Databases and Comparisons - CMB Education - Karolinska Institutet

Introduction to BioinformaticsDatabases and ComparisonsFredrik Lysholm

BioinformaticsMedicineBiologyComputer scienceMathematicsStatistics Investigate functions of proteins and genes– Sequence comparisons of DNA and protein– Sequence patterns– Stru ctural calculations– Molecular modelling– Predictions Data management–Databases of sequences and sequence-related informationLinköping University & Karolinska Institutet 2

Genome comparisons

Genome projects15 February 2001Linköping University & Karolinska Institutet 4

Completed genomesGenome sizeMb1000010001001010.10.010.0011975 1980 1985 1990 1995 2000 2005YearYear Organism Size (Mb) Genes1977 bacteriophage Φ X174 0.0054 91995 Haemophilus influenzae 1.83 1 6781996 Saccharomyces cerevisiae 13 6 2842001 Homo sapiens 3 200 40 000 (?)Linköping University & Karolinska Institutet 5

Completed genomes35302520151050Complete genomes in public databases (GOLD)1995 1996 1997 1998 1999 2000 2001First bacterial in 1995First eukaryotic in 1996Human in 2001Over 100 genomes(June 2003)Genomes in each kingdomEukaryotaArchaeEubacteriaLinköping University & Karolinska Institutet 6

Genome comparisonsH. sapiensclusterD. melanogaster C. elegans A. thalianaLinköping University & Karolinska Institutet 7

Molecular modelling

Protein foldingDNAProtein?Linköping University & Karolinska Institutet 9

Structure predictionsSecondary structure prediction– Presently, at most 75% correct assignments ofα, β and coilAb initio prediction– limited to ~23 amino acid residuesHomology modelling– requires template– quite good accuracySubstrate docking– getting better and betterLinköping University & Karolinska Institutet 10

Molecular modellingHomology modelling– an amino acid sequence is adopted to the structure of ahomologous protein with known three-dimensionalstructure– used to get an idea about the three-dimensional structurein lack of one that is experimentally determinedSubstrate docking– a substrate is modelled at the active site of a protein withknown three-dimensional structure– used to• investigate possible interactions• screen for potential substratesLinköping University & Karolinska Institutet 11

Docking of bile acids into class I γγ ADHLinköping University & Karolinska Institutet 12

Docking of bile acids into class I γγ ADH2.19Distances in Å at active siteγγ-ADH48(Og)--Ho3Zn--O3C4(NAD)--H3isoUDCA 2.22 2.30 2.19UDCA 3.20 2.88 3.532.22ββ-ADHisoUDCA 3.35 2.24 1.94UDCA 2.31 2.51 2.942.30ADH=alcohol dehydrogenaseUDCA=ursodeoxycholic acidLinköping University & Karolinska Institutet 13

Predictions ofstructure and function

Predictions of important regionsComparisons of related proteins– gives a number of conserved positionsKnown three-dimensional structure– in which the locations of these positions aremarkedLinköping University & Karolinska Institutet 15

Multiple sequence alignmentLinköping University & Karolinska Institutet 16

Conserved amino acid residuesLinköping University & Karolinska Institutet 17

Databases

Databases with sequence informationAmino acid sequences– Uniprot/Swissprot– Uniprot/TrEMBL– NCBI NRNucleotide sequences– EMBL, Genbank, NCBI NTThree-dimensional structures– PDB/BrookhavenLinköping University & Karolinska Institutet 19

SwissprotHigh level of annotation– description of protein function– domain structure– post-translational modifications– heterogeneity/variantsMinimal level of redundancyIntegration with other databases.One of the best protein sequence databasesdue to the quality of the annotation.Linköping University & Karolinska Institutet 20

Example of Swissprot entryID 2BHD_STREX STANDARD; PRT; 255 AA.AC P19992;DT 01-FEB-1991 (REL. 17, CREATED)DT 01-FEB-1991 (REL. 17, LAST SEQUENCE UPDATE)DT 01-NOV-1997 (REL. 35, LAST ANNOTATION UPDATE)DE 20-BETA-HYDROXYSTEROID DEHYDROGENASE (EC 1.1.1.53).OS STREPTOMYCES EXFOLIATUS (STREPTOMYCES HYDROGENANS).OC PROKARYOTA; FIRMICUTES; ACTINOMYCETALES; STREPTOMYCETACEAE.RN [1]RP SEQUENCE.RX MEDLINE; 90306362.RA MAREKOV L., KROOK M., JOERNVALL H.;RL FEBS LETT. 266:51-54(1990).RN [2]RP X-RAY CRYSTALLOGRAPHY (2.6 ANGSTROMS).RX MEDLINE; 92052211.RA GHOSH D., WEEKS C.M., GROCHULSKI P., DUAX W.L., ERMAN M.,RA RIMSAY R.L., ORR J.C.;RL PROC. NATL. ACAD. SCI. U.S.A. 88:10064-10068(1991).Linköping University & Karolinska Institutet 21

Example of Swissprot entry, cont.CC -!- CATALYTIC ACTIVITY: ANDROSTAN-3-ALPHA,17-BETA-DIOL + NAD(+) =CC 17-BETA-HYDROXYANDROSTAN-3-ONE + NADH.CC -!- SUBUNIT: HOMOTETRAMER.CC -!- SIMILARITY: BELONGS TO THE SHORT-CHAIN DEHYDROGENASES/REDUCTASESCC FAMILY (SDR).DR PIR; S10707; S10707.DR PDB; 2HSD; 31-AUG-94.DR PDB; 1HDC; 07-FEB-95.DR PROSITE; PS00061; ADH_SHORT; 1.KW OXIDOREDUCTASE; NAD; STEROID METABOLISM; 3D-STRUCTURE.FT NP_BIND 10 34 NAD (BY SIMILARITY).FT ACT_SITE 152 152SQ SEQUENCE 255 AA; 26484 MW; 49F64254 CRC32;MNDLSGKTVI ITGGARGLGA EAARQAVAAG ARVVLADVLD EEGAATAREL GDAARYQHLDVTIEEDWQRV VAYAREEFGS VDGLVNNAGI STGMFLETES VERFRKVVDI NLTGVFIGMKTVIPAMKDAG GGSIVNISSA AGLMGLALTS SYGASKWGVR GLSKLAAVEL GTDRIRVNSVHPGMTYTPMT AETGIRQGEG NYPNTPMGRV GNEPGEIAGA VVKLLSDTSS YVTGAELAVDGGWTTGPTVK YVMGQ//Linköping University & Karolinska Institutet 22

TrEMBLA computer-annotated supplement toSwissprot, containing all translations ofEMBL entries not found in Swissprot.Linköping University & Karolinska Institutet 23

EMBLSequences submitted directly by scientistsand genome sequencing groups.Sequences taken from literature and patents.There is comparatively little error checkingand there is a fair amount of redundancy.Daily syncronising with GenBank (USA) andDDBJ (Japan).Linköping University & Karolinska Institutet 24

Example of EMBL entryID HS3B5H5E standard; RNA; HUM; 1528 BP.XXAC X55997;XXNIXXDTg2386117-APR-1991 (Rel. 28, Created)DT 17-FEB-1997 (Rel. 50, Last updated, Version 7)XXDEXXKWXXOSOCOCXXHuman gene for 3-beta-5-hydroxy-5-ene steroid dehydrogenase3 beta-hydroxy-5-ene steroid dehydrogenase; dehydrogenase; isomerase.Homo sapiens (human)Eukaryotae; mitochondrial eukaryotes; Metazoa; Chordata; Vertebrata;Mammalia; Eutheria; Primates; Catarrhini; Hominidae; Homo.Linköping University & Karolinska Institutet 25

Example of EMBL entry, cont.RN [1]RA Sutcliffe R.G.;RT ;RLRLRLXXRN [2]...XXDRDRSubmitted (25-OCT-1990) to the EMBL/GenBank/DDBJ databases.Sutcliffe R.G., Dept. of Genetics, Church Sreet Glasgow University,Glasgow G11, 5JS.GDB; 120056; HSD3B1.SWISS-PROT; P14060; 3BH1_HUMAN.XXFH Key Location/QualifiersFHFT source 1..1528FTFTFTFT/organism="Homo sapiens"/tissue_type="placenta"/clone_lib="lambda gt11"/clone="1/6,B/6"Linköping University & Karolinska Institutet 26

Example of EMBL entry, cont.FT CDS 1..1122FT/db_xref="PID:g23862"FTFTFTFTFTFTFTFTFTFT/db_xref="SWISS-PROT:P14060"/EC_number="1.1.1.145"/product="3-beta-hydroxy-5-ene steroid dehydrogenase"/translation="MTGWSCLVTGAGGFLGQRIIRLLVKEKELKEIRVLDKAFGPELREEFSKLQNKTKLTVLEGDILDEPFLKRACQDVSVIIHTACIIDVFGVTHRESIMNVNVKGTQLLLEACVQASVPVFIYTSSIEVAGPNSYKEIIQNGHEEEPLENTWPAPYPHSKKLAEKAVLAANGWNLKNGGTLYTCALRPMYIYGEGSRFLSASINEALNNNGILSSVGKFSTVNPVYVGNVAWAHILALRALQDPKKAPSIRGQFYYISDDTPHQSYDNLNYTLSKEFGLRLDSRWSFPLSLMYWIGFLLEIVSFLLRPIYTYRPPFNRHIVTLSNSVFTFSYKKAQRDLAYKPLYSWEEAKQKTVEWVGSLVDRHKETLKSKTQ"FT mRNA 1..1528FT/evidence=EXPERIMENTALFT/note="3-beta-hydroxy-5-ene steroid dehydrogenase"FT polyA_signal 1511..1516Linköping University & Karolinska Institutet 27

Example of EMBL entry, cont.FT allele 1100FT/note="aa:ASN, see ref.[3]"FTFT allele 1012FTFTXXSQ...///replace="a"/note="see ref.[2]"/replace="t"Sequence 1528 BP; 410 A; 374 C; 364 G; 380 T; 0 other;ATGACGGGCT GGAGCTGCCT TGTGACAGGA GCAGGAGGGT TTCTGGGACA GAGGATCATC 60CGCCTCTTGG TGAAGGAGAA GGAGCTGAAG GAGATCAGGG TCTTGGACAA GGCCTTCGGA 120CCAGAATTGA GAGAGGAATT TTCTAAACTC CAGAACAAGA CCAAGCTGAC AGTGCTGGAA 180TGAACAATTT AGGGACTCTT TTAACTTGAG GGTCGTTTTG ACTACTAGAG CTCCATTTCT 1440ACTCTTAAAT GAGAAAGGAT TTCCTTTCTT TTTAATCTTC CATTCCTTCA CATAGTTTGA 1500TAAAAAGATC AATAAATGTT TGAATGTT 1528Linköping University & Karolinska Institutet 28

Example of PDB entryHEADER OXIDOREDUCTASE 28-MAR-94 2HSD 2HSD 2COMPND 3 ALPHA, 20 BETA-HYDROXYSTEROID DEHYDROGENASE (HOLO FORM) 2HSD 3COMPND 2 (E.C.1.1.1.53) 2HSD 4SOURCE (STREPTOMYCES HYDROGENANS) 2HSD 5AUTHOR D.GHOSH,W.L.DUAX 2HSD 6REVDAT 1 31-AUG-94 2HSD 0 2HSD 7SPRSDE 31-AUG-94 2HSD 1HSD 2HSD 8JRNL AUTH D.GHOSH,Z.WAWRZAK,C.M.WEEKS,W.L.DUAX,M.ERMAN 2HSD 9JRNL TITL THE REFINED THREE-DIMENSIONAL STRUCTURE OF 2HSD 10JRNL TITL 2 3ALPHA,20BETA-HYDROXYSTEROID DEHYDROGENASE AND 2HSD 11JRNL TITL 3 POSSIBLE ROLES OF THE RESIDUES CONSERVED IN 2HSD 12JRNL TITL 4 SHORT-CHAIN DEHYDROGENASES 2HSD 13JRNL REF STRUCTURE V. 2 629 1994 2HSD 14JRNL REFN ASTM UK ISSN 0969-2126 2005 2HSD 15REMARK 1 2HSD 16REMARK 1 REFERENCE 1 2HSD 17REMARK 1 AUTH D.GHOSH,C.M.WEEKS,P.GROCHULSKI,W.L.DUAX,M.ERMAN, 2HSD 18REMARK 1 AUTH 2 R.L.RIMSAY,J.C.ORR 2HSD 19Linköping University & Karolinska Institutet 29

Example of PDB entry, cont.REMARK 1 TITL THREE-DIMENSIONAL STRUCTURE OF HOLO 2HSD 20REMARK 1 TITL 2 3ALPHA,20BETA-HYDROXYSTEROID DEHYDROGENASE: 2HSD 21REMARK 1 TITL 3 A MEMBER OF A SHORT-CHAIN DEHYDROGENASE FAMILY 2HSD 22REMARK 1 REF PROC.NAT.ACAD.SCI.USA V. 88 10064 1991 2HSD 23REMARK 1 REFN ASTM PNASA6 US ISSN 0027-8424 0040 2HSD 24REMARK 2 2HSD 25REMARK 2 RESOLUTION. 2.64 ANGSTROMS. 2HSD 26REMARK 3 2HSD 27REMARK 3 REFINEMENT. 2HSD 28REMARK 3 PROGRAM 1 PROLSQ 2HSD 29REMARK 3 AUTHORS 1 KONNERT,HENDRICKSON 2HSD 30REMARK 3 PROGRAM 2 X-PLOR 2HSD 31REMARK 3 AUTHORS 2 BRUNGER 2HSD 32REMARK 3 R VALUE 0.188 2HSD 33REMARK 3 RMSD BOND DISTANCES 0.012 ANGSTROMS 2HSD 34REMARK 3 RMSD BOND ANGLE DISTANCES 0.035 ANGSTROMS 2HSD 35REMARK 3 2HSD 36REMARK 3 NUMBER OF REFLECTIONS 28327 2HSD 37REMARK 3 RESOLUTION RANGE 8.0 - 2.64 ANGSTROMS 2HSD 38Linköping University & Karolinska Institutet 30

Example of PDB entry, cont.SEQRES 1 A 253 ASN ASP LEU SER GLY LYS THR VAL ILE ILE THR GLY GLY 2HSD 86SEQRES 2 A 253 ALA ARG GLY LEU GLY ALA GLU ALA ALA ARG GLN ALA VAL 2HSD 87SEQRES 3 A 253 ALA ALA GLY ALA ARG VAL VAL LEU ALA ASP VAL LEU ASP 2HSD 88SEQRES 4 A 253 GLU GLU GLY ALA ALA THR ALA ARG GLU LEU GLY ASP ALA 2HSD 89...HET NAD A 256 44 NICOTINAMIDE-ADENINE-DINUCLEOTIDE 2HSD 166HET NAD B 256 44 NICOTINAMIDE-ADENINE-DINUCLEOTIDE 2HSD 167HET NAD C 256 44 NICOTINAMIDE-ADENINE-DINUCLEOTIDE 2HSD 168HET NAD D 256 44 NICOTINAMIDE-ADENINE-DINUCLEOTIDE 2HSD 169FORMUL 5 NAD 4(C21 H28 N7 O14 P2) 2HSD 170HELIX 1 BA ARG A 16 ALA A 29 1 2HSD 171HELIX 2 CA ASP A 40 ALA A 54 1 2HSD 172HELIX 3 DA TRP A 67 GLU A 77 1 2HSD 173...SHEET 1 S1A 7 GLN A 57 THR A 62 0 2HSD 195SHEET 2 S1A 7 GLY A 30 VAL A 38 1 N LEU A 35 O LEU A 59 2HSD 196SHEET 3 S1A 7 GLY A 6 THR A 12 1 N ILE A 10 O VAL A 34 2HSD 197SHEET 4 S1A 7 ASP A 82 GLY A 93 1 N ASP A 82 O LYS A 7 2HSD 198Linköping University & Karolinska Institutet 31

Example of PDB entry, cont.SHEET 5 S1A 7 GLY A 130 SER A 139 1 N VAL A 135 O LEU A 84 2HSD 199SHEET 6 S1A 7 ARG A 174 GLY A 183 1 O ARG A 174 N GLY A 132 2HSD 200SHEET 7 S1A 7 GLY A 234 ALA A 238 1 N LEU A 237 O SER A 179 2HSD 201...TURN 5 EA LYS A 127 GLY A 130 2HSD 227TURN 6 FA LEU A 170 ASP A 173 2HSD 228TURN 7 GA VAL A 210 GLU A 213 PART OF THE LONGEST LOOP 2HSD 229...CRYST1 106.200 106.200 203.800 90.00 90.00 90.00 P 43 21 2 32 2HSD 251...ATOM 1 N ASN A 2 100.288 -12.391 81.699 1.00 50.99 2HSD 267ATOM 2 CA ASN A 2 99.826 -12.757 83.069 1.00 50.77 2HSD 268ATOM 3 C ASN A 2 99.264 -11.499 83.704 1.00 51.56 2HSD 269ATOM 4 O ASN A 2 99.981 -10.517 83.905 1.00 54.06 2HSD 270ATOM 5 CB ASN A 2 100.992 -13.311 83.883 1.00 53.10 2HSD 271ATOM 6 CG ASN A 2 101.673 -14.476 83.192 1.00 53.65 2HSD 272ATOM 7 OD1 ASN A 2 101.646 -14.567 81.964 1.00 53.85 2HSD 273ATOM 8 ND2 ASN A 2 102.269 -15.374 83.965 1.00 50.11 2HSD 274Linköping University & Karolinska Institutet 32

Pairwise sequence comparisons

Pairwise sequence comparisonsQuery sequenceDatabaseNext Sequence sequence from from database databaseFound homologuesMethodsDiagonal plotsFASTABLASTLinköping University & Karolinska Institutet 34

Why comparisons?Identification of proteinSearch for homologies– giving functional/structural conclusionsMolecular architectureEvolutionLinköping University & Karolinska Institutet 35

Diagonal plots::10 A |* * * corresponding alignment:D | *E | *G | * * A K G C T G A E D A....G | * * A K G C S G G E D A....5 S |C | *G | * *K | *1 A |* * *+--------------------A K G C T G A E D A ....1 5 10Linköping University & Karolinska Institutet 36

Results from diagonal plot analysisLinköping University & Karolinska Institutet 37

Results from diagonal plot analysisLinköping University & Karolinska Institutet 38

Improved comparisons 1Not just considering identitiesbut alsosimilarities (based upon physicochemicalproperties or evolutionary likelihood)This is done by assigning weights fordifferent mismatches (substitution matrices)– A transition is more likely than a transversion– An Ile--Val exchange is more likely than an Ile--Arg change, since functional properties often arepreserved.Linköping University & Karolinska Institutet 39

Scoring matrixTo score matches and mismatches.A number is assigned to each position in thesequence depending on the match at that position.The scores for all positions in the alignment areadded to calculate a total score.This score is used to select the optimal alignmentamong alternative alignments.The simplest way of scoring is to assign 1 for amatch and 0 for a mismatch. Such a matrix isreferred to as a unitary matrix.Linköping University & Karolinska Institutet 40

Selection of scoring matrixThe scoring matrices contain information about thelikelihood of a certain change.Scoring matrices appear in all analysis involvingsequence comparison.The choice of matrix can strongly influence theoutcome of the analysis.Scoring matrices implicitly represent a particulartheory of evolution.Understanding theories underlying a given scoringmatrix can aid in making a proper choice.Linköping University & Karolinska Institutet 41

Scoring matrix (BLOSUM62)A R N D C Q E G H I L K M F P S T W Y VA 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4Linköping University & Karolinska Institutet 42

Using scoring matrixS 0 0 0 0 0=4C 0 0 0 =31 0G 0 0 =21 0 0K 0 1 0 0 0A 1 0 0 0 0A K G C T=4S 1 0 0 -1 1=12=11C 0 -3 -3 1 0=10G 0 -2 1 -3 0=9K -1 5 -2 -3 -1A 4 -1 0 0 0A K G C TLinköping University & Karolinska Institutet 43

PAMThe most important improvement achieved over theunitary matrix was based on evolutionary distances.Margaret O. Dayhoff pioneered this approach in the1970's.– An extensive study of the frequencies in which aminoacids substitute for each other during evolution.– The studies involved carefully aligning all of the proteinsin several families of proteins and then constructingphylogenetic trees for each family.– This lead to a table of relative frequencies with whichamino acids replace each other over an evolutionaryperiod. This table and the relative frequency of occurrenceof amino acids in the proteins studied were combined incomputing the PAM family of scoring matrices.Linköping University & Karolinska Institutet 44

PAM, cont.The PAM series are based on estimatedmutation rates (Percent Accepted Mutations)from closely related proteins and willtherefore be dominated by amino acidmutations caused by single base changes.It is also called a log-odds matrix, since thenumbers are proportional to the logarithm ofthe ”odds” for the replacement not being arandom change.Linköping University & Karolinska Institutet 45

PAM, cont.PAM1 stands for 1 accepted mutation per 100residues.PAM matrices for less similar sequences areobtained by extrapolations.The PAM100 matrix corresponds to 100 acceptedmutations per 100 residues, but since the sameresidue might change more than once, twosequences with this level of mutations will haveabout 50% identities.Similarly, the PAM250 matrix corresponds to a levelof about 20% identical residues.Linköping University & Karolinska Institutet 46

BLOSUMOne assumption inherent in the Dayhoff model isthat the evolutionary rates are uniform over thewhole of protein sequence.This is likely not to be true, because evolutionaryrates are lower in conserved regions and higher innon-conserved regions of proteins.Henikoff and Henikoff used the BLOCKS databaseto search for differences among sequences but onlyamong the very conserved regions of a protein family.Hence the term BLOSUM is fromBLOcks SUbstitution Matrix.Linköping University & Karolinska Institutet 47

BLOSUM, cont.They first collected all of the sequences in theBLOCKS database.Then for each one they sum the number ofamino acids at each site to get a frequencytable.The odds for relatedness were then calculatedin a similar way as for Dayhoff matrix.BLOSUM62 is derived from sequence blocksclustered at the 62% identity level.Linköping University & Karolinska Institutet 48

Improved comparisons 2Introduction of gapsA gap and its length are distinct quantities.– Different weights should be applied to each. Thereason for this is that it if a gap is introduced it ismuch more likely that it is prolonged than thatanother gap is formed.Linköping University & Karolinska Institutet 49

Introduction of gapsLinköping University & Karolinska Institutet 50

Gap penaltyThe score from all pairs of aligned residues arecombined with suitable penalties for introducinggaps to calculate a total score, which is used to selectthe optimal alignment.The gap penalty is normally determined by twoparameters:– one for opening of a gap and– one that gives a penalty proportional to the length of thegap.Most programs allow the user to choose theseparameters, which might have different optima fordifferent systems. They also depend on the scoringmatrix used.Linköping University & Karolinska Institutet 51

Improved comparisons 3A large part of the evolutionary process doeswork on the domain level, and not on thecomplete sequence level.– Therefore, unless two sequences are known to behomologous over their entire length, a localalignment is usually preferred to globalalignment.Linköping University & Karolinska Institutet 52

Lipman & PearsonFASTAIdentities / SimilaritiesScoring matrices– PAM250– BLOSUM62Best diagonal only– One alignmentLinköping University & Karolinska Institutet 53

FASTA run from the command linezierfandler: /home/bpn/test> fasta3FASTA searches a protein or DNA sequence data bankversion 3.0t77 April 29, 1997Please cite:W.R. Pearson & D.J. Lipman PNAS (1988) 85:2444-2448test sequence file name: query_sequence.fastaChoose sequence library:S: Swissprot completeG: Genpept completeP: PIREnter library filename (e.g. prot.lib), letter (e.g. P)or a % followed by a list of letters (e.g. %PN): sktup? (1 to 2) [2] 1query_sequence.fasta: 110 aa>INS_HUMANvs Swissprot complete librarysearching /zierfandler/data/swissprot/seq.dat 3 library..... ..... ..... ..... ..... ..... ..... ..... ..... .......... ..... ..... ..... ..... ..... ..... ..... ..... .......... ..... ..... ..... ..... ..... ..... ..... .....Done!Start programEnter querysequence nameEnter sequencelibraryEnter ktup valueLinköping University & Karolinska Institutet 54

Results from FASTA run, histogramopt E()< 20 182 0:===22 1 0:= one = represents 88 library sequences24 1 0:=26 8 1:*28 32 13:*30 84 81:*32 396 313:===*=34 1026 848:=========*==36 2019 1742:===================*===38 3168 2879:================================*===40 4160 4016:=============================================*==42 4809 4910:=======================================================*44 52445416:===========================================================*46 5035 5516:==========================================================*48 5090 5281:==========================================================*50 4607 4819:===================================================== *52 4171 4237:================================================*54 3481 3619:======================================== *56 3051 3023:==================================*58 2533 2482:============================*60 1995 2010:======================*62 1721 1612:==================*=64 1293 1282:==============*66 1026 1013:===========*68 840 797:=========*70 670 624:=======*72 527 488:=====*74 375 380:====*76 336 296:===*78 236 230:==*80 232 179:==*82 150 137:=*84 88 108:=*86 69 84:*88 70 65:* inset = represents 3 library sequences90 53 50:*92 34 39:* :============*94 23 30:* :======== *96 17 23:* :====== *98 17 18:* :=====*100 5 14:* :== *102 2 11:* := *104 4 8:* :==*106 5 6:* :=*108 4 5:* :=*110 3 4:* :=*112 3 3:* :*114 1 2:* :*116 0 2:* :*118 0 1:* :*>120 125 1:*= :*=======================================Linköping University & Karolinska Institutet 55

Results from FASTA run, hit list21210615 residues in 59022 sequencesstatistics extrapolated from 50000 to 58723 sequencesExpectation fit: rho(ln(x))= 4.8418+/-0.000523; mu= 9.7108+/- 0.029;mean_var=54.3033+/-10.602Kolmogorov-Smirnov statistic: 0.0170 (N=29) at 40FASTA (3.06 Sept, 1996) function (optimized, BL50 matrix) ktup: 1join: 42, opt: 30, gap-pen: -12/ -2, width: 32 reg.-scaledScan time: 54.450Enter filename for results: test1.resHow many scores would you like to see? [20]The best scores are:initn init1 opt z-sc E(58723)INS_HUMAN INSULIN PRECURSOR. ( 110) 765 765 765 1044.1 0INS_PANTR INSULIN PRECURSOR. ( 110) 756 756 756 1031.8 0INS_MACFA INSULIN PRECURSOR. ( 110) 750 750 750 1023.7 0INS_CERAE INSULIN PRECURSOR. ( 110) 745 745 745 1016.9 0INS_CANFA INSULIN PRECURSOR. ( 110) 676 676 676 923.3 0INS_AOTTR INSULIN PRECURSOR. ( 108) 489 489 658 899.0 0..INS_PIG PROINSULIN. ( 84) 296 296 495 679.4 2.9e-31INS_OCTDE INSULIN PRECURSOR.( 109) 414 238 477 653.3 8.2e-30INS_CHICK INSULIN PRECURSOR.( 107) 446 307 444 608.6 2.5e-27More scores? [0]Linköping University & Karolinska Institutet 56

Results from FASTA run, alignment>>INS_ONCKE INSULIN PRECURSOR.(105 aa)initn: 295 init1: 182 opt: 328 Z-score: 451.3 expect() 1.5e-18Smith-Waterman score: 328; 50.893% identity in 112 aa overlap10 20 30 40 50INS_HU MALWMRLLPLLALLALW-GPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAE::.:.. ::.:::: : : ::: :::::::::.::::::::.::::::: :...INS_ON MAFWLQAASLLVLLALSPGVDAAAA---QHLCGSHLVDALYLVCGEKGFFYTPK--RDVD10 20 30 40 5060 70 80 90 100 110INS_HU DLQVGQVELGGGPGAGSLQPLALEGSLQ-KRGIVEQCCTSICSLYQLENYCN: .: . .. . :. . .. ::::::::: . :....:.::::INS_ON PL-IGFLSPKSAK-ENEEYPFKDQTEMMVKRGIVEQCCHKPCNIFDLQNYCN60 70 80 90 100Linköping University & Karolinska Institutet 57

Alignment scoreThe significance of an alignment score can beestimated by repeating the alignment withrandomised sequences of the same aminoacid composition.A number of such alignments will give amean score and an estimate of the expectedvariation of the score for random sequencessimilar to the aligned sequences.From this, the obtained score for thealignment can be expressed as number ofstandard deviations above the mean.Linköping University & Karolinska Institutet 58

Caution with the interpretationsAn alignment demonstrates similarity, notnecessarily homology.– Homology is an evolutionary inference based onexamination of the similarity and its biologicalmeaning. Sequence similarity may result fromhomology but it may also result from chance oranalogy.Linköping University & Karolinska Institutet 59

The FASTA programLocal installation– program freely available via InternetPart of the GCG packageWWW service at EBI– http://www.ebi.ac.uk/fasta3/Linköping University & Karolinska Institutet 60

EBI home pageLinköping University & Karolinska Institutet 61

http://www.ebi.ac.uk/fastaLinköping University & Karolinska Institutet 62

http://www.ebi.ac.uk/fasta33/Linköping University & Karolinska Institutet 63

EBI FASTA submission form, cont.Linköping University & Karolinska Institutet 64

BLASTFast algorithmLocal installation possible– program available via InternetWWW services– http://www.ncbi.nlm.nih.gov/BLAST/Linköping University & Karolinska Institutet 65

BLAST programsProgram Query sequence Databaseblastp protein proteinblastn nucleotide nucleotideblastx nucleotide, translated 6 proteintblastn protein nucleotide, translated 6tblastx nucleotide, translated 6 nucleotide, translated 6Linköping University & Karolinska Institutet 66

NCBI home pageLinköping University & Karolinska Institutet 67

NCBI BLAST page,www.ncbi.nlm.nih.gov/BLAST/Linköping University & Karolinska Institutet 68

Basic BLASTLinköping University & Karolinska Institutet 69

Basic BLAST, cont.Linköping University & Karolinska Institutet 70

Basic BLAST, resultsLinköping University & Karolinska Institutet 71

Basic BLAST, results, cont.Linköping University & Karolinska Institutet 72

FASTA parametersScoring matrix– BLOSUM, PAM, ...Ktup– 1 or 2 for proteins, 1--6 for nucleotide sequences– Low number => high accuracy, long CPU timeGap penalties– Penalty to start a gap (points per gap)– Penalty to elongate a gap (points per residue)E-value (Expect value)– number of results to be expected by chance– values < 10 -6 ”safe”– dependent on search sequence, parameters and databaseLinköping University & Karolinska Institutet 73

Statistics, normal penalties (12, 2)Linköping University & Karolinska Institutet 74

Statistics, high penalties (48, 8)Linköping University & Karolinska Institutet 75

Statistics, low penalties (1.2, 0.2)Linköping University & Karolinska Institutet 76

Effect on alignment by gap penaltiesNormal penalties(12,2)High penalties(48,8)Linköping University & Karolinska Institutet 77

Multiple sequencealignments

Multiple sequence alignmentsCharacterisation of protein familiesImportant amino acid residuesConserved sequence motifsLinköping University & Karolinska Institutet 79

Alignment programsClustalW and ClustalX– freely available for DOS, Mac, linux, unix– WWW service: http://www.ebi.ac.uk/clustalw/T-CoffeeDialignKalignMafftMuscle….Linköping University & Karolinska Institutet 80

Clustering + AligningClustalAll-against-all pairwise comparisonsThe most similar sequence pair is aligned firstSubsequently, the sequence next in similarityorder is aligned to the first pairThis procedure is repeated for all sequences,creating a multiple sequence alignmentLinköping University & Karolinska Institutet 81

Clustal, cautionsA gap, introduced at the beginning, willpropagate through the alignmentSince Clustal makes a global alignment,manual intervention might be necessarywhen aligning sequences of large differencesin lengthLinköping University & Karolinska Institutet 82

ClustalW main menu********************************************************************** CLUSTAL W(1.5) Multiple Sequence Alignments **********************************************************************1. Sequence Input From Disc2. Multiple Alignments3. Profile Alignments4. Phylogenetic treesS. Execute a system commandH. HELPX. EXIT (leave program)Your choice: 1Sequences should all be in 1 file.6 formats accepted:NBRF/PIR, EMBL/SwissProt, Pearson (Fasta), GDE, Clustal, GCG/MSF.Enter the name of the sequence file: sequences.mfLinköping University & Karolinska Institutet 83

ClustalW multiple alignment menu****** MULTIPLE ALIGNMENT MENU ******1. Do complete multiple alignment now (Slow/Accurate)2. Produce guide tree file only3. Do alignment using old guide tree file4. Toggle Slow/Fast pairwise alignments = SLOW5. Pairwise alignment parameters6. Multiple alignment parameters7. Reset gaps between alignments? = ON8. Toggle screen display = ON9. Output format optionsS. Execute a system commandH. HELPor press [RETURN] to go back to main menuYour choice:Linköping University & Karolinska Institutet 84

Pairwise alignment parameters********* PAIRWISE ALIGNMENT PARAMETERS *********Slow/Accurate alignments:1. Gap Open Penalty :10.002. Gap Extension Penalty :0.103. Protein weight matrix :BLOSUM30Fast/Approximate alignments:4. Gap penalty :35. K-tuple (word) size :16. No. of top diagonals :57. Window size :58. Toggle Slow/Fast pairwise alignments = SLOWH. HELPEnter number (or [RETURN] to exit):Linköping University & Karolinska Institutet 85

Multiple alignment parameters********* MULTIPLE ALIGNMENT PARAMETERS *********1. Gap Opening Penalty :10.002. Gap Extension Penalty :0.053. Delay divergent sequences :40 %4. Toggle Transitions (DNA) :Weighted5. Protein weight matrix :BLOSUM series6. Use negative matrix :OFF7. Protein Gap ParametersH. HELPEnter number (or [RETURN] to exit):Linköping University & Karolinska Institutet 86

ClustalW at EBI, Submission formLinköping University & Karolinska Institutet 87

Clustal W, Submission form, cont.Linköping University & Karolinska Institutet 88

waiting ...Linköping University & Karolinska Institutet 89

ClustalW, ResultsLinköping University & Karolinska Institutet 90

ClustalW, Scores TableLinköping University & Karolinska Institutet 91

ClustalW, AlignmentLinköping University & Karolinska Institutet 92

ClustalW, Guide Tree & CladogramLinköping University & Karolinska Institutet 93

Alignment display using Jalview In Jalview, a numberof things can bechanged, e.g. font,character size, colour.Just use the pull-downmenus.Linköping University & Karolinska Institutet 94

JalViewA new experimental option has been added to theresults page which involved using a Java Appletcalled JalView.This is a fully featured MSA (multiple sequencealignment) editor which allows you not only to editthe alignment but also, to exchange the alignmentformats.Please note that JalView is under development.For documentation please click on the JalViewHyperlink.Linköping University & Karolinska Institutet 95

ClustalW, output fileLinköping University & Karolinska Institutet 96

Sequence formats in ClustalW Pearson (Fasta) GCG/MSF Clustal NBRF/PIR EMBL/SwissProt GDE RSF>title1SEQUENCE>title2NEXTSEQUENCELinköping University & Karolinska Institutet 97

Kalign at EBI, Submission formLinköping University & Karolinska Institutet 98

Kalign, AlignmentLinköping University & Karolinska Institutet 99

JalView, Kalign alignmentLinköping University & Karolinska Institutet 100

PSI-BLASTQuery sequenceFound homologuesDatabaseSequence profileMore homologuesLinköping University & Karolinska Institutet 101

PSI-BLASTPurpose of PSI-BLASTsearch:– to identify distant relatives– to gain insight into thefunction of the proteinfamily.Linköping University & Karolinska Institutet 102

PSI-BLAST, resultsLinköping University & Karolinska Institutet 103

PSI-BLASTTo start the next iteration,press this button.Linköping University & Karolinska Institutet 104

Expect valuesThe first of these (upper) sets the thresholdfor the initial BLAST search.– The default value is 10 as in the standard BLASTprogram.The second E value (lower) is the thresholdvalue for inclusion in the position-specificmatrix used for PSI-BLAST iterations.– The default setting of 0.001.Linköping University & Karolinska Institutet 105

Filter Low complexityIt is appropriate to filter most queries for lowcomplexity sequences.Some types of low complexity sequences may not bedetected by the filtering option in BLAST.For example, coiled-coil and transmembrane regionsneed to be detected using the appropriate programsoutside of BLAST.Since coiled-coil encoding sequences can lead tomatches with other coiled-coil proteins and thusobscure more meaningful hits, the user mightconsider manually masking the region to optimisethe sensitivity of the search.Linköping University & Karolinska Institutet 106

Advanced OptionsIn the "Advanced Options" field it is possibleto specify– gap costs– word size– and other parameters not otherwise selectable onthe query form.Output formatting options may also beadjusted here.– For example, the user might type: "-v150" to cause150 descriptions (rather than 100 or 250 availablethrough the pull-down menu) to be displayed.Linköping University & Karolinska Institutet 107

Set the output formatting options The default number of descriptions and alignmentsto be listed is 500. These variables affect the search in two ways:1. If the total number of hits in which E is less than thethreshold exceeds the number (x) of descriptionsrequested, only the top x most signficant would be listed;additional possibly significant alignments would not beshown, though these may embody importantinformation.2. The number of sequences used in generating themultiple alignment and the position specific matrix isspecified by the larger of the two (descriptions,alignments) variables. If at any point in the iterative PSI-BLAST process, significant sequences are omitted fromthe profile, all subsequent output will be affected.Linköping University & Karolinska Institutet 108

Set the output formatting optionsBy selecting a large number of descriptions(e.g. 250--500) it is possible to ensure that theE value and not the description limit will bethe determining factor in generating theprofile to be used for additional iterations.Reducing the output can then beaccomplished, if desired, by limiting thenumber of alignments to be reported.Linköping University & Karolinska Institutet 109

Alignment viewA variety of different alignment formats areavailable.– Pairwise alignmentgives a good view of the quality of an individualhit.– A flat query-anchored alignment (with identities)is a format in which identities shared bynumerous sequences can be easily spotted.Linköping University & Karolinska Institutet 110

Output from initial BLAST searchThe PSI-BLAST output is identical to the outputfrom a BLAST search.In a PSI-BLAST analysis, the results of the initialBLAST can be examined directly as for BLAST, butthey can also be used to generate a profile withwhich to perform additional searches of the samedatabase.The profile is generated automatically and isinvisible at the web interface.Although the profile is not shown, results of aprofile-directed search of the database are availableusing the "Run PSI-BLAST iteration" button.Linköping University & Karolinska Institutet 111

Examination of the PSI-BLAST resultsIn a PSI-BLAST search, the hits are dividedinto two categories.– Those that are better than the E value thresholdare listed first.– Those with E values worse than threshold, butnonetheless have an E value better than 1(selected on the query page) are listed furtherdown the page.– Hits with E values better than the threshold areused in forming he profile that will be used insubsequent PSI-BLAST iterations.Linköping University & Karolinska Institutet 112

Examination, cont.Sequences producing significant alignments:Score(bits) ValueEsp|Q57997|Y577_METJA PROTEIN MJ0577 >gi|2128018|pir||A64372 h... 314 2e-85pdb|1MJH| Structure-Based Assignment Of The Biochemical Fun... 272 1e-72The first and second hits are to the query sequence(MJ0577) itself. The second hit corresponds to thedatabase entry associated with determination of theMJ0577 structure.The score and E value for the structure entry aresomewhat worse because a certain number ofresidues were omitted from this database entrybecause they were disordered in the structure.Linköping University & Karolinska Institutet 113

Examination, cont.dbj|BAA29916| (AP000003) 170aa long hypothetical protein [Pyr... 107 6e-23sp|Q57951|Y531_METJA HYPOTHETICAL PROTEIN MJ0531 >gi|2128015|... 91 4e-18gi|2622094 (AE000872) conserved protein [Methanobacterium the... 85 4e-16gi|2621993 (AE000865) conserved protein [Methanobacterium the... 81 4e-15gi|2621194 (AE000803) conserved protein [Methanobacterium the... 80 7e-15gi|2622163 (AE000877) conserved protein [Methanobacterium the... 79 2e-14The next set of entries are to orthologous sequencesin two other Archaeal species, Pyrococcus horikoshiiand Methanobacterium thermoautotrophicum.Interestingly, the top hits also include sets ofparalogs in Methanococcus jannaschii andMethanobacterium thermoautotrophicum.Linköping University & Karolinska Institutet 114

Examination, cont.sp|P42297|YXIE_BACSU HYPOTHETICAL 15.9 KD PROTEIN IN BGLH-WAP... 76 1e-13sp|Q50777|YB54_METTM HYPOTHETICAL 16.1 KD PROTEIN IN MTR REGI... 66 2e-10gi|2648791 (AE000981) conserved hypothetical protein [Archaeo... 65 3e-10gi|2648610 (AE000970) conserved hypothetical protein [Archaeo... 64 5e-10gi|2983400 (AE000710) hypothetical protein [Aquifex aeolicus] 64 6e-10sp|P73475|YC30_SYNY3 HYPOTHETICAL 31.2 KD PROTEIN SLR1230 >gi... 63 1e-09gi|2983527 (AE000719) hypothetical protein [Aquifex aeolicus] 61 4e-09sp|O27222|YB54_METTH HYPOTHETICAL PROTEIN MTH1154 >gi|2622260... 59 2e-08dbj|BAA30650| (AP000006) 208aa long hypothetical protein [Pyr... 57 6e-08dbj|BAA31039.1| (AP000007) 149aa long hypothetical protein [P... 56 1e-07emb|CAB50594.1| (AJ248288) hypothetical protein [Pyrococcus a... 55 2e-07sp|P39177|UP12_ECOLI UNKNOWN PROTEIN FROM 2D-PAGE (SPOTS PR25... 55 2e-07sp|P74148|YD88_SYNY3 HYPOTHETICAL 17.3 KD PROTEIN SLL1388 >gi... 52 2e-06 These entries are to more distantly related Archaealsequences for the most part. Two sequences arebacterial.The scores and E values are respectable and most ofthe alignments extend the length of the query.Linköping University & Karolinska Institutet 115

First conclusions from PSI-BLASTThe output from the first stage PSI-BLAST search isidentical to the BLAST search results with theexception that in this query the E value thresholdwas set at 0.001.The results thus far have revealed that MJ0577 is onemember of a moderate size family of proteins thatare found in Archaea and in Eubacteria.To gain insight into the function of the MJ0577family of proteins, PSI-BLAST iterations may belaunched.The first iteration is initiated using the iterationbutton located at the end of the first result page.Linköping University & Karolinska Institutet 116

Performing PSI-BLAST iterationsThe power of PSI-BLAST is in its ability to generatea profile from the initial BLAST results and to usethat profile to re-search the same database.This procedure is initiated by the user upon clickingthe ”Run PSI-BLAST iteration” button.The number of iterations that should be performedwill depend on the goal of the experiment and thenature of the output.The investigator may find that useful newinformation is limited to the first iteration.When additional iterations yield no new hits, thesearch is said to have reached ”convergence”.Linköping University & Karolinska Institutet 117

Converged!The results of this iteration indicate that the searchhas converged.Convergence is the term used to describe theiteration in which no new hits have been identified.This is the logical end of a PSI-BLAST experiment,although iterations preceding the final one maycease to contain any new useful information.The remainder of the page looks like pages forprevious iterations, and includes a listing ofsignificant hits, hits that fall below the significancethreshold but are better than E=1 and alignments.Linköping University & Karolinska Institutet 118

Assignment 1Sequence comparisons– FASTA and BLAST– Protein level versus nucleotide level– Translation– Homology– Protein identificationhttp://www.ifm.liu.se/bioinfo/HL1008/http://bioinfo.limbo.ifm.liu.se/edu/HL1008/VT200Linköping University & Karolinska Institutet 119