSISLVPGTDYGTQESISL LEVSTLGKAKTEPNKCVSQCAAFENPKGLI HGCSKDNRNDTEGFKYPLGHEVNHSRETSI EMEESELDAQYLQNTFKVSKRQSFAPFSNP GNAEEECATFSAHSGSLKKQSPKVTFECEQ KEENQGKNESNIKPVQTVNITAGFPVVGQK DKPVDNAKCSIKGGSRFCLSSQFRGNETGL ITPNKHGLLQNPYRIPPLFPIKSFVKTKCKknlleenfeehsmsperemgnenipstvst 996-1089isrnnirenvfkeasssninevgsstnevgssineigssdeniqaelgrnrgpklnamlr lgvl 1090-1238 QPEVYKQSLPGSNCKHPEIKKQEYEEVVQT VNTDFSPYLISDNLEQPMGSSHASQVCSET PDDLLDDGEIKEDTSFAENDIKESSAVFSK SVQKGELSRSPSPFTHTHLAQGYRRGAKKL ESSEENLSSEDEELPCFQHLLFGKVNNIPsqstrhstvateclsknteenllslknsln 1239-1312dcsnqvilakasqehhlseetkcsaslfss qcseledltantnt 1313-1316 QDPF

Non-globular regionsGlobular domains

1422-1513 GSQPSNSYPSIISDSSALEDLRNPEQSTSE KAVLTSQKSSEYPISQNPEGLSADKFEVSA DSSTSKNKEPGVERSSPSKCPSLDDRWYMH SCsgslqnrnypsqeelikvvdveeqqleesg 1514-1616phdltetsylprqdlegtpylesgislfsddpesdpsedrapesarvgnipsstsalkvp qlkvaesaqspaa 1617-1863 AHTTDTAGYNAMEESVSREKPELTASTERV NKRMSMVVSGLTPEEFMLVYKFARKHHITL TNLITEETTHVVMKTDAEFVCERTLKYFLG IAGGKWVVSYFWVTQSIKERKMLNEHDFEV RGDVVNGRNHQGPKRARESQDRKIFRGLEI CCYGPFTNMPTDQLEWMVQLCGASVVKELS SFTLGTGVHPIVVVQPDAWTEDNGFHAIGQ MCEAPVVTREWVLDSVALYQCQELDTYLIP QIPHSHY

Re moving spurious database hits for the low se que nce comple xity prote in BRCA1 by modifying SEG parame te rs a

Number of residues E-value s of the BLAST hits Parame te r se t of SEGa Maske

d Unmaske d Dentin Plant

BRCA1

Opossum BRCA1

No filtering 0 1,863 3e-11 4e-15 1e-28

12 2.1 2.4 35 1,828 4e-9 4e-15 1e-28 12 2.2 2.5 (default)

117 1,746 5e-4 5e-12 7e-22

12 2.3 2.6 172 1,691 - 5e-11 3e-21 12 2.4 2.7 279 1,584 - 5e-11 1e-14 12 2.5 2.8 487 1,376 - 6e-11 8e-10 12 2.6 2.9 616 1,247 - 2e-10 5e-9 12 2.7 3.0 908 955 - 4e-06 2e-8 12 2.8 3.1 1,164 699 - 0.003 6e-7

Composition-based filtering

0 1,863 - 3e-12 1e-20

aSEG parameters are trigger window length, trigger complexity, and extension

1422-1513 GSQPSNSYPSIISDSSALEDLRNPEQSTSE KAVLTSQKSSEYPISQNPEGLSADKFEVSA DSSTSKNKEPGVERSSPSKCPSLDDRWYMH SCsgslqnrnypsqeelikvvdveeqqleesg 1514-1616phdltetsylprqdlegtpylesgislfsddpesdpsedrapesarvgnipsstsalkvp qlkvaesaqspaa 1617-1863 AHTTDTAGYNAMEESVSREKPELTASTERV NKRMSMVVSGLTPEEFMLVYKFARKHHITL TNLITEETTHVVMKTDAEFVCERTLKYFLG IAGGKWVVSYFWVTQSIKERKMLNEHDFEV RGDVVNGRNHQGPKRARESQDRKIFRGLEI CCYGPFTNMPTDQLEWMVQLCGASVVKELS SFTLGTGVHPIVVVQPDAWTEDNGFHAIGQ MCEAPVVTREWVLDSVALYQCQELDTYLIP QIPHSHY

Paradigm shift in database searching

Querysequence

Sequence database

Set of homologs

PSSM

Querysequence

PSSM databaseDomainarchitecture

Traditional

New

PSI-BLAST

RPS-BLAST

BRCA1RING

BARD1

DOMAIN ARCHITECTURE OF SELECTED BRCT PROTEINS

BRCTBRCT

CMP-trans REV1 yeast

DPB11 yeast

ATP-dep ligase DNA ligase IIIhuman

AZFPARP

PARPvertebrates

HhHpolX TdT eukaryotes

ATP and PCNA-binding RFC1

NAD-dep ligase DNA ligasebacteria

eukaryotes

PHD-lBRCA1/BARDhomolog plant

Use of profile libraries to examine domain representation in individual proteomes

Profile library

6,200

~20,000

yeast

worm

0

100

0

100

Detect domainsusingPSI-BLAST,IMPALA

Compare domaindistributions

Chervitz SA, Aravind L, Sherlock G, Ball CA, Koonin EV, Dwight SS, Harris MA, Dolinski K, Mohr S, SmithT, Weng S, Cherry JM, Botstein D. 1998. Comparison of the complete protein sets of worm and yeast: orthology and divergence. Science 282: 2022-8

Normalized domain counts in worm and yeast

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12Yeast

Wo

rm1

2

3

4

5

6 7

98

10

11

12

13

14

15

16 17

18

19

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

•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!