Introduction to Bioinformatics Lecture 4: Bioinformatics infrastructure Centre for Integrative Bioinformatics VU (IBIVU) C E N T R F O R I N T E G R A

Introduction to Bioinformatics

Lecture Lecture 44: : Bioinformatics infrastructure Bioinformatics infrastructure

Centre for Centre for Integrative Bioinformatics VU (IBIVU)Integrative Bioinformatics VU (IBIVU)

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“Nothing in Biology makes sense except in the light of evolution” (Theodosius Dobzhansky (1900-1975))

“Nothing in bioinformatics makes sense except in the light of Biology”

Bioinformatics

Divergent evolution

Ancestral sequence: ABCD

ACCD (B C) ABD (C ø)

ACCD or ACCD Pairwise Alignment

AB─D A─BD

mutation deletion

Divergent evolution

Ancestral sequence: ABCD

ACCD (B C) ABD (C ø)

ACCD or ACCD Pairwise Alignment AB─D A─BD

true alignment

mutation deletion

What can be observed about divergent evolution

Ancestral sequence

Sequence 1 Sequence 2

1: ACCTGTAATC2: ACGTGCGATC * **D = 3/10 (fraction different sites (nucleotides))

G

G C

(a) G

A C

(b)

G

A A

(c)

One substitution -one visible

Two substitutions -one visible

Two substitutions -none visible

G

G A

(d)

Back mutation -not visible G

Convergent evolution

Often with shorter motifs (e.g. active sites) Motif (function) has evolved more than once

independently, e.g. starting with two very different sequences adopting different folds

Sequences and associated structures remain different, but (functional) motif can become identical

Classical example: serine proteinase and chymotrypsin

Serine proteinase (subtilisin) and chymotrypsin

Different evolutionary origins These proteins chop up other proteins Similarities in the reaction mechanisms. Chymotrypsin,

subtilisin and carboxypeptidase C have a catalytic triad of serine, aspartate and histidine in common: serine acts as a nucleophile, aspartate as an electrophile, and histidine as a base.

The geometric orientations of the catalytic residues are similar between families, despite different protein folds.

The linear arrangements of the catalytic residues reflect different family relationships. For example the catalytic triad in the chymotrypsin clan is ordered HDS, but is ordered DHS in the subtilisin clan and SDH in the carboxypeptidase clan.


chymotrypsin

serine proteinase

carboxypeptidase C

H D S

H SD

S D H

Catalytic triads

Read http://www.ebi.ac.uk/interpro/potm/2003_5/Page1.htm



A gene codes for a protein

Protein

mRNA

DNA

transcription

translation

CCTGAGCCAACTATTGATGAA

PEPTIDE

CCUGAGCCAACUAUUGAUGAA

Transcription + Translation = Expression

DNA makes mRNA makes Protein

Translation happens within the ribosome

Ribosome structure In the nucleolus, ribosomal RNA is

transcribed, processed, and assembled with ribosomal proteins to produce ribosomal subunits

At least 40 ribosomes must be made every second in a yeast cell with a 90-min generation time (Tollervey et al. 1991). On average, this represents the nuclear import of 3100 ribosomal proteins every second and the export of 80 ribosomal subunits out of the nucleus every second. Thus, a significant fraction of nuclear trafficking is used in the production of ribosomes.

Ribosomes are made of a small (‘2’ in Figure) and a large subunit (‘1’ in Figure)

Large (1) and small (2) subunit fit together (note this figure mislabels angstroms as nanometers)

Transcriptional RegulationIntegrated View

Expression..

mRNA transcription

TF binding site

TATA

TF

Pol II

DNA

Epigenectics – Epigenomics: Gene Expression

Transcription factors (TF) are essential for transcription initialisation

Transcription is done by polymerase type II (eukaryotes)

mRNA must then move from nucleus to ribosomes (extranuclear) for translation

In eukaryotes there can be many TF-binding sites upstream of an ORF that together regulate transcription

Nucleosomes (chromatin structures composed of histones) are structures round of which DNA coils. This blocks access of TFs

Epigenectics – Epigenomics: Gene Expression

mRNA transcription

TF binding site (open)

TF binding site (closed)

TATA

Nucleosome

Expression Because DNA has flexibility, bound TFs can move in order to

interact with pol II, which is necessary for transcription initiation (see next slide)

Recent TF-based initialisation theory includes a wave function (Carlsberg) of TF-binding, which is supposed to go from left to right. In this way the TF-binding site nearest to the TATA box would be bound by a TF which will then in turn bind Pol II.

It has been suggested that “Speckles” have something to do with this (speckels are observed protein plaques in the nucleus)

Current prediction methods for gene co-expression, e.g. finding a single shared TF binding site, do not take this TF cooperativity into account (“parking lot optimisation”)

434 Cro protein complex(phage)

PDB: 3CRO

Zinc finger DNA recognition

(Drosophila) PDB: 2DRP

..YRCKVCSRVY THISNFCRHY VTSH...

Characteristics of the family:

Function: The DNA-binding motif is found as part of transcription regulatory proteins.

Structure: One of the most abundant DNA-binding motifs. Proteins may contain more than one finger in a single chain. For example Transcription Factor TF3A was the first zinc-finger protein discovered to contain 9 C2H2 zinc-finger motifs (tandem repeats). Each motif consists of 2 antiparallel beta-strands followed by by an alpha-helix. A single zinc ion is tetrahedrally coordinated by conserved histidine and cysteine residues, stabilising the motif.

Zinc-finger DNA binding protein family

Binding: Fingers bind to 3 base-pair subsites and specific contacts are mediated by amino acids in positions -1, 2, 3 and 6 relative to the start of the alpha-helix.

Contacts mainly involve one strand of the DNA.

Where proteins contain multiple fingers, each finger binds to adjacent subsites within a larger DNA recognition site thus allowing a relatively simple motif to specifically bind to a wide range of DNA sequences.

This means that the number and the type of zinc fingers dictates the specificity of binding to DNA

Characteristics of the family:

Zinc-finger DNA binding protein family

Leucine zipper(yeast)

PDB: 1YSA

..RA RKLQRMKQLE DKVEE LLSKN YHLENEVARL...

A protein sequence alignmentMSTGAVLIY--TSILIKECHAMPAGNE--------GGILLFHRTHELIKESHAMANDEGGSNNS * * * **** ***

A DNA sequence alignmentattcgttggcaaatcgcccctatccggccttaaatt---tggcggatcg-cctctacgggcc----*** **** **** ** ******

Searching for similarities

What is the function of the new gene?

The “lazy” investigation (i.e., no biologial experiments, just bioinformatics techniques):

– Find a set of similar protein sequences to the unknown sequence

– Identify similarities and differences

– For long proteins: first identify domains

Intermezzo: what is a domainA domain is a:

• Compact, semi-independent unit (Richardson, 1981).

• Stable unit of a protein structure that can fold autonomously (Wetlaufer, 1973).

• Recurring functional and evolutionary module (Bork, 1992).

“Nature is a ‘tinkerer’ and not an inventor” (Jacob, 1977).

The DEATH Domain (DD)• Present in a variety of Eukaryotic proteins involved with cell death.• Six helices enclose a tightly packed hydrophobic core.• Some DEATH domains form homotypic and heterotypic dimers.

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Protein domains recur in different combinations

Pyruvate kinasePhosphotransferase

barrel regulatory domain

barrel catalytic substrate binding domain

nucleotide binding domain

1 continuous + 2 discontinuous domains

Structural domain organisation can intricate…

Evolutionary and functional relationships

Reconstruct evolutionary relation:

•Based on sequence-Identity (simplest method)-Similarity

•Homology (common ancestry: the ultimate goal)•Other (e.g., 3D structure)

Functional relation:Sequence Structure Function

Common ancestry is more interesting:Makes it more likely that genes sharethe same function

Homology: sharing a common ancestor– a binary property (yes/no)– it’s a nice tool:When (an unknown) gene X is homologous to (a known) gene G it means that we gain a lot of information on X: what we know about G can be transferred to X as a good suggestion.

Searching for similarities

The deluge of genomic information begs the following question: what do all these genes do?

Many genes are not annotated, and many more are partially or erroneously annotated. Given a genome which is partially annotated at best, how do we fill in the blanks?

Of each sequenced genome, 20%-50% of the functions of proteins encoded by the genomes remains unknown!

Protein Function Prediction

We are faced with the problem of predicting protein function from sequence, genomic, expression, interaction and structural data. For all these reasons and many more, automated protein function prediction is rapidly gaining interest among bioinformaticians and computational biologists

Protein Function Prediction

Ways to predict function Sequence-based function prediction

Structure-based function prediction– Sequence-structure comparison– Structure-structure comparison

Motif-based function prediction

Phylogenetic profile analysis

Protein interaction prediction and databases

Functional inference at systems level

Classes of function prediction methods Sequence based approaches

– protein A has function X, and protein B is a homolog (ortholog) of protein A; Hence B has function X

Structure-based approaches– protein A has structure X, and X has so-so structural features;

Hence A’s function sites are ….

Motif-based approaches– a group of genes have function X and they all have motif Y; protein

A has motif Y; Hence protein A’s function might be related to X

Function prediction based on “guilt-by-association”– gene A has function X and gene B is often “associated” with gene A,

B might have function related to X

Sequence-based function prediction Homology searching Sequence comparison is a powerful tool for detection

of homologous genes but limited to genomes that are not too distant away

uery: 2 LSDGEWQLVLNVWGKVEADIPGHGQEVLIRLFKGHPETLEKFDKFKHLKSEDEMKASEDL 61 LSD + V +W K+ G + L R+ +P+T F + D S ++Sbjct: 3 LSDKDKAAVRALWSKIGKSSDAIGNDALSRMIVVYPQTKIYFSHWP-----DVTPGSPNI 57

Query: 62 KKHGATVLTALGGILKKKGHHEAEIKPLAQSHATKHKIPVKYLEFISECIIQVLQSKHPG 121 K HG V+ + + K + + L++ HA K ++ + ++ CI+ V+ + PSbjct: 58 KAHGKKVMGGIALAVSKIDDLKTGLMELSEQHAYKLRVDPSNFKILNHCILVVISTMFPK 117

Query: 122 DFGADAQGAMNKALELFRKDMASNYK 147 +F +A +++K L +A Y+Sbjct: 118 EFTPEAHVSLDKFLSGVALALAERYR 143

We have done homology searching (FASTA, BLAST, PSI-BLAST) in earlier lectures

Structure-based function prediction

Structure-based methods could possibly detect remote homologues that are not detectable by sequence-based method– using structural information in addition to sequence

information– protein threading (sequence-structure alignment) is a

popular method

Structure-based methods could provide more than just “homology” information

Threading

Query sequence

Template sequence

+

Template structure

Compatibility score

Threading

Query sequence

Template sequence

+

Template structure

Compatibility score


Threading Scoring function for measuring to what extend query sequence fits into template structure

For scoring we have to map an amino acid (query sequence) onto a local environment (template structure)

We can use the following structural features for scoring:

o Secondary structure

o Is environment inside or outside? – Residue accessible surface area (ASA)

o Polarity of environment

The best (highest scoring) “thread” through the structure gives a so-called structural alignment, this looks exactly the same as a sequence alignment but is based on structure.

Threading – inverse foldingMap sequence to structural environments

N C

?Query Template

What is the optimal thread for each local environment?

Find the best compromise over all environments

hydrophobic

hydrophilic

environment•Secondary structure

•ASA

•Polarity of environment

Fold recognition by threading

Query sequence

Compatibility scores

Fold 1

Fold 2

Fold 3

Fold N

What is the most compatible structure?The one with the highest threading score

Structure-based function prediction SCOP (http://scop.berkeley.edu/) is a protein structure

classification database where proteins are grouped into a hierarchy of families, superfamilies, folds and classes, based on their structural and functional similarities

Structure-based function prediction SCOP hierarchy – the top level: 11 classes


All-alpha protein

Coiled-coil proteinAll-beta protein

Alpha-beta proteinmembrane protein

Structure-based function prediction SCOP hierarchy – the second level: 800 folds

Structure-based function prediction SCOP hierarchy - third level: 1294 superfamilies


SCOP hierarchy - third level: 2327 families


Using sequence-structure alignment method, one can predict a protein belongs to a

– SCOP family, superfamily or fold

Proteins predicted to be in the same SCOP family are orthologous Proteins predicted to be in the same SCOPE superfamily are homologous Proteins predicted to be in the same SCOP fold are structurally

analogous

folds

superfamilies

families


Prediction of ligand binding sites– For ~85% of ligand-binding proteins, the largest largest cleft

is the ligand-binding site– For additional ~10% of ligand-binding proteins, the second

largest cleft is the ligand-binding site


Prediction of macromolecular binding site– there is a strong correlation between macromolecular

binding site (with protein, DNA and RNA) and disordered protein regions

– disordered regions in a protein sequence can be predicted using computational methods

http://www.pondr.com/


Prediction of protein functions based on identified sequence motifs

PROSITE contains patterns specific for more than a thousand protein families.

ScanPROSITE -- it allows to scan a protein sequence for occurrence of patterns and profiles stored in PROSITE


Search PROSITE using ScanPROSITE

The sequence has ASN_GLYCOSYLATION N-glycosylation site: 242 - 245 NETL

MSEGSDNNGDPQQQGAEGEAVGENKMKSRLRKGALKKKNVFNVKDHCFIARFFKQPTFCSHCKDFICGYQSGYAWMGFGKQGFQCQVCSYVVHKRCHEYVTFICPGKDKGNETLIDSDSPKTQH ……..

Regular expressions

Alignment

ADLGAVFALCDRYFQSDVGPRSCFCERFYQADLGRTQNRCDRYYQADIGQPHSLCERYFQ

Regular expression

[AS]-D-[IVL]-G-x4-{PG}-C-[DE]-R-[FY]2-Q

{PG} = not (P or G)

For short sequence stretches, regular expressions are often more suitable to describe the information than alignments (or profiles)

Regular expressions

Regular expression No. of exact matches in DB

D-A-V-I-D 71

D-A-V-I-[DENQ] 252

[DENQ]-A-V-I-[DENQ] 925

[DENQ]-A-[VLI]-I-[DENQ] 2739

[DENQ]-[AG]-[VLI]2-[DENQ] 51506

D-A-V-E 1088

Prosite

In addition to regular expressions, the Prosite database also contains so-called extended profiles

Extended profiles contain more explicit information than classical profiles, for example to describe expected gap lengths, etc.

This is because some patterns are better described using regular expressions (e.g. short motifs), while others are better formalised using (extended) profiles

Domain swappingDomain swapping is a structurally viable mechanism for forming oligomeric assemblies (Bennett et al., 1995). In domain swapping, a secondary or tertiary element of a monomeric protein is replaced by the same element of another protein.

Domain swapping can range from secondary structure elements to whole structural domains. It also represents a model of evolution for functional adaptation by oligomerization, e.g. of oligomeric enzymes that have their active site at sub-unit interfaces (Heringa and Taylor, 1997).

Domain databases

COGS Domain databaseThe COGs (Clusters of Orthologous Groups) database is a phylogenetic classification of the proteins encoded within complete genomes (Tatusov et al., 2001).

It primarily consists of bacterial and archaeal genomes.

Operational definition of orthology is based on bidirectional best hit

Incorporation of the larger genomes of multicellular eukaryotes into the COG system is achieved by identifying eukaryotic proteins that fit into already existing COGs. Eukaryotic proteins that have orthologs within different COGs are split into their individual domains.

The COGs database currently consists of 3166 COGs including 75,725 proteins from 44 genomes.

COGs: the beginning (1997)

In order to extract the maximum amount of information from the rapidly accumulating genome sequences, all conserved genes need to be classified according to their homologous relationships. Comparison of proteins encoded in seven complete genomes from five major phylogenetic lineages and elucidation of consistent patterns of sequence similarities allowed the delineation of 720 clusters of orthologous groups (COGs). Each COG consists of individual orthologous proteins or orthologous sets of paralogs from at least three lineages. Orthologs typically have the same function, allowing transfer of functional information from one member to an entire COG. This relation automatically yields a number of functional predictions for poorly characterized genomes. The COGs comprise a framework for functional and evolutionary genome analysis.

COG2813: 16S RNA G1207 methylase RsmC

COG members are mapped onto the genomes included in the DB

PRINTS database•PRINTS is a compendium of protein fingerprints.

•A fingerprint is a group of conserved motifs used to characterise a protein family; its diagnostic power (false positives and false negatives) is refined by iterative scanning of a SWISS-PROT/TrEMBL composite database.

•Usually the motifs do not overlap, but are separated along a sequence, though they may be contiguous in 3D-space.

•Fingerprints can encode protein folds and functionalities more flexibly and powerfully than can single motifs, full diagnostic potency deriving from the mutual context provided by motif neighbours

•PRINTS contains the most discriminating groups of regular expressions for each protein sequence

•Release 31.0 of PRINTS contains 1,550 entries, encoding 9,531 individual motifs.

INITIAL MOTIF SETS

BETAHAEM1 Length of motif = 17 Motif number = 1Beta haemoglobin motif I - 1 PCODE ST INT GRLLVVYPWTQRYFDSF HBB1_RAT 29 29 GRLLVVYPWTQRYFDSF HBB1_MOUSE 29 29 GRLLVVYPWTQRFFEHF HBB_ALCAA 28 28 GRLLVVYPWTQRFFEHF HBB_ODOVI 28 28 GRLLVVYPWTQRFFESF HBB_BOVIN 28 28 GRLLVVYPWTQRFFESF HBB_ATEGE 29 29 GRLLVVYPWTQRFFESF HBB_HUMAN 29 29 GRLLVVYPWTQRFFESF HBB_ANTPA 29 29 ARLLIVYPWTQRFFASF HBB_ANAPL 29 29 SRCLIVYPWTQRHFSGF HBB_NOTAN 29 29

BETAHAEM2 Length of motif = 16 Motif number = 2 Beta haemoglobin motif II - 1 PCODE ST INT DLSSASAIMGNPKVKA HBB1_RAT 47 1 DLSSASAIMGNAKVKA HBB1_MOUSE 47 1 DLSTADAVMHNAKVKE HBB_ALCAA 46 1 DLSSAGAVMGNPKVKA HBB_ODOVI 46 1 DLSTADAVMNNPKVKA HBB_BOVIN 46 1 DLSTPDAVMSNPKVKA HBB_ATEGE 47 1 DLSTPDAVMGNPKVKA HBB_HUMAN 47 1 DLSNAGAVMGNAKVKA HBB_ANTPA 47 1 NLSSPTAILGNPMVRA HBB_ANAPL 47 1 NLYNAEAILGNANVAA HBB_NOTAN 47 1

BETAHEAM: 2 of 5 PRINTS motifs making the fingerprint

After iteration the number of sequences for each motif can grow dramatically. Both the initial motifs (example here) and final motifs are provided to the user

The PRODOM Database

ProDom is a comprehensive set of protein domain families automatically generated from the SWISS-PROT and TrEMBL sequence databases

The PRODOM Database

ProDom (Corpet et al., 2000) is a database of protein domain families automatically generated from SWISSPROT and TrEMBL sequence databases (Bairoch and Apweiler, 2000) using a novel procedure based on recursive PSI-BLAST searches (Altschul et al., 1997). Release 2001.2 of ProDom contains 283,772 domain families, 101,957 having at least 2 sequence members. ProDom-CG (Complete Genome) is a version of the ProDom database which holds genome-specific domain data.

The PROSITE Database

PROSITE is a database of protein families and domains. It consists of biologically significant sites, patterns and profiles that help to reliably identify to which known protein family (if any) a new sequence belongs

PROSITE (Hofmann et al., 1999) is a good source of high quality annotation for protein domain families. A PROSITE sequence family is represented as a pattern or profile, providing a means of sensitive detection of common protein domains in new protein sequences.

PROSITE release 16.46 contains signatures specific for 1,098 protein families or domains. Each of these signatures comes with documentation providing background information on the structure and function of these proteins.

The PROSITE Database

A PROSITE sequence family is represented as a pattern or a profile.

A pattern is given as a regular expression (next slide)

The generalised profiles used in PROSITE carry the same increased information as compared to classical profiles as Hidden Markov Models (HMMs).

Regular expressions

Alignment

ADLGAVFALCDRYFQSDVGPRSCFCERFYQADLGRTQNRCDRYYQADIGQPHSLCERYFQ

Regular expression

[AS]-D-[IVL]-G-x4-{PG}-C-[DE]-R-[FY]2-Q

{PG} = not (P or G)

For short sequence stretches, regular expressions are often more suitable to describe the information than alignments (or profiles)

Regular expressions

Regular expression No. of exact matches in DB

D-A-V-I-D 71

D-A-V-I-[DENQ] 252

[DENQ]-A-V-I-[DENQ] 925

[DENQ]-A-[VLI]-I-[DENQ] 2739

[DENQ]-[AG]-[VLI]2-[DENQ] 51506

D-A-V-E 1088

Rationale for regular expressions “I want to see all sequences that ...

– ... contain a C” --- C

– ... contain a C or an F” -- [CF]

– ... contain a C and an F” -- (C.*F | F.*C) (‘|’ means ‘or’ and ‘.*’ means don’t care for any length)

– ... contain a C immediately followed by an F” -- CF

– ... contain a C later followed by an F” -- C.*F

– ... begin with a C” -- ^C (‘^’ means ‘starting with’)

– ... do not contain a C” -- {C}

– ... contain at least three Cs” -- C3-

– ... contain exactly three Cs” -- C3

– ... has a C at the seventh position” -- .6C

– ... either contain a C, an E, and an F in any order except CFE, unless there are also at most three Ps, or there is a ....

Regex limitations regex cannot remember indeterminate counts !!!

– “I want to see all sequences with ...☺ ... six Cs followed by six Ts”

– C6T6

☺ ... any number of Cs followed by any number of Ts”✰ C*T*

☹ ... Cs followed by an equal number of Ts” (This cannot be done..)✰ CnTn

✰ (CT|CCTT|CCCTTT|C4T4| ... )?

The PFAM Database

Pfam is a large collection of multiple sequence alignments and hidden Markov models covering many common protein domains and families. For each family in Pfam you can:

Look at multiple alignments View protein domain architectures Examine species distribution Follow links to other databases View known protein structures Search with Hidden Markov Model (HMM) for each alignment

The PFAM Database

Pfam is a database of two parts, the first is the curated part of Pfam containing over 5193 protein families (Pfam-A). Pfam-A comprises manually crafted multiplealignments and profile-HMMs . To give Pfam a more comprehensive coverage of known proteins we automatically generate a supplement called Pfam-B. This contains a large number of small families taken from the PRODOM database that do not overlap with Pfam-A. Although of lower quality Pfam-B families can be useful when no Pfam-A families are found.

The PFAM Database

Sequence coverage Pfam-A : 73% (Gr)Sequence coverage Pfam-B : 20% (Bl)Other (Grey)

CYB_TRYBB/1-197 M...LYKSG..EKRKG..LLMSGC.....LYR.....IYGVGFSLGFFIALQIIC..GVCLAWLFFSCFICSNWYFVLFLCYB_MARPO/1-208 M.ARRLSILKQPIFSTFNNHLIDY.....PTPSNISYWWGFGSLAGLCLVIQILTGVFLAMHYTPHVDLAFLSVEHIMR.CYB_HETFR/1-205 MATNIRKTH..PLLKIINHALVDL.....PAPSNISAWWNFGSLLVLCLAVQILTGLFLAMHYTADISLAFSSVIHICR.CYB_STELO/1-204 M.TNIRKTH..PLMKILNDAFIDL.....PTPSNISSWWNFGSLLGLCLIMQILTGLFLAMHYTPDTTTAFSSVAHICR.CYB_ASCSU/1-196 ...........MKLDFVNSMVVSL.....PSSKVLTYGWNFGSMLGMVLGFQILTGTFLAFYYSNDGALAFLSVQYIMY.CYB6_SPIOL/1-210 M.SKVYDWF..EERLEIQAIADDITSKYVPPHVNIFYCLGGITLT..CFLVQVATGFAMTFYYRPTVTDAFASVQYIMT.CYB6_MARPO/1-210 M.GKVYDWF..EERLEIQAIADDITSKYVPPHVNIFYCLGGITLT..CFLVQVATGFAMTFYYRPTVTEAFSSVQYIMT.CYB6_EUGGR/1-210 M.SRVYDWF..EERLEIQAIADDVSSKYVPPHVNIFYCLGGITFT..CFIIQVATGFAMTFYYRPTVTEAFLSVKYIMN.

CYB_TRYBB/1-197 WDFDLGFVIRSVHICFTSLLYLLLYIHIFKSITLIILFDTH..IL....VWFIGFILFVFIIIIAFIGYVLPCTMMSYWGCYB_MARPO/1-208 .DVKGGWLLRYMHANGASMFFIVVYLHFFRGLY....YGSY..ASPRELVWCLGVVILLLMIVTAFIGYVLPWGQMSFWGCYB_HETFR/1-205 .DVNYGWLIRNIHANGASLFFICIYLHIARGLY....YGSY..LLKE..TWNIGVILLFLLMATAFVGYVLPWGQMSFWGCYB_STELO/1-204 .DVNYGWFIRYLHANGASMFFICLYAHMGRGLY....YGSY..MFQE..TWNIGVLLLLTVMATAFVGYVLPWGQMSFWGCYB_ASCSU/1-196 .EVNFGWIFRVLHFNGASLFFIFLYLHLFKGLF....FMSY..RLKK..VWVSGIVILLLVMMEAFMGYVLVWAQMSFWACYB6_SPIOL/1-210 .EVNFGWLIRSVHRWSASMMVLMMILHVFRVYL....TGGFKKPREL..TWVTGVVLGVLTASFGVTGYSLPWDQIGYWACYB6_MARPO/1-210 .EVNFGWLIRSVHRWSASMMVLMMILHIFRVYL....TGGFKKPREL..TWVTGVILAVLTVSFGVTGYSLPWDQIGYWACYB6_EUGGR/1-210 .EVNFGWLIRSIHRWSASMMVLMMILHVCRVYL....TGGFKKPREL..TWVTGIILAILTVSFGVTGYSLPWDQVGYWA

CYB_TRYBB/1-197 LTVFSNIIATVPILGIWLCYWIWGSEFINDFTLLKLHVLHV.LLPFILLIILILHLFCLHYFMCYB_MARPO/1-208 ATVITSLASAIPVVGDTIVTWLWGGFSVDNATLNRFFSLHY.LLPFIIAGASILHLAALHQYGCYB_HETFR/1-205 ATVITNLLSAFPYIGDTLVQWIWGGFSIDNATLTRFFAFHF.LLPFLIIALTMLHFLFLHETGCYB_STELO/1-204 ATVITNLLSAIPYIGTTLVEWIWGGFSVDKATLTRFFAFHF.ILPFIITALAAVHLLFLHETGCYB_ASCSU/1-196 SVVITSLLSVIPVWGFAIVTWIWSGFTVSSATLKFFFVLHF.LVPWGLLLLVLLHLVFLHETGCYB6_SPIOL/1-210 VKIVTGVPDAIPVIGSPLVELLRGSASVGQSTLTRFYSLHTFVLPLLTAVFMLMHFLMIRKQGCYB6_MARPO/1-210 VKIVTGVPEAIPIIGSPLVELLRGSVSVGQSTLTRFYSLHTFVLPLLTAIFMLMHFLMIRKQGCYB6_EUGGR/1-210 VKIVTGVPEAIPLIGNFIVELLRGSVSVGQSTLTRFYSLHTFVLPLLTATFMLGHFLMIRKQG

A PFAM alignment

INTERPRO combined database

Because the underlying construction and analysis methods of the above domain family databases are different, the databases inevitably have different diagnostic strengths and weaknesses.

The InterPro database (Apweiler et al., 2000) is a collaboration between many of the domain database curators.

It aims to be a central resource reducing the amount of duplication between the databases.

Release 3.2 of InterPro contains 3,939 entries, representing 1,009 domains, 2,850 families, 65 repeats and 15 posttranslational modification sites. Entries are accompanied by regular expressions, profiles, fingerprints and Hidden Markov Models which facilitate sequence database searches.

Databases integrated in INTERPRO:

The UniProt (Universal Protein Resource) is the world's most comprehensive catalog of information on proteins. It is a central repository of protein sequence and function created by joining the information contained in Swiss-Prot, TrEMBL, and PIR.

PROSITE is a database of protein families and domains. It consists of biologically significant sites, patterns and profiles that help to reliably identify to which known protein family (if any) a new sequence belongs.

Pfam is a large collection of multiple sequence alignments and hidden Markov models covering many common protein domains.

PRINTS is a compendium of protein fingerprints. A fingerprint is a group of conserved motifs used to characterise a protein family; its diagnostic power is refined by iterative scanning of UniProt. Usually the motifs do not overlap, but are separated along a sequence, though they may be contiguous in 3D-space. Fingerprints can encode protein folds and functionalities more flexibly and powerfully than can single motifs, their full diagnostic potency deriving from the mutual context afforded by motif neighbours.

The ProDom protein domain database consists of an automatic compilation of homologous domains. Current versions of ProDom are built using a novel procedure based on recursive PSI-BLAST searches (Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W & Lipman DJ, 1997, Nucleic Acids Res., 25:3389-3402; Gouzy J., Corpet F. & Kahn D., 1999, Computers and Chemistry 23:333-340.) Large families are much better processed with this new procedure than with the former DOMAINER program (Sonnhammer, E.L.L. & Kahn, D., 1994, Protein Sci., 3:482-492).

Databases integrated in INTERPRO (Cont.):

SMART (a Simple Modular Architecture Research Tool) allows the identification and annotation of genetically mobile domains and the analysis of domain architectures. More than 500 domain families found in signalling, extracellular and chromatin-associated proteins are detectable. These domains are extensively annotated with respect to phyletic distributions, functional class, tertiary structures and functionally important residues. Each domain found in a non-redundant protein database as well as search parameters and taxonomic information are stored in a relational database system. User interfaces to this database allow searches for proteins containing specific combinations of domains in defined taxa.

TIGRFAMs is a collection of protein families, featuring curated multiple sequence alignments, Hidden Markov Models (HMMs) and annotation, which provides a tool for identifying functionally related proteins based on sequence homology. Those entries which are "equivalogs" group homologous proteins which are conserved with respect to function.

PIR Superfamily (PIRSF) is a classification system based on evolutionary relationship of whole proteins. Members of a superfamily are monophyletic (evolved from a common evolutionary ancestor) and homeomorphic (homologous over the full-length sequence and sharing a common domain architecture). A protein may be assigned to one and only one superfamily. Curated superfamilies contain functional information, domain information, bibliography, and cross-references to other databases, as well as full-length and domain HMMs, multiple sequence alignments, and phylogenetic tree of seed members. PIRSF can be used for functional annotation of protein sequences.

SUPERFAMILY is a library of profile hidden Markov models that represent all proteins of known structure. The library is based on the SCOP classification of proteins: each model corresponds to a SCOP domain and aims to represent the entire SCOP superfamily that the domain belongs to. SUPERFAMILY has been used to carry out structural assignments to all completely sequenced genomes. The results and analysis are available from the SUPERFAMILY website.

Domain structure databases

Several methods of structural classification have been developed to classify the large number of protein folds present in the PDB.

The most widely used and comprehensive databases are CATH, 3Dee, FSSP and SCOP, which use four unique methods to classify protein structures at the domain level.

Examples of domain structure databases

CATH 3DEE FSSP SCOP

CATH

The CATH domain database assigns domains based on a consensus approach using the three algorithms PUU (Holm and Sander, 1994), DETECTIVE (Swindells, 1995) and DOMAK (Siddiqui and Barton, 1995) as well as visual inspection (Jones et al., 1998). The CATH database release 2.3 contains approximately 30,000 domains ordered into five major levels: Class; Architecture; Topology/fold; Homologous superfamily; and Sequence family.

CATH

Class covers , , and / proteinsArchitecture is the overall shape of a domain as defined by the packing of secondary structural elements, but ignoring their connectivity. The topology-level consists of structures with the same number, arrangement and connectivity of secondary structure based on structural superposition using SSAP structure comparison algorithm (Taylor and Orengo, 1989). A homologous superfamily contains proteins having high structural similarity and similar functions, which suggests that they have evolved from a common ancestor. Finally, the sequence family level consists of proteins with sequence identities greater than 35%, again suggesting a common ancestor.

CATH

CATH classifies domains into approximately 700 fold families; ten of these folds are highly populated and are referred to as ‘super-folds’.

Super-folds are defined as folds for which there are at least three structures without significant sequence similarity (Orengo et al., 1994).

The most populated is the / -barrel super-fold.

3Dee

3Dee structural domain repository (Siddiqui et al., 2001) stores alternative domain definitions for the same protein and organises the domains into sequence and structural hierarchies. Most of the database creation and update processes are performed automatically using the DOMAK (Siddiqui and Barton, 1995) algorithm. However, somedomains are manually assigned. It contains non-redundant sets of sequences and structures, multiple structure alignments for all domain families, secondary structure and fold name definitions. The current 3Dee release is now a few years old and contains 18,896 structural domains.

FSSP

FSSP (Holm and Sander, 1997) is a complete comparison of all pairs of protein structures in the PDB. It is the basis for the Dali Domain Dictionary (Dietmann et al., 2001), a numerical taxonomy of all known structures in the PDB.

The taxonomy is derived automatically from measurements of structural, functional and sequence similarities.

The database is split into four hierarchical levels corresponding to super-secondary structural motifs, the topology of globular domains, remote homologues (functional families) and sequence families.

FSSP

The top level of the fold classification corresponds to secondary structure composition and super-secondary structural motifs. Domains are assigned by the PUU algorithm (Holm and Sander, 1994) and classified into one of five ‘attractors’, which can be characterised as all-, all-, / , - meander, and antiparallel -barrels. Domains which are not clearly defined to a single attractor are assigned to a mixed class.

In September 2000, the Dali classification contained 17,101 chains, 1,375 fold types and 3,724 domain sequence families. The database contains definitions of structurally conserved cores and a library of multiple alignments of distantly related protein families.

SCOPThe SCOP database (Structural Classification of Proteins) is a manual classification of protein structure (Murzin et al., 1995). The classification is at the domain level for many proteins, but in general, a protein is only split into domains when there is a clear indication that the individual domains may have existed as independent proteins.

Therefore, many of the domain definitions in SCOP will be different to those in the other structural domain databases. The principal levels of hierarchy are family, superfamily and fold, split into the traditional four domain classes, all-, all-, + and / .

Release1.55 of the SCOP database contains 13,220 PDB entries, 605 fold types and 31,474 domains.

Gene Ontology (GO) Not a genome sequence database Developing three structured, controlled

vocabularies (ontologies) to describe gene products in terms of:– biological process– cellular component– molecular function

in a species-independent manner

The GO ontology

Gene Ontology MembersFlyBase - database for the fruitfly Drosophila melanogaster Berkeley Drosophila Genome Project (BDGP) - Drosophila informatics; GO database & software, Sequence Ontology development

Saccharomyces Genome Database (SGD) - database for the budding yeast Saccharomyces cerevisiae Mouse Genome Database (MGD) & Gene Expression Database (GXD) - databases for the mouse Mus musculus

The Arabidopsis Information Resource (TAIR) - database for the brassica family plant Arabidopsis thaliana

WormBase - database for the nematode Caenorhabditis elegans EBI GOA project : annotation of UniProt (Swiss-Prot/TrEMBL/PIR) and InterPro databases Rat Genome Database (RGD) - database for the rat Rattus norvegicus DictyBase - informatics resource for the slime mold Dictyostelium discoideum GeneDB S. pombe - database for the fission yeast Schizosaccharomyces pombe (part of the Pathogen Sequencing Unit at the Wellcome Trust Sanger Institute)

GeneDB for protozoa - databases for Plasmodium falciparum, Leishmania major, Trypanosoma brucei, and several other protozoan parasites (part of the Pathogen Sequencing Unit at the Wellcome Trust Sanger Institute)

Genome Knowledge Base (GK) - a collaboration between Cold Spring Harbor Laboratory and EBI) TIGR - The Institute for Genomic Research Gramene - A Comparative Mapping Resource for Monocots Compugen (with its Internet Research Engine) The Zebrafish Information Network (ZFIN) - reference datasets and information on Danio rerio

Protein interaction database There are numerous databases of protein-protein

interactions

DIP is a popular protein-protein interaction database

The DIP database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions.

Protein interaction databases

BIND - Biomolecular Interaction Network DatabaseDIP - Database of Interacting ProteinsPIM – HybrigenicsPathCalling Yeast Interaction Database MINT - a Molecular Interactions DatabaseGRID - The General Repository for Interaction DatasetsInterPreTS - protein interaction prediction through tertiary structureSTRING - predicted functional associations among genes/proteinsMammalian protein-protein interaction database (PPI)InterDom - database of putative interacting protein domains FusionDB - database of bacterial and archaeal gene fusion eventsIntAct ProjectThe Human Protein Interaction Database (HPID)ADVICE - Automated Detection and Validation of Interaction by Co-evolutionInterWeaver - protein interaction reports with online evidencePathBLAST - alignment of protein interaction networksClusPro - a fully automated algorithm for protein-protein dockingHPRD - Human Protein Reference Database

Protein interaction database

Network of protein interactions and predicted functional links involving silencing information regulator (SIR) proteins. Filled circles represent proteins of known function; open circles represent proteins of unknown function, represented only by their Saccharomyces genome sequence numbers ( http://genome-www.stanford.edu/Saccharomyces). Solid lines show experimentally determined interactions, as summarized in the Database of Interacting Proteins19 (http://dip.doe-mbi.ucla.edu). Dashed lines show functional links predicted by the Rosetta Stone method12. Dotted lines show functional links predicted by phylogenetic profiles16. Some predicted links are omitted for clarity.

Network of predicted functional linkages involving the yeast prion protein20 Sup35. The dashed line shows the only experimentally determined interaction. The other functional links were calculated from genome and expression data11 by a combination of methods, including phylogenetic profiles, Rosetta stone linkages and mRNA expression. Linkages predicted by more than one method, and hence particularly reliable, are shown by heavy lines. Adapted from ref. 11.

STRING - predicted functional associations among genes/proteins

STRING is a database of predicted functional associations among genes/proteins.

Genes of similar function tend to be maintained in close neighborhood, tend to be present or absent together, i.e. to have the same phylogenetic occurrence, and can sometimes be found fused into a single gene encoding a combined polypeptide.

STRING integrates this information from as many genomes as possible to predict functional links between proteins.

Berend Snel en Martijn Huynen (RUN) and the group of Peer Bork (EMBL, Heidelberg)

STRING - predicted functional associations among genes/proteins STRING is a database of known and predicted protein-protein interactions.The interactions include direct (physical) and indirect (functional) associations; they are derived from four sources:

1. Genomic Context (Synteny) 2. High-throughput Experiments 3. (Conserved) Co-expression 4. Previous Knowledge

STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently contains 736429 proteins in 179 species

STRING - predicted functional associations among genes/proteins

Conserved Neighborhood

This view shows runs of genes that occur repeatedly in close neighborhood in (prokaryotic) genomes. Genes located together in a run are linked with a black line (maximum allowed intergenic distance is 300 bp). Note that if there are multiple runs for a given species, these are separated by white space. If there are other genes in the run that are below the current score threshold, they are drawn as small white triangles. Gene fusion occurences are also drawn, but only if they are present in a run (see also the Fusion section below for more details).


Function prediction of individual genes could be made in the context of biological pathways/networks

Example – phoB is predicted to be a transcription regulator and it regulates all the genes in the pho-regulon (a group of co-regulated operons); and within this regulon, gene A is interacting with gene B, etc.


KEGG is database of biological pathways and networks




By doing homologous search, one can map a known biological pathway in one organism to another one; hence predict gene functions in the context of biological pathways/networks

Wrapping up

We have seen a number of ways to infer a putative function for a protein sequence

To gain confidence, it is important to combine as many different prediction protocols as possible (the STRING server is an example of this)

Homework

Give an example of two proteins having the same structural fold but different biological functions through searching SCOP and Swiss-prot

What is the biological function of phoR in the two-component system of prokaryotic organism based on KEGG database search

Protein function

Lecture 17:

Introduction to BioinformaticsIntroduction to Bioinformatics

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Domain fusionFor example, vertebrates have a multi-enzyme protein (GARs-AIRs-GARt) comprising the enzymes GAR synthetase (GARs), AIR synthetase (AIRs), and GAR transformylase (GARt) 1.

In insects, the polypeptide appears as GARs-(AIRs)2-GARt. However, GARs-AIRs is encoded separately from GARt in yeast, and in bacteria each domain is encoded separately (Henikoff et al., 1997).

1GAR: glycinamide ribonucleotide synthetase AIR: aminoimidazole ribonucleotide synthetase

Genetic mechanisms influencing the layout of multidomain proteins include gross rearrangements such as inversions, translocations, deletions and duplications, homologous recombination, and slippage of DNA polymerase during replication (Bork et al., 1992).

Although genetically conceivable, the transition from two single domain proteins to a multidomain protein requires that both domains fold correctly and that they accomplish to bury a fraction of the previously solvent-exposed surface area in a newly generated inter-domain surface.

Domain fusion

Pathways and Pathway Diagrams Pathways

– Set of nodes (entities) and edges (associations)

Pathway Diagrams– XY coordinates– Node splitting allowed– Multiple views of the

same pathway– Different abstraction

levels

Kegg database (Japan)

Metabolic Metabolic networksnetworks

Glycolysis Glycolysis and and

GluconeogenesisGluconeogenesis

Domains, their prediction and domain databases

Lecture 16:

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Sequence

Structure

Function

Threading

Homology searching (BLAST)

Ab initio prediction and folding

Function prediction from structure

Sequence-Structure-Function

impossible but for the smallest structures

very difficult

TERTIARY STRUCTURE (fold)TERTIARY STRUCTURE (fold)

Genome

Expressome

Proteome

Metabolome

Functional Genomics – Systems Functional Genomics – Systems BiologyBiology

Metabolomics

fluxomics

Systems Biology

is the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behaviour of that system (for example, the enzymes and metabolites in a metabolic pathway). The aim is to quantitatively understand the system and to be able to predict the system’s time processes

the interactions are nonlinear the interactions give rise to emergent properties, i.e. properties

that cannot be explained by the components in the system Biological processes include many time-scales, many

compartments and many interconnected network levels (e.g. regulation, signalling, expression,..)

Systems Biology

understanding is often achieved through modeling and simulation of the system’s components and interactions.

Many times, the ‘four Ms’ cycle is adopted:

Measuring

Mining

Modeling

Manipulating

‘The silicon cell’

(some people think ‘silly-con’ cell)

A system response

Apoptosis: programmed cell death

Necrosis: accidental cell death

This pathway diagram shows a comparison of pathways in (left) Homo sapiens (human) and (right) Saccharomyces cerevisiae (baker’s yeast). Changes in controlling enzymes (square boxes in red) and the pathway itself have occurred (yeast has one altered (‘overtaking’) path in the graph)

We need to be able to do automatic pathway comparison (pathway alignment)

Human Yeast

‘Comparative metabolomics’

Important difference with human pathway

Experimental

Structural genomics Functional genomics Protein-protein interaction Metabolic pathways

Expression data

Issue when elucidating function experimentally

Partial information (indirect interactions) and subsequent filling of the missing steps

Negative results (elements that have been shown not to interact, enzymes missing in an organism)

Putative interactions resulting from computational analyses

Protein function categories Catalysis (enzymes) Binding – transport (active/passive)

– Protein-DNA/RNA binding (e.g. histones, transcription factors)

– Protein-protein interactions (e.g. antibody-lysozyme) (experimentally determined by yeast two-hybrid (Y2H) or bacterial two-hybrid (B2H) screening )

– Protein-fatty acid binding (e.g. apolipoproteins)

– Protein – small molecules (drug interaction, structure decoding)

Structural component (e.g. -crystallin) Regulation Signalling Transcription regulation Immune system Motor proteins (actin/myosin)

Catalytic properties of enzymes

[S]

Mo

les/

s

Vmax

Vmax/2

Km

Michaelis-Menten equation:

Km kcat

E + S ES E + P E = enzyme S = substrate ES = enzyme-substrate complex (transition state) P = product Km = Michaelis constant Kcat = catalytic rate constant (turnover number) Kcat/Km = specificity constant (useful for comparison)

Vmax × [S]V = ------------------- Km + [S]

Protein interaction domains

http://pawsonlab.mshri.on.ca/html/domains.html

Energy difference upon binding

Examples of protein interactions (and of functional importance) include:

Protein – protein (pathway analysis); Protein – small molecules

(drug interaction, structure decoding); Protein – peptides, DNA/RNA

The change in Gibb’s Free Energy of the protein-ligand binding interaction can be monitored and expressed by the following equation:

G = H – T S

(H=Enthalpy, S=Entropy and T=Temperature)

Protein-protein interaction networks

Protein function Many proteins combine functions Some immunoglobulin structures are

thought to have more than 100 different functions (and active/binding sites)

Alternative splicing can generate (partially) alternative structures

Protein function & Interaction

Active site / binding cleft

Shape complementarity

Protein function evolution

Chymotrypsin

How to infer function Experiment Deduction from sequence

– Multiple sequence alignment – conservation patterns

– Homology searching Deduction from structure

– Threading– Structure-structure comparison– Homology modelling

Cholesterol Biosynthesis:

Cholesterol biosynthesis primarily occurs in eukaryotic cells. It is necessary for membrane synthesis, and is a precursor for steroid hormone production as well as for vitamin D. While the pathway had previously been assumed to be localized in the cytosol and ER, more recent evidence suggests that a good deal of the enzymes in the pathway exist largely, if not exclusively, in the peroxisome (the enzymes listed in blue in the pathway to the left are thought to be at least partly peroxisomal). Patients with peroxisome biogenesis disorders (PBDs) have a variable deficiency in cholesterol biosynthesis

Mevalonate plays a role in epithelial cancers: it can inhibit EGFR

Cholesterol Biosynthesis: from acetyl-Coa to mevalonate

Epidermal Growth Factor as a Clinical Target in Cancer

A malignant tumour is the product of uncontrolled cell proliferation. Cell growth is controlled by a delicate balance between growth-promoting and growth-inhibiting factors. In normal tissue the production and activity of these factors results in differentiated cells growing in a controlled and regulated manner that maintains the normal integrity and functioning of the organ. The malignant cell has evaded this control; the natural balance is disturbed (via a variety of mechanisms) and unregulated, aberrant cell growth occurs. A key driver for growth is the epidermal growth factor (EGF) and the receptor for EGF (the EGFR) has been implicated in the development and progression of a number of human solid tumours including those of the lung, breast, prostate, colon, ovary, head and neck.

Energy housekeeping:Adenosine diphosphate (ADP) – Adenosine triphosphate (ATP)

Chemical Reaction

Add Enzymatic Catalysis

Add Gene Expression

Add Inhibition

Metabolic Pathway: Proline Biosynthesis

Proline as end product effects a negative feedback loop

Transcriptional Regulation

Methionine Biosynthesis in E. coli

Shortcut Representation

High-level Interaction representation

Levels of Resolution

SREBP Pathway

Signal Transduction

Important signalling pathways: Map-kinase (MapK) signalling pathway, or TGF- pathway

Transport

Phosphate Utilization in Yeast

Multiple Levels of Regulation

Gene expression Protein posttranslational modification Protein activity Protein intracellular location Protein degradation Substrate transport

Graphical Representation – Gene Expression

Protein interaction domains

Protein Interaction Domains

http://pawsonlab.mshri.on.ca/index.php?option=com_content&task=view&id=30&Itemid=63

Domain function

Active site / binding cleft

Protein-protein (domain-domain) interaction

Shape complementarity

A domain is a:

Compact, semi-independent unit (Richardson, 1981).

Stable unit of a protein structure that can fold autonomously (Wetlaufer, 1973).

Recurring functional and evolutionary module (Bork, 1992).“Nature is a tinkerer and not an inventor” (Jacob, 1977).

Smallest unit of function

Delineating domains is essential for:• Obtaining high resolution structures (x-ray but

particularly NMR – size of proteins)• Sequence analysis • Multiple sequence alignment methods• Prediction algorithms (SS, Class, secondary/tertiary

structure)• Fold recognition and threading• Elucidating the evolution, structure and function of

a protein family (e.g. ‘Rosetta Stone’ method)• Structural/functional genomics• Cross genome comparative analysis

Domain connectivity

linker

Pyruvate kinasePhosphotransferase

barrel regulatory domain

barrel catalytic substrate binding domain

nucleotide binding domain

1 continuous + 2 discontinuous domains

Structural domain organisation can be nasty…

Domain sizeThe size of individual structural domains varies widely

– from 36 residues in E-selectin to 692 residues in lipoxygenase-1 (Jones et al., 1998)

– the majority (90%) having less than 200 residues (Siddiqui and Barton, 1995)

– with an average of about 100 residues (Islam et al., 1995). Small domains (less than 40 residues) are often stabilised by metal ions or disulphide bonds.Large domains (greater than 300 residues) are likely to consist of multiple hydrophobic cores (Garel, 1992).

Analysis of chain hydrophobicity in multidomain proteins

Analysis of chain hydrophobicity in multidomain proteins

Domain characteristics

Domains are genetically mobile units, and multidomain families are found in all three kingdoms (Archaea, Bacteria and Eukarya) underlining the finding that ‘Nature is a tinkerer and not an inventor’ (Jacob, 1977). The majority of genomic proteins, 75% in unicellular organisms and more than 80% in metazoa, are multidomain proteins created as a result of gene duplication events (Apic et al., 2001). Domains in multidomain structures are likely to have once existed as independent proteins, and many domains in eukaryotic multidomain proteins can be found as independent proteins in prokaryotes (Davidson et al., 1993).

Protein function evolution- Gene (domain) duplication -

Chymotrypsin

Active site

Pyruvate phosphate dikinase

3-domain protein Two domains catalyse 2-step reaction

A B C Third so-called ‘swivelling domain’

actively brings intermediate enzymatic product (B) over 45Å from one active site to the other

/

Pyruvate phosphate dikinase

3-domain protein Two domains catalyse 2-step reaction

A B C Third so-called ‘swivelling domain’

actively brings intermediate enzymatic product (B) over 45Å from one active site to the other

/

The DEATH Domain• Present in a variety of Eukaryotic proteins involved with cell death.• Six helices enclose a tightly packed hydrophobic core.• Some DEATH domains form homotypic and heterotypic dimers.

http

://w

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Detecting Structural Domains A structural domain may be detected as a compact,

globular substructure with more interactions within itself than with the rest of the structure (Janin and Wodak, 1983).

Therefore, a structural domain can be determined by two shape characteristics: compactness and its extent of isolation (Tsai and Nussinov, 1997).

Measures of local compactness in proteins have been used in many of the early methods of domain assignment (Rossmann et al., 1974; Crippen, 1978; Rose, 1979; Go, 1978) and in several of the more recent methods (Holm and Sander, 1994; Islam et al., 1995; Siddiqui and Barton, 1995; Zehfus, 1997; Taylor, 1999).

Detecting Structural Domains

However, approaches encounter problems when faced with discontinuous or highly associated domains and many definitions will require manual interpretation.

Consequently there are discrepancies between assignments made by domain databases (Hadley and Jones, 1999).

Detecting Domains using Sequence only

Even more difficult than prediction from structure!

SnapDRAGON

Richard A. George

George R.A. and Heringa, J. (2002) J. Mol. Biol., 316, 839-851.

Integrating protein multiple sequence alignment, secondary and tertiary structure

prediction in order to predict structural domain boundaries in sequence data

Protein structure hierarchical levels

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

PRIMARY STRUCTURE (amino acid sequence)

QUATERNARY STRUCTURE

SECONDARY STRUCTURE (helices, strands)

TERTIARY STRUCTURE (fold)



















SNAPDRAGONDomain boundary prediction protocol using sequence information alone (Richard George)

1. Input: Multiple sequence alignment (MSA) and predicted secondary structure

2. Generate 100 DRAGON 3D models for the protein structure associated with the MSA

3. Assign domain boundaries to each of the 3D models (Taylor, 1999)

4. Sum proposed boundary positions within 100 models along the length of the sequence, and smooth boundaries using a weighted windowGeorge R.A. and Heringa J.(2002) SnapDRAGON - a method to delineate protein structural

domains from sequence data, J. Mol. Biol. 316, 839-851.

SnapDragonFolds generated by Dragon

Boundary recognition

(Taylor, 1999)Summed and Smoothed Boundaries

CCHHHCCEEE

Multiple alignment

Predicted secondary structure


1. Input: Multiple sequence alignment (MSA)

1. Sequence searches using PSI-BLAST (Altschul et al., 1997)

2. followed by sequence redundancy filtering using OBSTRUCT (Heringa et al.,1992)

3. and alignment by PRALINE (Heringa, 1999)

and predicted secondary structure4. PREDATOR secondary structure prediction

programGeorge R.A. and Heringa J.(2002) SnapDRAGON - a method to delineate protein structural domains from sequence data, J. Mol. Biol. 316, 839-851.

Distance Regularisation Algorithm for Geometry OptimisatioN

(Aszodi & Taylor, 1994)

Domain prediction using DRAGON

•Fold proteins based on the requirement that (conserved) hydrophobic residues cluster together.

•First construct a random high dimensional C distance matrix.

•Distance geometry is used to find the 3D conformation corresponding to a prescribed target matrix of desired distances between residues.


2. Generate 100 DRAGON (Aszodi & Taylor, 1994) models for the protein structure associated with the MSA– DRAGON folds proteins based on the requirement that

(conserved) hydrophobic residues cluster together– (Predicted) secondary structures are used to further

estimate distances between residues (e.g. between the first and last residue in a -strand).

– It first constructs a random high dimensional C (and pseudo C) distance matrix

– Distance geometry is used to find the 3D conformation corresponding to a prescribed matrix of desired distances between residues (by gradual inertia projection and based on input MSA and predicted secondary structure)

DRAGON = Distance Regularisation Algorithm for Geometry OptimisatioN

•The C distance matrix is divided into smaller clusters.

•Separately, each cluster is embedded into a local centroid.

•The final predicted structure is generated from full embedding of the multiple centroids and their corresponding local structures.

3NN

NN

C distancematrix

Targetmatrix

N

CCHHHCCEEE

Multiple alignment

Predicted secondary structure100 randomised

initial matrices

100 predictions Input data

Lysozyme 4lzm

PDB

DRAGON

Methyltransferase 1sfe

DRAGON

PDB

Phosphatase 2hhm-A

PDB DRAGON

Taylor method (1999)DOMAIN-3D3. Assign domain boundaries to each

of the 3D models (Taylor, 1999) Easy and clever method Uses a notion of spin glass theory (disordered

magnetic systems) to delineate domains in a protein 3D structure

Steps:1. Take sequence with residue numbers (1..N)2. Look at neighbourhood of each residue (first shell)3. If (“average nghhood residue number” > res no) resno =

resno+1else resno = resno-1

4. If (convergence) then take regions with identical “residue number” as domains and terminate

Taylor,WR. (1999) Protein structural domain identification. Protein Engineering 12 :203-216

Taylor method (1999)

41

5

6

89

56

78

repeat until convergence

if 41 < (5+6+56+78+89)/5

then Res 41 42 (up 1)

else Res 41 40 (down 1)

Taylor method (1999)

continuous

discontinuous


4. Sum proposed boundary positions within 100 models along the length of the sequence, and smooth boundaries using a weighted window (assign central position)

Window score = 1≤ i ≤ l Si × Wi

Where Wi = (p - |p-i|)/p2 and p = ½(n+1).

It follows that l Wi = 1George R.A. and Heringa J.(2002) SnapDRAGON - a method to delineate protein structural domains from sequence data, J. Mol. Biol. 316, 839-851.

i

Wi

SNAPDRAGONStatistical significance: Convert peak scores to Z-scores using

z = (x-mean)/stdev If z > 2 then assign domain boundary

Statistical significance using random models: Test hydrophibic collapse given distribution of

hydrophobicity over sequence Make 5 scrambled multiple alignments (MSAs) and predict

their secondary structure Make 100 models for each MSA Compile mean and stdev from the boundary distribution

over the 500 random models If observed peak z > 2.0 stdev (from random models) then

assign domain boundary

SnapDRAGON prediction assessment

• Test set of 414 multiple alignments;183 single and 231 multiple domain proteins.

• Boundary predictions are compared to the region of the protein connecting two domains (maximally 10 residues from true boundary)

SnapDRAGON prediction assessment• Baseline method I:

• Divide sequence in equal parts based on number of domains predicted by SnapDRAGON

• Baseline method II: • Similar to Wheelan et al., based on domain length

partition density function (PDF)• PDF derived from 2750 non-redundant structures

(deposited at NCBI) • Given sequence, calculate probability of one-

domain, two-domain, .., protein• Highest probability taken and sequence split equally

as in baseline method I

Continuous set Discontinuous set Full set

SnapDRAGONCoverage 63.9 (± 43.0) 35.4 (± 25.0) 51.8 (± 39.1)

Success 46.8 (± 36.4) 44.4 (± 33.9) 45.8 (± 35.4)

Baseline 1Coverage 43.6 (± 45.3) 20.5 (± 27.1) 34.7 (± 40.8)

Success 34.3 (± 39.6) 22.2 (± 29.5) 29.6 (± 36.6)

Baseline 2Coverage 45.3 (± 46.9) 22.7 (± 27.3) 35.7 (± 41.3)

Success 37.1 (± 42.0) 23.1 (± 29.6) 31.2 (± 37.9)

Average prediction results per protein

Coverage is the % linkers predicted (TP/TP+FN)Success is the % of correct predictions made (TP/TP+FP)

Average prediction results per protein


Function prediction of genes based on “guilt-by-association” – a non-homologous approach

The phylogenetic profile of a protein is a string that encodes the presence or absence of the protein in every sequenced genome

Because proteins that participate in a common structural complex or metabolic pathway are likely to co-evolve, the phylogenetic profiles of such proteins are often ``similar''


Phylogenetic profile (against N genomes)– For each gene X in a target genome (e.g., E coli),

build a phylogenetic profile as follows– If gene X has a homolog in genome #i, the ith bit

of X’s phylogenetic profile is “1” otherwise it is “0”


Example – phylogenetic profiles based on 60 genomes

orf1034:1110110110010111110100010100000000111100011111110110111010101orf1036:1011110001000001010000010010000000010111101110011011010000101orf1037:1101100110000001110010000111111001101111101011101111000010100orf1038:1110100110010010110010011100000101110101101111111111110000101orf1039:1111111111111111111111111111111111111111101111111111111111101orf104: 1000101000000000000000101000000000110000000000000100101000100orf1040:1110111111111101111101111100000111111100111111110110111111101orf1041:1111111111111111110111111111111101111111101111111111111111101orf1042:1110100101010010010110000100001001111110111110101101100010101orf1043:1110100110010000010100111100100001111110101111011101000010101orf1044:1111100111110010010111010111111001111111111111101101100010101orf1045:1111110110110011111111111111111101111111101111111111110010101orf1046:0101100000010001011000000111110000010100000001010010100000000orf1047:0000000000000001000010000001000100000000000000010000000000000orf105: 0110110110100010111101101010111001101100101111100010000010001orf1054:0100100110000001100001000100000000100100100001000100100000000

Genes with similar phylogenetic profiles have related functions or functionally linked – D Eisenberg and colleagues (1999)

By correlating the rows (open reading frames (ORF) or genes) you find out about joint presence or absence of genes: this is a signal for a functional connection

gene

genome


Phylogenetic profiles contain great amount of functional information

Phlylogenetic profile analysis can be used to distinguish orthologous genes from paralogous genes

Subcellular localization: 361 yeast nucleus-encoded mitochondrial proteins are identified at 50% accuracy with 58% coverage through phylogenetic profile analysis

Functional complementarity: By examining inverse phylogenetic profiles, one can find functionally complementary genes that have evolved through one of several mechanisms of convergent evolution.

Prediction of protein-protein interactions

Rosetta stone

Gene fusion is the an effective method for prediction of protein-protein interactions– If proteins A and B are homologous to two domains of a

protein C, A and B are predicted to have interaction

Though gene-fusion has low prediction coverage, it false-positive rate is low

A B

C

Domain fusion exampleVertebrates have a multi-enzyme protein (GARs-AIRs-GARt) comprising the enzymes GAR synthetase (GARs), AIR synthetase (AIRs), and GAR transformylase (GARt). In insects, the polypeptide appears as GARs-(AIRs)2-GARt. In yeast, GARs-AIRs is encoded separately from GARtIn bacteria each domain is encoded separately (Henikoff et al., 1997).

GAR: glycinamide ribonucleotide AIR: aminoimidazole ribonucleotide

Documents

Introduction to Bioinformatics Lecture 4: Bioinformatics infrastructure Centre for Integrative Bioinformatics VU (IBIVU) C E N T R F O R I N T E G R A