Bioinformatics approaches for

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Bioinformatics approaches for

• Teresa K Attwood
• Faculty of Life Sciences School of Computer
  Science
• University of Manchester, Oxford Road
• Manchester M13 9PT, UK
• http//www.bioinf.man.ac.uk/dbbrowser/

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.analysing GPCRs.
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.which craft is best?
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Overview

• What are GPCRs?
• why theyre interesting important
• why bioinformatics approaches are important
• In silico function prediction
• a reality check
• Family-based methods for characterising GPCRs

• Understanding the tools
• problems with pair-wise family-based approaches
• estimating (biological) significance
• Seeking deeper functional insights
• Conclusions

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What are GPCRs?G protein-coupled receptors

• A functionally diverse family of cell-surface 7TM
  proteins
• Functional diversity achieved via
• interaction with a variety of ligands
• stimulation of various intracellular pathways via
  coupling to different G proteins

GDP
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Why are GPCRs interesting?Attwood, TK Flower,
DR (2002) Trawling the genome for G
protein-coupled receptors the importance of
integrating bioinformatic approaches. In Drug
Design Cutting Edge Approaches, pp.60-71.

• They are ubiquitous
• gt800 GPCR genes in the human genome, from 3 major
  superfamilies
• rhodopsin-, secretin- metabotropic glutamate
  receptor-like
• Share almost no sequence similarity
• but are united by common 7TM architecture
• Constitute a complex multi-gene family
• populated by gt50 families gt350 subtypes

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Isnt just stamp collecting!Attwood, TK
Flower, DR (2002) Trawling the genome for G
protein-coupled receptors the importance of
integrating bioinformatic approaches. In Drug
Design Cutting Edge Approaches, pp.60-71.

• GPCRs are of profound biomedical importance
• targets for gt50 of prescription drugs
• yield sales gt16 billion/annum
• theyre big business!
• Given their importance, we need to
• characterise the ones we know about
• identify new ones
• discover what they do!
• e.g., as potential new drug targets

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Why studying GPCRs is difficult

• Only 2 crystal structures available
• bovine rhodopsin (2000) human ?2-adrenergic
  receptor (2007)
• Many GPCRs havent been characterised
  experimentally
• remain 'orphans, with unknown ligand specificity
• With gt800 human GPCRs, this isnt much to go on!

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Why use bioinformatics approaches?

• Computational approaches are important
• can be used to help identify, characterise
  model novel receptors
• usually by similarity extrapolation of known
  characteristics
• Bioinformatics thus offers complementary tools
  for elucidating the structures functions of
  receptors
• But the task is non-trivial
• GPCRs exhibit rich relationships complex
  molecular interactions
• present many challenges for in silico analysis
• in trying to derive meaningful functional
  insights, traditional methods are likely to be
  limited

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Weve been using biology-unaware search tools to
analyse such complex systems
How far can we truly expect to understand
cellular function with such naïve approaches?
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In silico function predictiona reality check

• What is the function of this structure?

• What is the function of this sequence?

• What is the function of this motif?
• the fold provides a scaffold, which can be
  decorated in different ways by different
  sequences to confer different functions - knowing
  the fold function allows us to rationalise how
  the structure effects its function at the
  molecular level

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A test case for structural genomics
Structure-based assignment of the biochemical
function of hypothetical protein mj0577
(Zarembinski et al., PNAS 95 1998)
Although the structure co-crystallised with ATP,
the biochemical function of the protein is
unknown
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What's in a sequence?
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Methods for family analysis Attwood, TK (2000).
The quest to deduce protein function from
sequence the role of pattern databases. Int.J.
Biochem. Cell Biol., 32(2), 139155.
Fuzzy regex (eMOTIF)
Single motif methods
Exact regex (PROSITE)
Full domain alignment methods
Profiles (Profile Library)
HMMs (Pfam)
Identity matrices (PRINTS)
Multiple motif methods
Weight matrices (Blocks)
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The challenge of family analysis

• highly divergent family with single function?
• superfamily with many diverse functional
  families?
• must distinguish if function analysis done in
  silico
• a tough challenge!

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In the beginning was PROSITE
TM domain
GSTALIVMYWC-GSTANCPDE-EDPKRH-X(2)-LIVMNQGA
-X(2)-LIVMFT-GSTANC-LIVMFYWSTAC-DENH-R
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Diagnostic limitations of PROSITE

• ID G_PROTEIN_RECEP_F1_1 PATTERN.
• AC PS00237
• DT APR-1990 (CREATED) NOV-1997 (DATA UPDATE)
  SEP-2004 (INFO UPDATE).
• DE G-protein coupled receptors family 1
  signature.
• PA GSTALIVMFYWC-GSTANCPDE-EDPKRH-x(2)-LIVMN
  QGA-x(2)-LIVMFT-
• PA GSTANC-LIVMFYWSTAC-DENH-R-FYWCSH-x(2)-
  LIVM.
• NR /RELEASE44.6,159201
• NR /TOTAL1622(1621) /POSITIVE1530(1529)
  /UNKNOWN0(0)
• NR /FALSE_POS92(92) /FALSE_NEG261
  /PARTIAL61

• This represents an apparent 22 error rate
• the actual rate is probably higher
• Thus, a match to a pattern is not necessarily
  true
• a mis-match is not necessarily false!
• False-negatives are a fundamental limitation to
  this type of pattern matching
• if you don't know what you're looking for, you'll
  never know you missed it!

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Where do motifs (fingerprints) fit in?
(fingerprints are hierarchical)
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Rhodopsin-like superfamily, family subtype
GPCRs in PRINTS Attwood, TK (2001) A compendium
of specific motifs for diagnosing GPCR subtypes.
TiPS, 22(4), 162-165.
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Searching PRINTS - FingerPRINTScan Scordis, P,
Flower, DR Attwood, TK (1999) FingerPRINTScan
intelligent searching of the PRINTS motif
database. Bioinformatics, 15, 523-524.

• GPCR fingerprints are embedded in PRINTS
• allows diagnosis of GPCR mosaics

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Visualising fingerprints Attwood, TK Findlay,
JBC (1993) Design of a discriminating fingerprint
for G-protein-coupled receptors. Protein Eng.,
6(2), 167176.
N
C
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Visualising fingerprints Attwood, TK Findlay,
JBC (1993) Design of a discriminating fingerprint
for G-protein-coupled receptors. Protein Eng.,
6(2), 167176.
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Diagnosing partial matches

• Missed by PROSITE
• wasnt annotated as a FN

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An integrated approachMulder, NJ, Apweiler, R,
Attwood, TK, Bairoch, A et al. (2007) New
developments in InterPro. NAR, 35, D224-8.

• To simplify sequence analysis, the family dbs
  were integrated within a unified annotation
  resource InterPro
• initial partners were PRINTS, PROSITE, profiles
  Pfam
• now many more partners
• linked to its satellite dbs
• but lags behind their coverage
• by Oct 2007, it had 14,768 entries covered 76
  of UnitProtKB
• major role in fly human genome annotation

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InterPro method comparison
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Where has this got us?
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Understanding the tools estimating significance

• How do we know what to believe?
• Lets explore some of the difficulties that arise
  when pair-wise search tools (BLAST FastA)
  family-based methods are used naïvely
• these examples caution us to think about what the
  results actually mean in biological terms.....

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Identifying sequence similarity

• GPCRs present many challenges for in silico
  functional analysis
• Several signature-based methods now available
• with different areas of optimum application
• Yet naïve, pair-wise similarity searching has
  been the mainstay of functional annotation
  efforts
• it allows us to identify/quantify relationships
  between sequences
• But quantifying similarity between sequences is
  not the same as identifying their functions

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Problems with pairwise similarity toolsGaulton,
A Attwood, TK (2003) Bioinformatics approaches
for the classification of G protein-coupled
receptors. Current Opinion in Pharmacology, 3,
114-120.

• For identifying precise families to which
  receptors belong the ligands they bind,
  pair-wise tools are limited
• at what level of seq ID is ligand specificity
  conserved?
• some GPCRs with 25 ID share a common ligand
• others, with greater levels, dont
• It may be impossible to tell from BLAST if an
  orphan belongs to a known family (the top hit),
  or if it will bind a novel ligand
• e.g., for the now de-orphaned UR2R, BLAST
  indicates most similarity to the type 4 SSRs, yet
  it is known to bind a different (related) ligand

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When is a GPCR not an SSR?

• Query length 389 AA
• Date run 2002-10-18 090829 UTC0100 on
  sib-blast.unil.ch
• Taxon Homo sapiensDatabase XXswissprot
  
• 120,412 sequences 45,523,583 total letters
  SWISS-PROT Release 40.29 of 10-Oct-2002
• Db AC Description
  Score E-value
• sp Q9UKP6 Q9UKP6 Orphan receptor Homo
  sapiens... 782 0.0
• sp P31391 SSR4_HUMAN Somatostatin receptor
  type 4 (SS4R) SSTR4... 167 3e-41
• sp O43603 GALS_HUMAN Galanin receptor type 2
  (GAL2-R) (GALR2) G... 147 4e-35
• sp P30872 SSR1_HUMAN Somatostatin receptor
  type 1 (SS1R) (SRIF-2... 144 3e-34
• sp P32745 SSR3_HUMAN Somatostatin receptor
  type 3 (SS3R) (SSR-28... 140 3e-33
• sp P35346 SSR5_HUMAN Somatostatin receptor
  type 3 (SS5R) (SSTR5)... 140 6e-33
• sp P30874 SPLICE ISOFORM B of P30874 SSTR2
  Homo sapiens... 134 3e-31
• sp P30874 SSR2_HUMAN Somatostatin receptor
  type 2 (SS2R) (SRIF-1... 134 3e-31
• sp P48145 GPR7_HUMAN Neuropeptides B/W
  receptor type 1 (G protei... 133 7e-31
• sp O60755 GALT_HUMAN Galanin receptor type 3
  (GAL3-R) (GALR3) G... 132 2e-30
• sp P41143 OPRD_HUMAN Delta-type opioid
  receptor (DOR-1) OPRD1 ... 128 2e-29
• sp P35372 SPLICE ISOFORM 1A of P35372
  OPRM1 Homo sapien... 125 1e-28
• sp P35372 OPRM_HUMAN Mu-type opioid receptor
  (MOR-1) OPRM1 Ho... 125 1e-28

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When is a GPCR not an SSR?when its a UR2R

• Query length 389 AA
• Date run 2002-10-18 090829 UTC0100 on
  sib-blast.unil.ch
• Taxon Homo sapiensDatabase XXswissprot
  
• 120,412 sequences 45,523,583 total letters
  SWISS-PROT Release 40.29 of 10-Oct-2002
• Db AC Description
  Score E-value
• sp Q9UKP6 UR2R_HUMAN Urotensin II receptor
  (UR-II-R) GPR14 Ho... 782 0.0
• sp P31391 SSR4_HUMAN Somatostatin receptor
  type 4 (SS4R) SSTR4... 167 3e-41
• sp O43603 GALS_HUMAN Galanin receptor type 2
  (GAL2-R) (GALR2) G... 147 4e-35
• sp P30872 SSR1_HUMAN Somatostatin receptor
  type 1 (SS1R) (SRIF-2... 144 3e-34
• sp P32745 SSR3_HUMAN Somatostatin receptor
  type 3 (SS3R) (SSR-28... 140 3e-33
• sp P35346 SSR5_HUMAN Somatostatin receptor
  type 3 (SS5R) (SSTR5)... 140 6e-33
• sp P30874 SPLICE ISOFORM B of P30874 SSTR2
  Homo sapiens... 134 3e-31
• sp P30874 SSR2_HUMAN Somatostatin receptor
  type 2 (SS2R) (SRIF-1... 134 3e-31
• sp P48145 GPR7_HUMAN Neuropeptides B/W
  receptor type 1 (G protei... 133 7e-31
• sp O60755 GALT_HUMAN Galanin receptor type 3
  (GAL3-R) (GALR3) G... 132 2e-30
• sp P41143 OPRD_HUMAN Delta-type opioid
  receptor (DOR-1) OPRD1 ... 128 2e-29
• sp P35372 SPLICE ISOFORM 1A of P35372
  OPRM1 Homo sapien... 125 1e-28
• sp P35372 OPRM_HUMAN Mu-type opioid receptor
  (MOR-1) OPRM1 Ho... 125 1e-28

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The trouble with top hits

• The most statistically significant hit is not
  always the most biologically relevant
• Yet many rule-based expert systems still rely
  on top BLAST or FastA hits to make their
  diagnoses
• BLAST/FastA see generic similarity not the
  often-subtle differences that constitute the
  functional determinants between closely-related
  receptor families subtypes
• Failure to appreciate this fundamental point has
  generated numerous annotation errors in our
  databases

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Misleading annotation via FastA
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Misleading results from BLAST

• As weve seen, its tempting to use top hits from
  BLAST or FastA results to classify unknown
  proteins
• but this may lead us ( especially computer
  programs) to false functional conclusions
• PSI-BLAST is more sensitive than BLAST, because
  it creates a profile from hits above a given
  threshold
• but this too can cause problems
• lets take a closer look

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So, is UL78 a GPCR? if so, what sort?
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What PSI-BLAST said (profile dilution in action)



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What GeneQuiz said
a thrombin receptor
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What GeneQuiz said later
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Overview of results pair-wise family-based
methods
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What is UL78?
Tool No hit Poor hit Significant hit
BLAST GPCRs in list
PSI-BLAST thrombin receptor chemokine opioid receptors
PROSITE profile GPCR
Pfam
PRINTS
Blocks-PRINTS GPCR
GeneQuiz thrombin receptor C5A receptor
?
?
Bioinformatics tools, alone, cannot tell us!
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So, beware top hitsbut also beware bottom hits!

• Let us now compare contrast some InterPro
  results with those of its source dbs

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Rhodopsin-like superfamily GPCRs in InterPro 2005

• IPR000276 GPCR_Rhodopsn 7752
  proteins
• PS50262 G_PROTEIN_RECEP_F1_2 7702 proteins
• PF00001 7tm_1 7064 proteins
• PS00237 G_PROTEIN_RECEP_F1_1 6527 proteins
• PR00237 GPCRRHODOPSN 5821 proteins
  (dont include partials)

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Rhodopsin-like superfamily GPCRs in the source
databases

• Pfam FP ? FN ? U ? TP? 8776 matches
  7064
• PROSITE (profile) FP 3 FN 3 U 12 TP 1837
  matches
• 7702
• PROSITE (regex) FP 92 FN 261 U 0 TP 1530
  matches 6527
• PRINTS FP 0 FN ? U 0 TP 1154 matches
  5821
• gt2165 updated

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Rhodopsin-like superfamily GPCRs in InterPro 2007

• IPR000276 GPCR_Rhodopsn 16,845
  proteins
• PS50262 G_PROTEIN_RECEP_F1_2 16,714 proteins
• PF00001 7tm_1 15,712 proteins
• PR00237 GPCRRHODOPSN 13,405 proteins
• PS00237 G_PROTEIN_RECEP_F1_1 13,723 proteins
  

No human curator has time to validate all these
matches
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14,615 rhodopsin-like superfamily GPCRs in Pfam?
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Pfam match Q6NV75/24-297
ID Q6NV75 PRELIMINARY PRT 609
AA. AC Q6NV75 DT 05-JUL-2004 (TrEMBLrel. 27,
Created) DT 05-JUL-2004 (TrEMBLrel. 27, Last
sequence update) DT 05-JUL-2004 (TrEMBLrel. 27,
Last annotation update) DE G protein-coupled
receptor 153. GN NameGPR153 OS Homo sapiens
(Human). OX NCBI_TaxID9606 RN 1 RP
SEQUENCE FROM N.A. RC TISSUEBrain RA
Strausberg R.L., Feingold E.A., Grouse L.H.,
Derge J.G., RA Jones S.J., Marra M.A. RT
"Generation and initial analysis of more than
15,000 full-length RT human and mouse cDNA
sequences." RL Proc. Natl. Acad. Sci. U.S.A.
9916899-16903(2002). RP SEQUENCE FROM N.A. RC
TISSUEBrain RA Strausberg R. RL Submitted
(MAR-2004) to the EMBL/GenBank/DDBJ databases. DR
EMBL BC068275 AAH68275.1 -. DR GO
GO0004872 DR InterPro IPR000276
GPCR_Rhodpsn. DR Pfam PF00001 7tm_1 1. DR
PROSITE PS50262 G_PROTEIN_RECEP_F1_2 1. KW
Receptor SQ SEQUENCE 609 AA 65341 MW
E525CC7F60D0891C CRC64 MSDERRLPGS
AVGWLVCGGL SLLANAWGIL SVGAKQKKWK PLEFLLCTLA
ATHMLNVAVP IATYSVVQLR RQRPDFEWNE GLCKVFVSTF
YTLTLATCFS VTSLSYHRMW MVCWPVNYRL SNAKKQAVHT
VMGIWMVSFI LSALPAVGWH DTSERFYTHG CRFIVAEIGL
GFGVCFLLLV GGSVAMGVIC TAIALFQTLA VQVGRQADHR
AFTVPTIVVE DAQGKRRSSI DGSEPAKTSL QTTGLVTTIV
FIYDCLMGFP VLVVSFSSLR ADASAPWMAL CVLWCSVAQA
LLLPVFLWAC DRYRADLKAV REKCMALMAN DEESDDETSL
EGGISPDLVL ERSLDYGYGG DFVALDRMAK YEISALEGGL
PQLYPLRPLQ EDKMQYLQVP PTRRFSHDDA DVWAAVPLPA
FLPRWGSGED LAALAHLVLP AGPERRRASL LAFAEDAPPS
RARRRSAESL LSLRPSALDS GPRGARDSPP GSPRRRPGPG
PRSASASLLP DAFALTAFEC EPQALRRPPG PFPAAPAAPD
GADPGEAPTP PSSAQRSPGP RPSAHSHAGS LRPGLSASWG
EPGGLRAAGG GGSTSSFLSS PSESSGYATL HSDSLGSAS //
?
PROSITE (profile) no match
false negative
PROSITE (regex) no match
PRINTS no match
ClustalW sequences too divergent to be aligned
GPCR?
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Beware top bottom hitsbut also beware
simplistic analysis tools coupled with wet
experiments!

• Lets finally look at how hydropathy profiles
  can compel biologists to make strange deductions
  
• - still get their results published in
  Science!

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ID Q9C929_ARATH Unreviewed
401 AA. AC Q9C929 DT 01-JUN-2001, integrated
into UniProtKB/TrEMBL. DT 01-JUN-2001, sequence
version 1. DT 24-JUL-2007, entry version 23. DE
Putative G protein-coupled receptor
80093-78432. GN NameF14G24.19
OrderedLocusNamesAt1g52920 OS Arabidopsis
thaliana (Mouse-ear cress). OC Eukaryota
Viridiplantae Streptophyta ... Arabidopsis. OX
NCBI_TaxID3702 RN 1 RP NUCLEOTIDE
SEQUENCE. RA Lin X., Kaul S., Town C.D., Benito
M., Creasy T.H., Haas B.J., Wu D., RA Maiti R.,
Ronning C.M., Koo H., Fujii C.Y., Utterback
T.R., RA Barnstead M.E., Bowman C.L., White O.,
Nierman W.C., Fraser C.M. RT "Arabidopsis
thaliana chromosome 1 BAC F14G24 genomic
sequence." RL Submitted (DEC-1999) to the
EMBL/GenBank/DDBJ databases. RN 2 RP
NUCLEOTIDE SEQUENCE. RA Town C.D., Kaul S. RL
Submitted (JAN-2001) to the EMBL/GenBank/DDBJ
databases. DR EMBL AC019018 AAG52264.1 -
Genomic_DNA. EMBL / GenBank / DDBJ DR PIR
E96570 E96570. DR UniGene At.66935 -. DR
GenomeReviews CT485782_GR AT1G52920. DR KEGG
athAt1g52920 -. DR TAIR At1g52920 -. DR
GO GO0004872 Freceptor activity
IEAUniProtKB-KW. DR InterPro IPR007822
LANC_like. DR InterPro Graphical view of
domain structure. DR Pfam PF05147 LANC_like
1. KW Receptor. SQ SEQUENCE 401 AA 45284
MW C9D3BF8CC8F0FE0B CRC64 MPEFVPEDLS
GEEETVTECK DSLTKLLSLP YKSFSEKLHR YALSIKDKVV
WETWERSGKR VRDYNLYTGV LGTAYLLFKS YQVTRNEDDL
KLCLENVEAC DVASRDSERV TFICGYAGVC ALGAVAAKCL
GDDQLYDRYL ARFRGIRLPS DLPYELLYGR AGYLWACLFL
NKHIGQESIS SERMRSVVEE IFRAGRQLGN KGTCPLMYEW
HGKRYWGAAH GLAGIMNVLM HTELEPDEIK DVKGTLSYMI
QNRFPSGNYL SSEGSKSDRL VHWCHGAPGV ALTLVKAAQV
YNTKEFVEAA MEAGEVVWSR GLLKRVGICH GISGNTYVFL
SLYRLTRNPK YLYRAKAFAS FLLDKSEKLI SEGQMHGGDR
PFSLFEGIGG MAYMLLDMND PTQALFPGYE L //
Pfam Lanthionine synthetase C-like protein
PROSITE (profile) no match
PROSITE (regex) no match
PRINTS no match
ClustalW sequences too divergent to be aligned
GPCR?
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They do sums (quickly) crude string matching
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Seeking deeper functional insights Attwood, TK,
Croning, MD Gaulton, A (2002) Deriving
structural and functional insights from a
ligand-based hierarchical classification of G
protein-coupled receptors. Protein Eng., 15,
7-12.

• Sfamily, family subtype motifs have different
  locations
• If sfamily motifs define the common scaffold,
  hypothesis
• family motifs relate to ligand binding?
• subtype motifs relate to G protein coupling?
• powerful tools for subtyping potentially
  de-orphaning GPCRs

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Locations of ligand-binding residues motif
distribution
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Locations of G protein-coupling residues
distribution of motifs
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Seeking deeper functional insights? Attwood, TK,
Croning, MD Gaulton, A (2002) Deriving
structural and functional insights from a
ligand-based hierarchical classification of G
protein-coupled receptors. Protein Eng., 15,
7-12.

• Clearly, many family- subtype motifs are simply
  in the wrong place for the initial hypothesis
  to be true

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Refining the hypothesis

• Besides, its not that simple
• only part of the answer
• Need to consider that GPCRs dont function in
  isolation
• their functions are modulated via interactions
  with other proteins
• Also, the phenomenon of dimerisation challenges
  the view of the GPCR monomer as functional unit
• many GPCRs exist as homo- heterodimers
• Such observations demand a more systematic
  analysis of motifs their likely functional
  roles

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Oligomerisation protein-protein interaction
residues/regions A pilot study with adrenergic,
bradykinin dopamine receptors
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Where next?

• Based on location, some family-level motifs
  couldnt be involved in ligand binding some
  subtype-level motifs couldnt be involved in G
  protein coupling
• clearly, 3D location must be taken into account
• functional correlations would then be stronger
• The remaining motifs are likely to be involved in
  other molecular interactions
• e.g., dimerisation, effector proteins.(early
  results promising)
• this will help us to build a knowledge-based
  system to help suggest the likely functional
  roles for family- subtype-level motifs in future

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Conclusions

• There are many barriers to success for the
  jobbing bioinformatician, e.g.
• not fully understanding the processes were
  trying to model predict (e.g., protein folding)
• the dynamic nature of biological data
• not having been rigorous in the way we define
  /or describe biology/biological processes in the
  literature
• the volume of data, data heterogeneity
• maintenance of data, propagation of errors
• Possibly the largest hurdle is that computers are
  number crunchers
• they dont do biology, trying to teach them is
  hard
• the harder we try, the clearer it is how naïve
  weve been

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Conclusions

• In silico functional annotation requires several
  dbs to be searched several tools to be used
• different methods provide different perspectives
• dbs arent complete their contents dont fully
  overlap
• The more dbs searched, the harder it is to
  interpret results
• The more computers are involved in automating
  annotation, the greater the need for
  collaboration
• especially between s/w developers, annotators
  wet experimentalists
• The more data we have, the more rigorous we must
  be in thinking/writing if we are to make sense of
  the complexities

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ConclusionsFlower DR Attwood, TK (2004)
Integrative bioinformatics for functional genome
annotation trawling for G protein-coupled
receptors.Semin Cell Dev Biol., 15(6), 693-701.

• For GPCRs, there are many analysis tools
  available
• BLAST, FastA, family databases, modelling tools,
  etc.
• We must understand the limitations of the methods
• no method is infallible or able to replace the
  need for biological validation
• use all available resources understand their
  problems none is best!
• Used wisely, bioinformatics tools are useful
• BLAST/FastA offer broad brush strokes,
  motif-methods add fine detail
• together, they facilitate receptor
  characterisation prediction of ligand
  specificity, allow identification of novel
  ligand-binding, G protein-coupling or other
  likely molecular interaction motifs
• We are a long way from having reliable tools for
  deducing GPCR function structure from sequence
• but with the right approach, there is hope

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