GENOME ANNOTATION AND FUNCTIONAL GENOMICS The protein sequence perspective

1
GENOME ANNOTATION AND FUNCTIONAL GENOMICSThe
protein sequence perspective
2
GENOME ANNOTATION

• Two main levels
• STRUCTURAL ANNOTATION Finding genes and other
  biologically relevant sites thus building up a
  model of genome as objects with specific
  locations
• FUNCTIONAL ANNOTATION Objects are used in
  database searches (and expts) aim is attributing
  biologically relevant information to whole
  sequence and individual objects

3
WHY PROTEIN RATHER THAN DNA?

• Larger alphabet -more sensitive comparisons
• Protein sequences lower signal to noise ratio
• Less redundancy and no frameshifts
• Each aa has different properties like size,
  charge etc
• Closer to biological function
• 3D structure of similar proteins may be known
• Evolutionary relationships more evident
• Availability of good, well annotated protein
  sequence and pattern databases

4
Large-scale genome analysis projects

• Rate-limiting step is annotation
• Whole genome availability provides context
  information
• Main goal is to bridge gap between genotype and
  phenotype

5
Definitions of Annotation

• Addition of as much reliable and up-to-date
  information as possible to describe a sequence
• Identification, structural description,
  characterisation of putative protein products and
  other features in primary genomic sequence
• Information attached to genomic coordinates with
  start and end point, can occur at different
  levels
• Interpreting raw sequence data into useful
  biological information

6
ANNOTATION/FUNCTION CAN BE MAPPED TO DIFFERENT
LEVELS

• ? ORGANISM -phenotypic function (morphology,
  physiology, behaviour, environemntal response),
  context NB
• ? CELLULAR -metabolic pathway, signal cascades,
  cellular localisation. Context dependent
• ? MOLECULAR -binding sites, catalytic activity,
  PTM, 3D structure
• ? DOMAIN
• ? SINGLE RESIDUE

7
Annotation is the description of

• Function(s) of the protein
• Post-translational modification(s)
• Domains and sites
• Secondary structure
• Quaternary structure
• Similarities to other proteins
• Disease(s) associated with deficiencie(s) in the
  protein
• Sequence conflicts, variants, etc.

8
Additional information for proteins

• ALTERNATIVE PRODUCTS
• CATALYTIC ACTIVITY
• COFACTOR
• DEVELOPMENTAL STAGE
• DISEASE
• DOMAIN
• ENZYME REGULATION
• FUNCTION
• INDUCTION

• PATHWAY
• PHARMACEUTICALS
• POLYMORPHISM
• PTM
• SIMILARITY
• SUBCELLULAR LOCATION
• SUBUNIT
• TISSUE SPECIFICITY

9
Amino-acid sites are

• Post-translational modification of a residue
• Covalent binding of a lipidic moiety
• Disulfide bond
• Thiolester bond
• Thioether bond
• Glycosylation site
• Binding site for a metal ion
• Binding site for any chemical group (co-enzyme,
  prosthetic group, etc.)

10
Regions

• SIGNAL SEQUENCE
• TRANSIT PEPTIDE
• PROPEPTIDE
• CHAIN
• PEPTIDE
• DOMAIN

• ACTIVE SITE
• DNA BIND SITE
• METAL BIND SITE
• MOLECULE BIND SITE
• TRANSMEMBRANE

11
Annotation sources

• publications that report new sequence data
• review articles to periodically update the
  annotation of families or groups of proteins
• external experts
• protein sequence analysis

12
Approaches to functional annotation

• Automatic annotation (sequence homology, rules,
  transfer info from pdb)
• Automatic classification (pattern databases,
  clustering, structure)
• Automatic characterisation (functional databases)
• Context information (comparitive genome analysis,
  metabolic pathway databases)
• Experimental results (2D gels, microarrays)
• Full manual annotation (SWISS-PROT style)

13
PROTEIN SEQUENCE ANALYSIS

• Protein sequence can come from gene predictions,
  literature or peptide sequencing
• Analysis on different levels
• molecular
• cellular
• organism
• Simplest case- match for whole sequence in
  database- determination of structure and function
• In between- partial matches across sequence to
  diverse or hypothetical proteins
• Difficult case- no match, have to derive
  information from amino acid properties, pattern
  searches etc

14
From sequence to function
15
Predicting function from sequence similarity

• Orthologues- arose from speciation, same gene in
  different organisms -can have lt30 homology
• Paralogues- from duplication within a genome,
  second copy may have new or changed function
• (difficult to distinguish between otho- and
  paralogues unless whole genome is available)
• Equivalog- proteins with equivalent functions
• Analog- proteins catalyzing same reaction but not
  structurally related
• Some enzymes may have seq similarity simply
  because common catalytic site, substrate,
  pathway.

16
TYPES OF HOMOLOGY
PROTEIN/DOMAIN
Superfamily
Duplication within species
Paralogues may have different functions
A
B
Speciation
Orthologues may have different functions, if
same - Equivalogs
B2
B1
17
Sequence homology in genomes

• When you do a whole genome BLAST search there is
  a general pattern of results

Maverick genes tend to diverge more frequently
than core genes
18
Using homology information for automatic
annotation- automatic annotation of TrEMBL as an
example
19
Requirements for automatic annotation

• Well-annotated reference database (eg SWISS-PROT)
• Highly reliable diagnostic protein family
  signature database with the means to assign
  proteins to groups (eg CDD, InterPro, IProClass)
• A RuleBase to store and manage the annotation
  rules, their sources and their usage

20
Direct Transfer

• Search target
• Transfer annotation to target database
• ExampleFASTA against sequence database and
  transfer of DE line of best hit

XDB
Target
21
Multiple Sources

• Usually more than one external database is used
• Combine the different results

XDB
Target
22
Conflicts

• Contradiction
• Inconsistencies
• Synonyms
• Redundancy

23
Translation

• Use a translator to map XDB language to target
  language

XDB
Target
24
Translation Examples

• ENZYME ??TrEMBL CA L-ALANINED-ALANINECC -!-
  CATALYTIC ACTIVITY L-ALANINECC D-ALANINE.
• PROSITE ??TrEMBL/SITE3,heme_ironFT METAL
  IRON
• Pfam ??TrEMBL FT DOMAIN zf_C3HC4FT
  ZN_FING C3HC4-TYPE

25
Demands on a system for automated data analysis
and annotation

• Correctness
• Scalability
• Updateable
• Low level of redundant information
• Completeness
• Standardized vocabulary

26
What do we have?

• SWISS-PROT
• RuleBase
• TrEMBL
• PROSITE (and Pfam, PRINTS, ProDom, SMART, Blocks
  etc)
• SWISS-PROT/TrEMBL/RuleBase in Oracle

27
Standardized transfer of annotation from
characterized proteins in SWISS-PROT to TrEMBL
entries

• TrEMBL entry is reliably recognized by a given
  method as a member of a certain group of proteins
• corresponding group of proteins in SWISS-PROT
  shares certain annotation
• common annotation is transferred to the TrEMBL
  entry and flagged as annotated by similarity

28
Automatic annotation information flow

• Get information necessary to assign proteins to
  groups eg using InterPro or other biological or
  family information- store in RuleBase
• Group proteins in SWISS-PROT by these conditions
• Extract common annotation shared by all these
  proteins- store in RuleBase
• Group unannotated sequences by the conditions
• Transfer common annotation flagged with evidence
  tags
• Note can add taxonomic constraints

29
Extract Reference Entries

• Use XDB to extract entries from standard database
• ExamplePfamPF00509 HemagglutininHEMA_IAVI7/P03
  435HEMA_IANT6/P03436HEMA_IAAIC/P03437HEMA_IAX31
  /P03438HEMA_IAME2/P03439HEMA_IAEN7/P03440HEMA_I
  ABAN/P03441HEMA_IADU3/P03442HEMA_IADA1/P03443HE
  MA_IADMA/P03444HEMA_IADM1/P03445HEMA_IADA2/P0344
  6HEMA_IASH5/P03447

Pfam
TrEMBL
SWISS-PROT
30
Extract Common Annotation

• 132 entries read131 ID HEMA_XXXXX125 DE
  HEMAGGLUTININ PRECURSOR. 6 DE
  HEMAGGLUTININ.131 GN HA130 CC -!- FUNCTION
  HEMAGGLUTININ IS RESPONSIBLE FOR ATTACHING
  THE130 CC VIRUS TO CELL RECEPTORS AND FOR
  INITIATING INFECTION.125 CC -!- SUBUNIT
  HOMOTRIMER. EACH OF THE MONOMER IS FORMED BY
  TWO125 CC CHAINS (HA1 AND HA2) LINKED BY A
  DISULFIDE BOND. 75 DR HSSP P03437 1HGD. 31
  DR HSSP P03437 1DLH.131 KW HEMAGGLUTININ
  GLYCOPROTEIN ENVELOPE PROTEIN102 KW SIGNAL
  1 KW COAT PROTEIN POLYPROTEIN
  3D-STRUCTURE130 FT CHAIN
  HA1 CHAIN.107 FT CHAIN
  HA2 CHAIN.102 FT SIGNAL

31
Store Common Annotation

• Store the used conditions and the extracted
  common annotation in a separate database

XDB
TrEMBL
SWISS-PROT
RuleBase
32
RULES

• Rules describe
• the content of the annotation to be transferred
  (ACTIONS),
• the CONDITIONS which the target TrEMBL entry
  must fulfill in order to allow transfer of the
  annotation.
• Rules uniquely describe or delineate a set of
  SWISS-PROT entries.
• The common annotation in these entries is
  transferred to TrEMBL.

33
// RULE RU000482 DATE 2001-01-11 USER
OPSWFL PACK PROSITE ?PSAC PS00449 ?EMOT
PS00449 !ECNO 3.6.1.34 !SPDE ATP synthase A
chain !CCFU KEY COMPONENT OF THE PROTON
CHANNEL IT MAY PLAY A DIRECT ROLE IN THE
TRANSLOCATION OF PROTONS ACROSS THE MEMBRANE (BY
SIMILARITY) !CCSU F-TYPE ATPASES HAVE 2
COMPONENTS, CF(1) - THE CATALYTIC CORE - AND
CF(0) - THE MEMBRANE PROTON CHANNEL. CF(1) HAS
FIVE SUBUNITS ALPHA(3), BETA(3), GAMM A(1),
DELTA(1), EPSILON(1). CF(0) HAS THREE MAIN
SUBUNITS A, B AND C (BY SIMILARITY) !CCLO
INTEGRAL MEMBRANE PROTEIN (By Similarity) !CCSI
TO THE ATPASE A CHAIN FAMILY !SPKW CF(0) !SPKW
Hydrogen ion transport !SPKW Transmembrane //
ACTIONS

CONDITIONS
34
Add Annotation to Target

• Use conditions to extract entries from TrEMBL
• Add common annotation to the entries

XDB
TrEMBL
SWISS-PROT
RuleBase
35
Automatic annotation using multiple dbs

• Extract conditions from XDB
• Group SWISS-PROT by conditions
• Extract common annotation
• Group TrEMBL by conditions
• Add common annotation to TrEMBL

ENZYME
Pfam
INTERPRO
PROSITE
TrEMBL
SWISS-PROT
RuleBase
36
Using tree structure of InterPro
37
RU000652 with additional condition connected by
AND
// RULE RU000652 DATE 2001-01-11 USER
OPSWFL PACK PROSITE ?IPRO IPR002379 ?PSAC
PS00605 ?EMOT PS00605 !SPDE ATP synthase C
chain (Lipid-binding protein) (Subunit C) !ECNO
3.6.1.34 !CCSU F-TYPE ATPASES HAVE 2
COMPONENTS, CF(1) - THE CATALYTIC CORE - AND
CF(0) - THE MEMBRANE PROTON CHANNEL. CF(1) HAS
FIVE SUBUNITS ALPHA(3), BETA(3), GAMMA(1),
DELTA(1), EPSILON(1). CF(0) HAS THREE MAIN
SUBUNITS A, B AND C (By Similarity) !CCSI TO
THE ATPASE C CHAIN FAMILY !SPKW CF(0) !SPKW
Hydrogen ion transport !SPKW Lipid-binding !SPKW
Transmembrane //
Additional condition (parent signature)
38
Condition types

• Signature hits
• - Prosite, Prints, Pfam, Prodom
• Taxonomy
• - Broad groups like
• Archaea
• Bacteriophage
• Eukaryota
• Prokaryota
• Eukaryotic viruses
• - more specific such as species
• Organelle

• Conditions
• Negated conditions

39
Rule-building

• Grouping and extraction of common annotation
• - semi automated but involves manual data-mining
• assisted by perl/shell scripts.
• Algorithmic data-mining
• - fully automated.
• - fast.
• - exhaustive exploration of condition-set/annota
  tion
• search-space .
• - non-biological, validity of rules being
  assessed
• by comparison with semi-manual
  approach.

40
Advantages of this method

• Uses reliable ref database, prevents propagation
  of incorrect annotation
• Using common annotation of multiple entries,
  lower over-prediction than from best hit of BLAST
• Can standardize annotation and nomenclature of
  target sequences, since reference is standardized
• Can have different levels of common annotation
  from different levels of family hierarchy
• Independent of multi-domain organisation
• Evidence tags allow for easy tracking and updating

41
Pitfalls of automatic functional analysis

• Multifunctional proteins- genome projects often
  assign single function, info is lost in homology
  search
• Hypothetical proteins (40 oRFs unknown), and
  poorly or even wrongly annotated proteins
• No coverage of position-specific annotation eg
  active sites
• Current methods provide only a phrase describing
  some properties of the unknown protein
• It is important to have evidence for all
  annotation added

42
EVIDENCE TAGS
43
(No Transcript)
44
Predicting function from non-homology

• Look at position of genes relative to others,
  compare with other organisms
• Can still build up rules from annotated sequences
  using information you have on other features like
  fold, physical properties etc.
• Use physical properties and known attributes

45
Protein functions from regions

• Active sites- short, highly conserved regions
• Loops- charged residues and variable sequence
• Interior of protein- conservation of charged
  amino acids

46
Protein functions from specific residues

• Polar (C,D,E,H,K,N,Q,R,S,T) - active sites
• Aromatic (F,H,W,Y) - protein ligand- binding
  sites
• Zn-coord (C,D,E,H,N,Q) - active site, zinc
  finger
• Ca2-coord (D,E,N,Q) - ligand-binding site
• Mg/Mn-coord (D,E,N,S,R,T) - Mg2 or Mn2
  catalysis, ligand binding
• Ph-bind (H,K,R,S,T) - phosphate and sulphate
  binding

• C disulphide-rich, metallo- thionein, zinc
  fingers
• DE acidic proteins (unknown)
• G collagens
• H histidine-rich glycoprotein
• KR nuclear proteins, nuclear localisation
• P collagen, filaments
• SR RNA binding motifs
• ST mucins

47
Supplement annotation with Xrefs to other
databases

• DDBJ/EMBL/GenBank Nucleotide Sequence Database
• PDB
• Genomic databases (FlyBase, MGD, SGD)
• 2D-Gel databases (ECO2DBASE, SWISS-2DPAGE,
  Aarhus/Ghent, YEPD, Harefield), Gene expression
  data
• Specialized collections (OMIM, InterPro, PROSITE,
  PRINTS, PFAM, ProDom, SMART, ENZYME, GPCRDB,
  Transfac, HSSP)

48
Approaches to functional annotation

• Automatic annotation (sequence homology, rules,
  transfer info from pdb)
• Automatic classification (pattern databases,
  clustering, structure)
• Automatic characterisation (functional databases)
• Context information (comparitive genome analysis,
  metabolic pathway databases)
• Experimental results (2D gels, microarrays)
• Full manual annotation (SWISS-PROT style)

49
AUTOMATIC CLASSIFICATION
Annotation can by using Clustering methods eg
CluSTR (EBI), and pattern searches (InterPro
etc)- classification of proteins into different
families
50
(No Transcript)
51
(No Transcript)
52
AUTOMATIC CHARACTERIZATION- FUNCTIONAL ANNOTATION
SCHEMES

• First attempt Riley classification of E.coli
• Genome sequencing projects driving force
• Need standardised system and vocabulary
• Functional schemes normally hierarchies of
  different levels of generalisation

53
Databases for Functional Information

• KEGG -Kyoto encyclopedia of genes and genomes
• (http//www.genome.ad.jp/kegg/)
• Links genome information (GENES database) to high
  order functional information stored in PATHWAY
  database.
• Also has LIGAND database for chemical compounds,
  molecules and reactions.
• PEDANT -Protein Extraction, Description and
  Analysis Tool
• (http//pedant.gsf.de/)
• Annotation for complete and incomplete genomes
  eg. List of ORFs, EC numbers, functional
  categories, list seqs with homologs, gene
  clusters, domain hits, TM, structure links,
  search facility for sequences etc
• WIT What is there
• ( http//www.cme.msu.edu/WIT)
• Database of metabolic pathways, can text search
  for ORFs, pathways, enzymes

54
Databases for Functional Information (2)

• COG -Clusters of Orthologous Groups
• (http//www.ncbi.nlm.nih.gov/COG)
• Phylogenetic classification of proteins encoded
  in complete genomes.
• Contains 2791 COGs including 30 genomes.
• COGs thought to contain orthologous proteins,
  classified into broad functional categories
  (transciption, replication, cell division).
• COGNITOR assigns proteins to COGs based on
  best-hit, divides multi-domain proteins
• Can compare results with complete genomes, look
  for missing functions
• GO Gene Ontology
• (http//www.geneontology.org)
• Standard vocabulary first used for mouse, fly and
  yeast
• Three ontologies molecular function, biological
  process and cellular component

55
Databases for Functional Information (3)

• MIPSMYGD FunCat Functional catalogue (yeast)
  http//www.mips.biochem.mpg.de/proj/yeast
• EcoCyc -Encyclopedia of E. coli Genes and
  Metabolism http//ecocyc.doubletwist.com/ecocyc/e
  cocyc.html
• Enzyme database http//wwwexpasy.ch/sprot/enzym
  e.html
• TIGR Gene identification list http//www.tigr.org
  /tdb/mdb/mdb.html
• All schemes have different depths, breadths and
  resolutions
• Schemes need to be applicable to all organisms,
  standardized for comparisons and permit multiple
  assignments

56
Assignment of function

• Use a combination of databases, especially those
  with standardised functional information
• Search function databases with sequences to find
  matches -assign function eg PENDANT, PIR
  superfamilies, COGs, GO (via InterPro)

57
FUNCTIONAL CLASSIFICATION USING INTERPRO

• InterPro classification with 3-4 letter codes
• Mapping of InterPro entries to GO
• GO- Gene Ontology (SGD, FB MGD) universal
  ontology for
• molecular function
• biological process
• cellular component

58
Classification of IPRs
CGD Cell cycle/growth/death -CGDc cell
cycle/division -CGDg cell growth/development -CGDd
cell death CYS Cytoskeletal/structural -CYSc
cytoskeletal -CYSs structural -CYSv virus
coat/capsid protein DPT Defense/pathogenesis/tox
in DRG DNA/RNA-binding/regulation DRM DNA/RNA
metabolism -DRMr DNA repair/recombination -DRMp
DNA replication -DRMm DNA/RNA modification -DRMt
transcription/translation -DRMb ribosomal
protein
MET Metabolism -METs substrate metabolism
-METe electron transfer -METa amino acid
metabolism -METn nucleic acid metabolism
-METm metal binding proteins OTH Other
functions -OTHm cell motility -OTHt
transposition -OTHa cell adhesion -OTHg
miscellaneous functions -OTHh hormones -OTHi
immune-response proteins -OTHf multifunctional
proteins -OTHo multifunctional domains PFD
Protein folding degradation -PFDc chaperone
-PFDp protease/endopeptidase -PFDi
protease inhibitor
PRG Protein-binding/other regulation -PRGg
GPCRs -PRGr other receptors -PRGo other
regulation STD Signal transduction -STDk
sig transduction kinases -STDp sig transduction
phosphatases -STDr sig transduction response
reg -STDs sig transduction sensors -STDc
cell signalling TRS Transport and
secretion -TRSt transport (subtrates) -TRSi
transport (ions) -TRSs secretion -TRSr
carrier proteins UNK Unknown function
59
(No Transcript)
60
(No Transcript)
61
Pie charts of whole proteome analysis of 4
organisms
62
Distribution of protein functions
63
GENOME ANNOTATION TOOLS

• Oakridge Genome Annotation Channel
  (http//compbio.ornl.gov/channel/)
• ENSEMBL (http//ensembl.ebi.ac.uk)
• Artemis (http//www.sanger.ac.uk/Software/Artemis)
  Sequence viewer and annotation tool
• GeneQuiz (http//www.sander.ebi.ac.uk/genequiz/)
  System for automated annotation of sequences, web
  access required
• Genome Annotation Assessment Project (GASP1)
  (http//www.fruitfly.org/GASP1)

64
PEDANT SYSTEM
Layer 1 bioinformatics tools
Databases for searching
PSI-BLAST IMPALA PREDATOR CLUSTALW TMAP
SIGNALP SEG PROSEARCH COILS HMMER
MIPS PROSITE BLOCKS PIR COGS
parser of results
Layer 2 database to store information -MySQL
Manual annotation tool
Layer 3 user interface to display results
Programs written in Perl5 and some in C
-portable. Processing of one sequence takes about
3 minutes
65
Summary of protein sequence annotation

• Mask compositionally-biased and coiled-coil
  regions
• Identify transmembrane regions, signal peptides,
  GPI anchors
• Predict secondary structure
• Look for known domains from protein pattern
  databases
• Search sequence database for similar sequences
• If no or few results search with subsequences, do
  iterative searches
• Functional annotation consider function of each
  domain present, annotation from database
  homologs, function from hits with 3D structure

66
SUMMARY OF ANNOTATION PIPELINE
NB look out for multi-domain proteins, put into
genome context
Supplement with manual curation and use evidence
tags
67
LIMITS OF PROTEIN SEQUENCE ANALYSIS

• Predicting function from sequence requires
  another sequence to be mapped to a function many
  hypothetical proteins in db and UPFs
• If sequence homologues are found, may not be
  functional homologues -qualitative rather than
  quantitative process
• - orthologues may have different functions
• -enzyme homologues may be inactive
• -equivalent functions may use different genes,
  not orthologue
• Analogy can infer molecular function, but not
  necessarily cellular function

68
LIMITS OF PROTEIN SEQUENCE ANALYSIS (2)

• Databases are biased in sequence and aa
  composition and search is dependent on size
• If no homology found- limited amount of
  information can be inferred
• Incorrect annotation can be propagated when
  similarity is over part on sequence not used in
  annotation
• No answers to tissue-specificity, binding of
  ligands, relationship between genotype and
  phenotype

69
LIMITS OF PROTEIN SEQUENCE ANALYSIS (3)

• Need additional information from experiments, eg
  can predict glycosylation sites, but not kind of
  sugar attached
• Problem with multidomain proteins (assign
  orthology on basis of domains or domain
  composition of whole protein?) -check also known
  domain architectures and their taxonomic
  limitations

70
Using different approaches to functional
annotation Status for SPTR

• Automatic annotation (RuleBase) 20 of all
  protein sequences/20 of all new sequences
• Automatic classification (InterPro, CluSTr,
  Structure) 60 of all protein sequences/60 of
  all new sequences
• Automatic characterisation (GO) 40 of all
  protein sequences/40 of all new sequences
• Full annotation (SWISS-PROT style) 20 of all
  protein sequences/5 of all new sequences

71
Using different approaches to functional
annotation Future for SPTR

• Automatic annotation (RuleBase) 50 of all
  protein sequences in 2004
• Automatic classification (InterPro, CluSTr,
  Structure) 90 of all protein sequences in 2004
• Automatic characterisation (GO) 70 of all
  protein sequences in 2004
• Full annotation (SWISS-PROT style) 10 of all
  protein sequences in 2004

72
IMPORTANT TO NOTE

• DONT COMPLETELY TRUST COMPUTER RESULTS
• CHECK LITERATURE
• CONFIRM WITH WETLAB WORK- mutational analysis
  gives valuable info about function
• COMPROMISE BETWEEN OVER AND UNDER-PREDICTIONS
  -overpredictions can be checked by curators,
  easier to delete than find missing info.