intro protein function - motifs pp2 introfunc3 lecture ...€¦ · © burkhard rost (tum munich) 1...

© Burkhard Rost (TUM Munich) /001

title: Intro protein function - motifsshort title: pp2_introfunc3

lecture: Protein Prediction 2 - Protein function TUM winter 2013

Monday November 4, 2013


Announcements

Videos: SciVe / www.rostlab.orgTHANKS : Tim Karl + Jona ReebSpecial lectures:

• Oct 29: Tobias Hamp• Nov 21: Tanya Goldberg• Dec 19 + Jan 09: Andrea Schafferhans• Jan 28: Arthur Dong• Jan 21+23: Marco De Vivo/Marco Punta

No lecture:• Oct 10 Thu (Reformation)• Nov 12 Tue (Student assembly)• Dec 12 Thu (TUM Dies Academicus)

LAST lecture: Jan 30Examen: Feb 4 (likely this room)

• Makeup: Apr 9 - morning

CONTACT: Marlena Drabik [email protected]

2Monday November 4, 2013

http://www.rostlab.org

http://www.rostlab.org

mailto:[email protected]

mailto:[email protected]


Past - TOC today - Next

So far: Function introduction• Molecular biology is just at an exciting beginning• We can compute some aspects of molecular life• Most accurate inference of function: based on homology

Today• Motifs• Function by association

NEXT• localization



I.2b Function Intro:

Sequence motifs



Homology-based inference of function



Homology-based inference

6

P1 is a Cytochrome P450

P2 is sequence similar to P2 -> also a Cytochrome P450?

MELLQLWSALIILVVTYTISLLINQWRKPKPQGKFPPGPPKLPLIGHLHLLWGKLPQHALASVAKEYGPVAHVQLGEVFSVVLSSREATKEAMKLVDPACANRFESIGTRIMWYDNEDIIFSPYSEHWRQMRKICVSELLSSRNVRSFGFIRQDEVSRLLRHLRSSAGAAVDMTERIETLTCSIICRAAFGSVIRDNAELVGLVKDALSMASGFELADMFPSSKLLNLLCWNKSKLWRMRRRVDTILEAIVDEHKFKKSGEFGGEDIIDVLFRMQKDTQIKVPITTNSIKAFIFDTFSAGTETSSTTTLWVLAELMRNPAVMAKAQAEVRAALKEKTNWDVDDVQELKYMKSVVKETMRMHPPIPLIPRSCREECVVNGYTIPNKARIMINVWSMGRNPLYWEKPDTFWPERFDQVSKDFMGNDFEFVPFGAGRRICPGLNFGLANVEVPLAQLLYHFDWKLAEGMKPSDMDMSEAEGLTGILKNNLLLVPTPYDPSS

MELDLLSAIIILVATYIVSLLINQWRKSKSQQNLPPSPPKLPVIGHLHFLWGGLPQHVFRSIAQKYGPVAHVQLGEVYSVVLSSAEAAKQAMKVLDPNFADRFDGIGSRTMWYDKDDIIFSPYNDHWRQMRRICVTELLSPKNVRSFGYIRQEEIERLIRLLGSSGGAPVDVTEEVSKMSCVVVCRAAFGSVLKDQGSLAELVKESLALASGFELADLYPSSWLLNLLSLNKYRLQRMRRRLDHILDGFLEEHREKKSGEFGGEDIVDVLFRMQKGSDIKIPITSNCIKGFIFDTFSAGAETSSTTISWALSELMRNPAKMAKVQAEVREALKGKTVVDLSEVQELKYLRSVLKETLRLHPPFPLIPRQSREECEVNGYTIPAKTRIFINVWAIGRDPQYWEDPDTFRPERFDEVSRDFMGNDFEFIPFGAGRRICPGLHFGLANVEIPLAQLLYHFDWKLPQGMTDADLDMTETPGLSGPKKKNVCLVPTLYKSP



similar sequence

similar function

Important but: HANDLE WITH CARE!!

?

Annotation transfer



Target selection



what else 2 use?(in absence of

structure)



What could it be?

10

serine protease (ia46)trypsin domain of human prothrombinHistidine - Aspartate - Serine

CJA Sigrist, L Cerutti, N Hulo, A Gattiker, L Falquet, M Pani, Amos Bairoch & Philipp Bucher (2002) Briefings in Bioinformatics 3:265-274 (Fig. 1)



Motifs - intro



Sequence vs motif

12

Full sequence (ADH1_human, 95 aa): MANEVIKCKAAVAWEAGKPLSIEEIEVAPPKAHEVRIKIIATAVCHTDAY TLSGADPEGCFPVILGHEGAGIVESVGEGVTKLKAVWRMQILSKS

Motifs could be:

MANEVIKCKAA

Or:MAN[ED]hh[KR]C[KR]



How can we use this concept 2 search?

13

?Monday November 4, 2013


Resources for motifs/patterns

14

PROSITE: http://us.expasy.org/prosite/[Hulo et al. Nucl. Acids. Res. 32:D134-D137(2004)]

PRINTS:

http://www.bioinf.manchester.ac.uk/dbbrowser/PRINTS/[Attwood, Briefings in Bioinformatics, 3(3), 252-263 (2002)]

BLOCKS:

http://www.blocks.fhcrc.org/[Henikoff et al., Nucl. Acids Res. 28:228-230 (2000)]


http://www.blocks.fhcrc.org

http://www.blocks.fhcrc.org


PROSITE



Amos Bairoch

16

1986 starts SWISS-PROT1988 starts PROSITE1993 starts ExPasy (with Ron Appel)1998 SIB: Swiss Institute of Bioinformatics2009 CALIPHO Computer and Laboratory Investigation of Proteins of Human Origin

Amos Bairoch



Amos Bairoch

FILL IN papers asf!!!!papers: • >300 papers (Nov 2011)• 3 >1,000 citations (end 2011)• 72 over 100• H-index 83 (ISI Nov 2011)xother• x

17

Shapers and Shakers



Motifs and patterns

Manually align family + annotate motifsUse motifs for automatic alignment and annotation of unknown

18

Search for the motif pattern in a new protein

Find a motif or a pattern in a functionally characterized family

Transfer function annotation

© Marco Punta & Yanay Ofran & Burkhard Rost (Columbia New York)



PROSITE: Concepts for DB

completeness:DB as many motifs as possiblehigh specificity:no false positives at a level at which most are founddocumentationperiodic reviewing



PROSITE history

Bairoch A (1991) NAR 19 2241-5 PROSITE: a dictionary of sites and patterns in proteinsrepeated: 1992, 1993



PROSITE history


21

Search for the motif pattern in a new protein

Find a motif or a pattern in a functionally characterized family




PROSITE history


Solution:GxxGxxG (membrane)[RK](2)-x-[ST] (phosphorylation)

22

Search for the motif Find a motif or a pattern in a

Transfer function



PROSITE: Concepts for DB

completeness:DB as many motifs as possiblehigh specificity:no false positives at a level at which most are founddocumentationperiodic reviewing

23

Search for the motif Find a motif or a pattern in a

Transfer function



PROSITE history

Bairoch A (1991) NAR 19 2241-5 PROSITE: a dictionary of sites and patterns in proteinsrepeated: 1992, 1993A Bairoch & P Bucher (1994) NAR 22:3583-9PROSITE: recent developments (profiles)A Bairoch, P Bucher & K Hofmann (1996) NAR 24:189-96repeated 1997, 1999 (Hofmann, Bucher, Falquet, Bairoch)L Falquet, M Pani, P Bucher, N Hulo, CJ Sigrist, K Hofmann, & A Bairoch (2002) NAR 30:235-8

24

Philip Bucher



PROSITE history

CJ Sigrist, L Cerutti, N Hulo, A Gattiker, L Falquet, M Pagni, A Bairoch, P Bucher (2002) Brief Bioinform 3:265-74N Hulo, CJ Sigrist, V Le Saux, PS Langendijk-Genevaux, L Bordoli, A Gattiker, E De Castro, P Bucher, A Bairoch (2004) NAR 32:D134-7A Gattiker, E Gasteiger, A Bairoch (2002) Appl Bioinformatics 1:107-8ScanProsite: a reference implementation of a PROSITE scanning tool



PROSITE - evolution of methodA Bairoch (1991) NAR 19 Suppl: 2241-5, prev (1992) NAR 20 Suppl: 2013-8, x (1993) NAR 21: 3097-103, A Bairoch and P Bucher (1994) NAR 22: 3583-9, A Bairoch, P Bucher and K Hofmann (1996) NAR 24: 189-96, prev (1997) NAR 25: 217-21, K Hofmann, P Bucher, L Falquet and A Bairoch (1999) NAR 27: 215-9, L Falquet, M Pagni, P Bucher, N Hulo, CJ Sigrist, K Hofmann and A Bairoch (2002) NAR 30: 235-8, A Gattiker, E Gasteiger and A Bairoch (2002) Appl Bioinformatics 1: 107-8, CJ Sigrist, L Cerutti, N Hulo, A Gattiker, L Falquet, M Pagni, A Bairoch and P Bucher (2002) Brief Bioinform 3: 265-74, N Hulo, CJ Sigrist, V Le Saux, PS Langendijk-Genevaux, L Bordoli, A Gattiker, E De Castro, P Bucher and A Bairoch (2004) NAR 32: D134-7, CJ Sigrist, E De Castro, PS Langendijk-Genevaux, V Le Saux, A Bairoch and N Hulo (2005) Bioinformatics 21: 4060-6, E de Castro, CJ Sigrist, A Gattiker, V Bulliard, PS Langendijk-Genevaux, E Gasteiger, A Bairoch and N Hulo (2006) NAR 34: W362-5, N Hulo, A Bairoch, V Bulliard, L Cerutti, E De Castro, PS Langendijk-Genevaux, M Pagni and CJ Sigrist (2006) NAR 34: D227-30, N Hulo, A Bairoch, V Bulliard, L Cerutti, BA Cuche, E de Castro, C Lachaize, PS Langendijk-Genevaux and CJ Sigrist (2008) NAR 36: D245-9,



PROSITE / ScanProsite


K Hofmann, P Bucher, L Falquet & A Bairoch (1999) Nucl Acids Res 27: 215-9N Hulo et al. (2004) Nucleic Acids Res 32: D134-7Monday November 4, 2013


PRINTS



Terry K Attwood

29

University of Manchester (Faculty of Life Sciences & School of Computer Sciences)PRINTS: dignostic fingerprint databaseTK Attwood & ME Beck (1994) PRINTs-a protein motif fingerprint database

Terry K Attwood



PRINTS concept

Motifs are stretches of evolutionary conserved fingerprintsversion 42.0 (Manchester Univ, Feb 2012) 2,156 FINGERPRINTS encoding 12,444 single motifsTK Attwood, P Bradley, DR Flower, A Gaulton, N Maudling, A Mitchell, G Moulton, A Nordle, K Paine, P Taylor, A Uddin, C Zygouri (2003) NAR:31, 400-2



PRINTS: examplehomeoboxThe homeobox is a 60-residue motif first identified in a number of Drosophila homeotic and segmentation proteins, but now known to be well-conserved in many other animals, including vertebrates [1-3]. Proteins containing homeobox domains are likely to play an important role in development - most are known to be sequence-specific DNA-binding transcription factors. The domain binds DNA through a helix-turn-helix (HTH) structure.



BLOCKS



Jorja & Steven Henikoff

Fred Hutchinson Cancer Center, SeattleHHMI (Howard Hughes Medical Institute)papers: • >300 papers (Nov 2011)• 3 >1,000 citations (end 2011)• 72 over 100• H-index 83 (ISI Nov 2011)Paradigm changes• gene in gene - in intron (1986)• histones NOT only in octamers (2004) • DNA-methylation in histones: H2.AZ in histone spool promotes

gene expression (2008): NOT DNA-methylation shuts off genes (important for cancer drug development)

33

Shapers and Shakers



BLOSUM

compile log-odd ratios

BLOSUMn=threshold at n% pairwise sequence identity

S Henikoff & Jorja Henikoff (1992) PNAS 89:10915-9

34

Steven HenikoffMonday November 4, 2013


BLOSUM

BLOcks of amino acid SUbstitution MatricesAlign only conserved regions

JG Henikoff and S Henikoff (1996) Meth Enzymology 266: 88-104 S Pietrokovski, JG Henikoff & S Henikoff (1996) NAR 24: 197-201



BLOCKS

idea taken from multiple alignments



BLOCKS: length distribution

37J Liu & B Rost (2003) Current Opinion in Chemical Biology 7, 5-11



Pfam



Alex Bateman

39

classify all proteins and RNA into families to better understand their function and evolution 1997 starts Pfam (Protein families)2003 Rfam (RNA-families)

Citation giant:• 229 papers (Nov 2011)• 1 with >8,800 citations (Nov 2011)• 6 with >1,000 citations (11/2011)• 32 with > 100 citations (11/2011)• Hirsh index: 48

Shapers and Shakers



Pfam: Protein families

40

EL Sonnhammer, SR Eddy, R Durbin (1997) Pfam: a comprehensive database of protein families based on seed alignments. Proteins 28:405-20EL Sonnhammer, SR Eddy, E Birney, A Bateman, R Durbin (1998) NAR 26:320-2A Bateman, E Birney, R Durbin, SR Eddy, RD Finn, EL Sonnhammer (1999) Pfam 3.1: 1313 multiple alignments and profile HMMs match the majority of proteins. NAR 27:260-2SJ Sammut, RD Finn, A Bateman (2008) Pfam 10 years on: 10,000 families and still growing. Brief. Bioinform 9:210-9



Pfam: how its done

41

manual alignment



Pfam - current stats

version/families/



Pfam-7TM

A Bateman, et al. (2004) Nucleic Acids Res 32: D138-41© Marco Punta & Yanay Ofran & Burkhard Rost (Columbia New York)



Clusters & FamiliesDB/Method Version Latest

UpdateEntries Update URL (all begin with http://)

Short sequence motifsPROSITE 17.23 10/2002 1573 manual www.expasy.ch/prosite/Blocks+ 8/2001 8656 manual blocks.fhcrc.org/blocks/PRINTS 35.0 7/2002 1750 manual www.bioinf.man.ac.uk/dbbrowser/PRINTS/

Structural domain-like regions

Pfam-A 7.6 9/2002 4463 manual pfam.wustl.eduTIGRFAM 2.1 9/2002 1622 manual www.tigr.org/TIGRFAMs/SMART 3.4 10/2002 654 manual smart.embl-heidelberg.deSBASE 9.0 10/2002 483 semi-

manualhydra.icgeb.trieste.it/~kristian/SBASE/

DOMO 2.0 4/1998 automatic www.infobiogen.fr/services/domo/ProDom 2001.3 12/2001 automatic prodes.toulouse.inra.fr/prodom/doc/prodom.htmGeneRAGE automatic www.ebi.ac.uk/research/cgg/services/rage/TribeMCL automatic www.ebi.ac.uk/research/cgg/tribe/CHOP 10/2002 automatic cubic.bioc.columbia.edu/db/chop/

Integration

InterPro 5.2 9/2002 5875 N/A www.ebi.ac.uk/interpro/MetaFam 4.1 9/2002 N/A metafam.ahc.umn.edu

Clusters of proteins

CluSTr automatic www.ebi.ac.uk/clustr/SYSTERS 3.0 automatic systers.molgen.mpg.dePICASSO 0 3/1998 automatic systers.molgen.mpg.deProtoNet 1.4 9/2002 automatic www.protonet.cs.huji.ac.il/protonet/ProClust 1.0 automatic promoter.mi.uni-koeln.de/~proclust/

J Liu & B Rost (2003) Cur Op Chem Biol 7, 5-11Monday November 4, 2013


Some overlap between databases

45J Liu & B Rost (2003) Cur Op Chem Biol 7, 5-11Monday November 4, 2013


… not everything that shines is copper

J Liu & B Rost (2003) Cur Op Chem Biol 7, 5-11Monday November 4, 2013


localization motifs



motif-based inference of localization



Rajesh Nairnow: FDA,

Waschington

49

Rajesh Nair



Similar proteins may differ in localization

R Nair & B Rost (2002) Protein Science 11: 2836-47 50Monday November 4, 2013


Shuttle into the nucleus

CYTOPLASM

NUCLEUS

NLS M9

Transportin Importin

Nucleus

Cytoplasm

51M Cokol, R Nair & B Rost (2000) EMBO Rep 1: 411-415Monday November 4, 2013


Types of zip-codes

following:B Alberts, D Bray, J Lewis, M Raff, K Roberts, JD Watson: The Cell, Garland, 1994



How many NLS motifs in databases?

ONE in PROSITEbi-partite motif

Set A N NLS B Nprot nuc C Nfam nuc D Accuracy E

Coverage F

PROSITE 1 96 31 90 % 3 %SWISS-PROT 322 290 n.a. 9 %

NLS-lit cleaned 91 309 35 100 % 10 %NLS-lit consensus 91 537 35 100 % 17 %PredictNLS_DB 214 1354 186 100 % 43 %

Coverage



Experimental NLS: positive chargesNLS Protein Reference

RKRKK YstDNApolalpha Hsieh et al., 1998RKRRR Amida Irie et al., 2000KKKKRKREK LEF-1 Prieve et al., 1998KKKRRSREK TCF-1 Prieve et al.,. 1998RQARRNRRRRWR HIV-1 Rev Truant et al., 1999RRMKWKK PDX-1 Moede et al., 1999PKKKRKV SV40 LrgT Kalderon et al., 1984PRRRK SRY Sudbeck and Scherer, 1997GKKRSKA H2B Moreland et al., 1987KAKRQR v-Rel Gilmore and Temin, 1988RGRRRRQR Amida Irie et al., 2000PPVKRERTS RanBP3 Welch et al., 1999PYLNKRKGKP Pho4p Welch et al., 1999KRx{7,9}PQPKKKP p53-NLS1 Liang and Clarke, 1999KVTKRKHDNEGSGSKRPK Hum-Ku70 Koike et al., 1999RLKKLKCSKx{19}KTKR GAL4 Chan et al., 1998RKRIREDRKx{18}RKRKR TCPTP Chan et al., 1998RRERx{4}RPRKIPR BDV-P Schwemmle et al., 1999KKKKKEEEGEGKKK act/inh betaA Blauer et al., 1999PRPRKIPR BDV-P Shoya et al., 1998PPRIYPQLPSAPT BDV-P Shoya et al., 1998KDCVINKHHRNRCQYCRLQR TR2 Yu et al., 1998APKRKSGVSKC PolyomaVP1 Chang et al., 1992RKKRRQRRR HIV-1 Tat Truant et al., 1999MPKTRRRPRRSQRKRPPT Rex Palmeri and Malim, 1999KRPMNAFIVWSRDQRRK SRY Sudbeck and Scherer, 1997KRPMNAFMVWAQAARRK SOX9 Sudbeck and Scherer, 1997PPRKKRTVV NS5A Ide et al., 1996YKRPCKRSFIRFI DNAse EBV Liu et al., 1998LKDVRKRKLGPGH DNAse EBV Lyons et al., 1987KRPRP AdenovE1a Bouvier and Baldacci, 1995RRSMKRK hVDR Vihinen-Ranta et al., 1997PAKRARRGYK CPV capsid Kaneko et al., 1997RKCLQAGMNLEARKTKK hGlu.cort. Kaneko et al., 1997RRERNKMAAAKCRNRRR CFOS Kaneko et al., 1997KRMRNRIAASKCRKRKL CJUN Kaneko et al., 1997



Experimental NLS: more complicated

NLS Protein Reference

CYGSKNTGAKKRKIDDA DNAhelicaseQ1 Miyamoto et al., 1997

[AKR]TPIQKHWRPTVLTEGPPVKIRIETGEWE[KA] ASVintegrase Kukolj G. 1998

GGGx{3}KNRRx{6}RGGRN Nab2 Truant et al., 1998

KRxxxxxxxxxKTKK THOV NP Weber et al., 1998

EYLSRKGKLEL VirD2-Nterm Tinland et al., 1992KRPACTLKPECVQQLLVCSQEAKK HCDA Somasekaram et al., 1999

RVHPYQR QKI-5 Wu et al., 1999HARNT Eguchi et al., 1997YNNQSSNFGPMKGGN M9 Bonifaci et al., 1997

SxGTKRSYxxM InfluenzaNP Wang et al., 1997TKRSxxxM InfluenzaNP Wang et al., 1997VNEAFETLKRC MyoD Vandromme et al., 1995

MNKIPIKDLLNPG Mat-alpha Hall et al., 1984



In silico mutagenisis



Increasing accuracy and coverage

57


Coverage F



Coverage



Increasing accuracy and coverage

58


Coverage F



Coverage



Types of zip-codes



Sarah Gilman

60

Kaz Wrzeszczynski



ER

&

Golgi retention

signals

Sequence motif 1 ER/Golgi Non-ER/GolgiN % N %

Endoplasmic reticulum (ER) motifs 2

KDEL-C-term 56 92 5 8KDEL 61 7 714 92HDEL-C-term 45 92 4 8HDEL 46 15 269 2HDEF-C-term 2 50 2 50HDEF 2 2 89 98

Golgi apparatus motifs 3

YQRL 3 1 270 99YKGL 5 1 442 99YHPL 4 5 76 95YXXZ 477 1 83112 99NPFKD 0 0 14 100FXFXD 31 1 3169 99FQFND 1 25 3 75PXPXP 65 1 8477 99X 479 1 80461 99GRIP-motif 5 1 50 1 50GRIP-motif (shortened) 6 1 3 28 97

C-term variations 4PROSITE Pattern 7 134 77 39 23{KH}DEL 86 78 5 4{KHR}{DENQ}EL 125 80 32 20{KHR}{DENQ}L 125 71 49 29{KHRDENQAS}{DENQIYCV}{DENQ}L 156 25 477 75{KRDEAVYF}{KRDEVYFMQ}{KHED}{DK}EL 39 89 5 11

KO Wrzeszczynski & B Rost (2004) CMLS 61: 1341-53Monday November 4, 2013


Open challenges - motifs and patterns

Automate

Unify

Remote homologues




Identify active site / functional element

Search for this structural pattern in a new protein


S Jones & J Thornton (2004) Curr Opin Struc Biol 8:3-7

Structural motifs

Manual identification of active site Automatic structural alignment?




Open challenges - structural motifs

Find

Search

Add biophysics of the site to the spatial search




Example 3:Voltage-gated

potassium channel



Example: Voltage-gated potassium channel

71

V Ruta et al. & R MacKinnon (2003) Nature, 422:180-5

• Eukaryotic voltage-gated potassium channel (VG-K+) • Prokaryotic membrane proteins are easier to crystallize than eukaryotic ones

• find a prokaryotic VG-K+ having functional and structural features similar to the eukaryotic one

© Marco PuntaMonday November 4, 2013


Voltage-gated K+ channel: sequence

72

1MAAVAGLYGLGEDRQHRKKQQQQQQHQKEQLEQKEEQKKIAERKLQLREQQLQRNSLDGY

GSLPKLSSQDEEGGAGHGFGGGPQHFEPIPHDHDFCERVVINVSGLRFETQLRTLNQFPD

TLLGDPARRLRYFDPLRNEYFFDRSRPSFDAILYYYQSGGRLRRPVNVPLDVFSEEIKFY

ELGDQAINKFREDEGFIKEEERPLPDNEKQRKVWLLFEYPESSQAARVVAIISVFVILLS

IVIFCLETLPEFKHYKVFNTTTNGTKIEEDEVPDITDPFFLIETLCIIWFTFELTVRFLA

CPNKLNFCRDVMNVIDIIAIIPYFITLATVVAEEEDTLNLPKAPVSPQDKSSNQAMSLAI

LRVIRLVRVFRIFKLSRHSKGLQILGRTLKASMRELGLLIFFLFIGVVLFSSAVYFAEAG

SENSFFKSIPDAFWWAVVTMTTVGYGDMTPVGVWGKIVGSLCAIAGVLTIALPVPVIVSN

FNYFYHRETDQEEMQSQNFNHVTSCPYLPGTLGQHMKKSSLSESSSDMMDLDDGVESTPG

LTETHPGRSAVAPFLGAQQQQQQQPVASSLSMSIDKQLQHPLQHVTQTQLYQQQQQQQQQ

QQNGFKQQQQQTQQQLQQQQSHTINASAAAATSGSGSSGLTMRHNNALAVSIETDV

The template: voltage gated potassium channel from Shaker



Voltage-gated K+ channel: search

73PSI-BLAST: http://www.ncbi.nih.gov/BLAST/ © Marco PuntaMonday November 4, 2013


Voltage-gated K+ channel: alignment

74

Shaker: 413 AVYFAEAGSENSFFKSIPDAFWWAVVTMTTVGYGDMTPVGVWGKIVGSLCAIAGVLTIAL 472 A+Y E NS KS+ DA WWAVVT TTVGYGD+ P GK++G + G+ + LTarget: 150 AIYIVEYPDPNSSIKSVFDALWWAVVTATTVGYGDVVPATPIGKVIGIAVMLTGISALTL 209

Shaker: 473 PVPVIVSNF 481 + + + FTarget: 210 LIGTVSNMF 218

the alignment




74



the alignment

~ 30% SI and 80 aligned residues



Voltage-gated K+ channel: filter

75© Marco PuntaMonday November 4, 2013



76



the alignment

Target :

295

1

the entire sequence of the identified protein

MSVERWVFPGCSVMARFRRGLSDLGGRVRNIGDVMEHPLVELGVSYAALLSVIVVVVEYT

MQLSGEYLVRLYLVDLILVIILWADYAYRAYKSGDPAGYVKKTLYEIPALVPAGLLALIE

GHLAGLGLFRLVRLLRFLRILLIISRGSKFLSAIADAADKIRFYHLFGAVMLTVLYGAFA

IYIVEYPDPNSSIKSVFDALWWAVVTATTVGYGDVVPATPIGKVIGIAVMLTGISALTLL

IGTVSNMFQKILVGEPEPSCSPAKLAEMVSSMSEEEFEEFVRTLKNLRRLENSMK



Voltage-gated K+ channel: function?

77

Shaker channel

• Membrane protein



Voltage-gated K+ channel:

78

Out

In

α-bundle β-barrel



Voltage-gated K+ channel: TMH predicted

79

1 MAAVAGLYGLGEDRQHRKKQQQQQQHQKEQLEQKEEQKKIAERKLQLREQQLQRNSLDGY











S1

S2

S3

S4 S5

P S6



Voltage-gated K+ channel: TMH predicted

80

Side Viewsingle subunit

Top Viewtetramer



Programs available for membrane helices

81

PHDpsihtm: http://www.embl-heidelberg.de/predictprotein/predictprotein.html

DAS: http://www.sbc.su.se/~miklos/DAS/maindas.html

HMMTOP2: http://www.enzim.hu/hmmtop/

SOSUI: http://sosui.proteome.bio.tuat.ac.jp/sosuiframe0.html TMHMM2: http://www.cbs.dtu.dk/services/TMHMM/



Voltage-gated K+ channel: TMHs predicted

82

MSVERWVFPGCSVMARFRRGLSDLGGRVRNIGDVMEHPLVELGVSYAALLSVIVVVVEYT

MQLSGEYLVRLYLVDLILVIILWADYAYRAYKSGDPAGYVKKTLYEIPALVPAGLLALIE

GHLAGLGLFRLVRLLRFLRILLIISRGSKFLSAIADAADKIRFYHLFGAVMLTVLYGAFA

IYIVEYPDPNSSIKSVFDALWWAVVTATTVGYGDVVPATPIGKVIGIAVMLTGISALTLL

IGTVSNMFQKILVGEPEPSCSPAKLAEMVSSMSEEEFEEFVRTLKNLRRLENSMK

S1

S2 S3

S4 S5

P S6

TMHs predictions on the target sequence



Voltage-gated K+ channel: function of template

83

Shaker channel


• K+ selectivity




84

Out

In + -

-

++ -

-

+



Voltage-gated K+ channel: selectivity motif

85












S1

S2

S3

S4 S5

P S6

656

TxxTxGxG



Voltage-gated K+ channel: conservation of outer pore

86



P S6

the selectivity filter

S5 S6

P

S4S3S2S1T

Gx

xG

x xT



Voltage-gated K+ channel: functional characterization of target

87

Shaker channel


• K+ selectivity



Voltage-gated K+ channel: functional characterization of target

88

Shaker channel


• K+ selectivity

• Voltage gating



Voltage-gated K+ channel: residues related with gating

91












S1

S2

S3

S4 S5

P S6

656



Voltage-gated K+ channel: Voltage sensor

92












S1

S2

S3

S4 S5

P S6

RxxRxxRxxR



Voltage-gated K+ channel: Other voltage sensing residues

93












S1

S2

S3

S4 S5

P S6

656

DE E R



Voltage-gated K+ channel: Gating hinge glycine

94












S1

S2

S3

S4 S5

P S6G



Voltage-gated K+ channel: Conservation of functional residues in target

95

S5 S6

P

S4S3S2S1

Shaker: 413 AVYFAEAGSENSFFKSIPDAFWWAVVTMTTVGYGDMTPVGVWGKIVGSLCAIAGVLTIAL 472 A+Y E NS KS+ DA WWAVVT TTVGYGD+ P GK++G + G+ + LSbjct : 150 AIYIVEYPDPNSSIKSVFDALWWAVVTATTVGYGDVVPATPIGKVIGIAVMLTGISALTL 209

Shaker: 473 PVPVIVSNF 481 + + + FSbjct : 210 LIGTVSNMF 218

P S6

the gating hinge




96

S5 S6

P

S4S3S2S1

Shaker: 413 AVYFAEAGSENSFFKSIPDAFWWAVVTMTTVGYGDMTPVGVWGKIVGSLCAIAGVLTIAL 472 A+Y E NS KS+ DA WWAVVT TTVGYGD+ P GK++G + G+ + LSbjct : 150 AIYIVEYPDPNSSIKSVFDALWWAVVTATTVGYGDVVPATPIGKVIGIAVMLTGISALTL 209

Shaker: 473 PVPVIVSNF 481 + + + FSbjct : 210 LIGTVSNMF 218

P S6

the gating hinge




97

S5 S6

P

S3S2S1



P S6

+

+++

S4

voltage sensor




98

PLPDNEKQRKVWLLFEYPESSQAARVVAIISVFVILLSIVIFCLETL MSVERWVFPGCSVMARFRRGLSDLGGRVRNIGDVMEHPLVELGVSYAALLSVIVVVVEYT

PEFKHYKVF≠PFFLIETLCIIWFTFELTVRFLACPNKLNFC≠VMNVIDIIAIIPYFITLAAMQLSGEYLV≠RLYLVDLILVIILWADYAYRAYKSGDPAGYV≠KKTLYEIPALVPAGLLALI

IIPYFIT≠ILRVIRLVRVFRIFKLSRHSKGLQILGRTLKAMRELGLLIFFLFIGVVLFSSAEGHLAGL≠LFRLVRLLRFLRILLIISRGSKFLSAIADAADKIRFYHLFGAVMLTVLYGAFA

VYFAEAGSENSFFKSIPDAFWWAVVTMTTVGYGDMTPVGVWGKIVGSLCAIAGVLTIALPVIYIVEYPDPNSSIKSVFDALWWAVVTATTVGYGDVVPATPIGKVIGIAVMLTGISALTLLI

PVIVSNFNYFYHGTVSNMFQKILVGEPEPSCSPAKLAEMVSSMSEEEFEEFVRTLKNLRRLENSMK

S1

S2 S3

S4 S5

P S6

voltage sensor




99






S1

S2 S3

S4 S5

P S6

voltage sensor




100

S5 S6

P

S3S2S1



P S6

S4

other voltage sensing residues




Conservation of functional residues in target

101






S1

S2 S3

S4 S5

P S6

voltage sensor sensitive residues



Voltage-gated K+ channel: Function of target

102

Shaker channel


• K+ selectivity

• Voltage gating



MacKinnon’s nobel



I.2c Function Intro:

Function by association



Co-expression

Expression data Machine Learning / Clustering Functional classes

For example: P Brown et al. (2000) PNAS 97:262-267© Marco Punta & Yanay Ofran & Burkhard Rost (Columbia New York)



Interactions / networks

For example: AH Tong et al. (2002) Science 295: 321-324© Marco Punta & Yanay Ofran & Burkhard Rost (Columbia New York)


© Burkhard Rost (TUM Munich) /00A Bairoch (2000) Nucleic Acid Res 28:304-305

Differentiate functional and physical interaction

Improve accuracy and coverage (data, algorithm)

Ab initio/de novo prediction




Predict aspects of function

Sub-cellular localization (nucleus, membrane,

etc.)

Post-translational modifications

Functionally important residues

Interaction sites




Conclusions today

Function introduction• Molecular biology is just at an exciting beginning• We can compute some aspects of molecular life• Most accurate inference of function: based on homology• Homology-based inference of function can be improved by

motifsproblem: definition of motifs still not fully automated

NEXT• Prediction of subcellular localization



Lecture plan (PP2 function)01: 2013/10/15: no lecture02: 2013/10/17: welcome: who we are03: 2013/10/22: Intro - function 1: concepts04: 2013/10/24: Intro - function 2: homology05: 2013/10/29: Tobias Hamp: Homology-based prediction of function06: 2013/10/31: no lecture Reformation07: 2013/11/05: Intro - function 2: inference08: 2013/11/07: Intro - function 3: motifs09: 2013/11/12: no lecture: SVV (student reps)10: 2013/11/14: Localization 111: 2013/11/19: Localization 212: 2013/11/21: Localization 3 - Tatyana Goldberg13: 2013/11/26: SNP effect 114: 2013/11/28: SNP effect 215: 2013/12/03: SNP effect 316: 2013/12/05: Protein-protein interaction 117: 2013/12/10: Exercise/Presentations18: 2013/12/12: no lecture: Dies Academicus / Protein-protein interaction 119: 2013/12/17: Protein-protein interaction 220: 2013/12/19: Andrea Schafferhans: 3D function prediction21-24: no lectures - winter break (2013/12/23 - 2014/01/06)25: 2014/01/07: Protein-protein interaction 326: 2014/01/09: Andrea Schafferhans: Docking 27: 2014/01/14: Protein-DNA interaction 128: 2014/01/16: Protein-DNA/RNA interaction 229: 2014/01/21: Marco De Vivo (ISS Genoa)30: 2014/01/23: Marco Punta (Pfam)31: 2014/01/28: Arthur Dong: networks32: 2014/01/30: wrap-up33: 2014/02/04: examen34: 2014/02/06: no lecture



Lecture plan (PP2 function-should!)01: 2013/10/15: no lecture02: 2013/10/17: welcome: who we are03: 2013/10/22: Intro - function 1: concepts04: 2013/10/24: Intro - function 2: homology05: 2013/10/29: Tobias Hamp: Homology-based prediction of function06: 2013/10/31: no lecture Reformation07: 2013/11/05: Intro - function 3: motifs08: 2013/11/07: Localization 109: 2013/11/12: no lecture: SVV (student reps)10: 2013/11/14: Localization 211: 2013/11/19: Individualized medicine12: 2013/11/21: Localization 3 - Tatyana Goldberg13: 2013/11/26: SNP effect 114: 2013/11/28: SNP effect 215: 2013/12/03: SNP effect 316: 2013/12/05: Protein-protein interaction 117: 2013/12/10: Exercise/Presentations18: 2013/12/12: no lecture: Dies Academicus / Protein-protein interaction 119: 2013/12/17: Protein-protein interaction 220: 2013/12/19: Andrea Schafferhans: 3D function prediction21-24: no lectures - winter break (2013/12/23 - 2014/01/06)25: 2014/01/07: Protein-protein interaction 326: 2014/01/09: Andrea Schafferhans: Docking 27: 2014/01/14: Protein-DNA interaction 128: 2014/01/16: Protein-DNA/RNA interaction 229: 2014/01/21: Marco De Vivo (ISS Genoa)30: 2014/01/23: Marco Punta (Pfam)31: 2014/01/28: Arthur Dong: networks32: 2014/01/30: wrap-up33: 2014/02/04: examen34: 2014/02/06: no lecture

111

should have


intro protein function - motifs pp2 introfunc3 lecture ...€¦ · © burkhard rost (tum munich) 1...

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