investigating questions in biology using computational ... filemosquito human mosquito liver rbc...
TRANSCRIPT
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Investigating questions in biology using computational approaches
Group LeaderMRC Laboratory of Molecular Biology, Cambridge
M. Madan Babu
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At what level is this talk pitched at?
“Biologically” inclined
“Com
puta
tiona
lly”i
nclin
ed
Development of methodsAlgorithms, programs, etc
Uncovering general principlesDiscovery using computational approaches
Prioritising experimentsInterpreting experimental results
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Outline
• Introduction to resources and tools (10 minutes)
• A case study to highlight data integration (10 mins)
• Specific questions (25 mins)
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How can I know more about a gene?
Knowing more about a gene is like trying toobtain all possible information about a
suspect (as in a murder case) or a person (as in you are interested in someone ☺)
Treat it like investigating a case!
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Treat it like investigating a case!(suspect)
What is the suspect’s name?What does the suspect look like?
Where does the suspect live?Where does the suspect work?
What does the suspect do?Who does the suspect interact with?What is the ancestry of the suspect?
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Treat it like investigating a case!(gene)
What is the sequence of the protein?What is the structure of the protein?Where in the genome is it encoded?
Which tissues are the genes expressed?Which cellular compartment does it reside in?
What is the function of the protein? Who are the interacting partners of the protein?
What is its evolutionary history?
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Information retrieval(think of google)
Database
Query
Search tool
Ranking, statistics
Output
How can one extract information?
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Explosion of information about living systems
Major challenge – How to exploit this information?
Expression 20,000 different conditions150 organisms (SMD, GEO, ArrayExpress)
Interaction 100,000 interactions30 organisms (Bind, DIP, publications)
Structure 50,000 structures from 10,000 organisms (PDB, MSD)
Sequence 45,000,000 sequences from 160,000 organisms (Genbank, NCBI)
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Sequence
Sequence database (NCBI, ENSEMBL)
Query sequence
BLAST, PSI-BLAST, etc
E-value, score, etc
SequenceAlignment
Structure
Structure database (PDB)
Query structure
DALI, SSM, VAST, etc
p-value, score, etc
StructureAlignments
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Expresion
Expression database (GEO, BioGPS)
Query gene
Pearson correlation coefficient
Correlation coefficient
Other transcripts withSimilar expression profile
Network
Functional networks, Protein interaction network, pathway
database, etc
Gene list
Sub-graph extraction
P-value, enrichments
Network of functional associationFunctional enrichment
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What is the list of databases that is currently available?
Question #1
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http://www.oxfordjournals.org/nar/database/c/
1230 selected databases covering various aspects of molecular and cell biology
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http://www.expasy.ch/links.html
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http://database.oxfordjournals.org/
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What is the list of tools, web-servers and programs that are
currently available?
Question #2
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0ver 1500 selected tools covering various aspects of molecular and cell biology
http://bioinformatics.ca/links_directory/
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http://www.expasy.ch/tools/
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http://toolkit.tuebingen.mpg.de/sections/search
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Outline
• Introduction to resources and tools (10 minutes)
• A case study to highlight data integration (10 mins)
• Specific questions (25 mins)
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Mosquito Human
Mos
quito
LiverR
BC
www.cdc.gov
Previous comparative genomic analysis of eukaryotes suggested lack of detectable transcription factors in Plasmodium
5300 genes with over 700 metabolic enzymes
Extensive complement of chromosomal regulatory proteins
Extensive complementsignaling proteins (GTPases, kinases)
Large number of genes Complex life cycle
The Problem!How does this pathogen regulate gene expression?
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Alternative regulatory mechanismsChromatin-level regulation
Post-translational modificationRNA based regulation
Undetected transcription factorsDistantly related or unrelated
to known DNA binding domains
Possible explanations for the paradoxical observation
Proteome of Plasmodium
Profiles & HMMs of known DBDs
bZIP
Homeo
MADs
AT-hook
Forkhead
ARID
PF14_0633
+ ?
AT-Hook
SEG
UncharacterizedGlobular domain
~60 aaThe suspect!
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Characterization of the globular domain – sequence analysis I
Non-redundant database
+
...
..
..
Lineage specific expansion in Apicomplexa
Plasmodium falciparumPlasmodium vivax
Cryptosporidium parvum
Theileria annulata
Cryptosporidium hominis
Profiles + HMMof this region
Non-redundant database
+
Floral Homeotic protein Q(Triticum)
49L, an endonuclease (X. oryzae phage Xp10)
Globular region maps to AP2 DNA-binding domain
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Non-redundant database
+AP2 DNA-binding
Domain fromD. Psychrophila
DP2593
MAL6P1.287(Plasmodium falciparum)
Cgd6_1140/Chro.60146(Cryptosporidium)
AP2 DNA-binding domain maps to the Globular region
Characterization of the globular domain – sequence analysis II
Multiple sequence alignment of all globular
domains
JPRED/PHD
Sequence of secondary structure is similar to the AP2 DNA-binding domain
Homologs of the conserved globular domain constitutes a novel family of the AP2 DNA-binding domain
S1 S2 S3 H1
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Characterization of the globular domain – structural analysis I
A. thaliana ethylene response factor(ATERF1 - 1gcc – NMR structure)
Binds GC rich sequences
S1 S2 S3 H1
S1 S2 S3 H1
Predicted SS of ApiAP2
SS of ATERF1
S1 S2 S3 H1
12 residues show a strong pattern of conservation andthese are involved in key stabilizing hydrophobic
interactions that determine the path of the backbonein the three strands and helix of the AP2 domain
Core fold of the ApiAp2 domainwill be similar to the plantAP2 DNA-binding domain
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Characterization of the globular domain – structural analysis II
Y186
R147
K156
T175
R170
W154
R152
R150
E160
W172
W162
G5
C6C7
G21
G20
G18
G17
R152 --- G5 (oxo group)D/N --- A (amino group)
R150 --- G20 (oxo group)S/T --- A (amino group)
Changes in base-contacting residues suggest binding to
AT-rich sequence
S2 S3
Charged residues in the insertmay contact multiple phosphate
groups to provide affinity
ApiAp2 domain binds DNA in a sequence specific manner
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RBC infection & merozoite burst
Characterization of the globular domain – expression analysis I
Mosquito Human
Mos
quito
www.cdc.gov
Complex life cycle
Liver
RB
C
Intra-erythrocyte developmental cycleDeRisi Lab
mRNA expression profilingUsing microarray
(sorbitol syncronization)
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Characterization of the globular domain – expression analysis II
Co-expressed genes
Ave
rage
exp
ress
ion
prof
ile o
f all
gene
s
0 46Time points
Ring stage
Trophozoite stage
Early Schizont stage
Schizont stage
22 Transcription factors
0 46Time points
Striking expression pattern in specific developmental stages suggests that they could mediate transcriptional regulation of stage specific genes
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Characterization of the globular domain – interaction analysis I
Protein interaction network of P. falciparum
Protein (1267)
Physical interaction (2846)LaCount et. al. Nature (2005)
Modified Y2H: Gal4 DBD + Protein + auxotrophic gene
RNA isolated from mixed stages ofIntra-erythrocyte developmental cycle
Guilt by association
Function of interacting neighbors provides cluesabout function of the protein
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Characterization of the globular domain – interaction analysis II
Protein interaction network of P. falciparum
Guilt by association supports the role of ApiAp2 proteins to beinvolved in regulation of gene expression
ApiAp2 proteins (13)
Chromatin proteins (8)
50% hypotheticalNucleosome assemblyHMG proteinGlycolytic enzymesAntigenic proteinsHave a PPint domain
MAL8P1.153 (ES)
PFD0985w (S)
PF10_0075 (T)
PF07_0126 (R)
Network of ApiAp2 proteins (97 interactions, 93 proteins)
Gcn5
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Sequence Structure Expression Interaction
Integration of different types of experimental data allowed us to discover potential transcription factors
in the Plasmodium genome
Integration of data can generate experimentally testable hypotheses
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The verdict!
Guilty!
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Outline
• Introduction to resources and tools (10 minutes)
• A case study to highlight data integration (10 mins)
• Specific questions (25 mins)
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1.Formulate the big question2.Come up with several specific questions3.Prioritise questions and prepare a checklist4.Identify the database5.Identify the tools6.Be aware of the basic statistics7.Retrieve and integrate the information8.Formulate hypothesis and READ A LOT!9.Design experiments10.Publish work & be happy ever after ☺
General approach to investigate biological questions using computational approach
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How can I analyse the sequence of a protein?
Question #1
>gi|75018194MESTEDEFYTICLNLTAEDPSFGNCNYTTDFENGELLEKVVSRVVPIFFGFIGIVGLVGNALVVLVVAANPGMRSTTNLLIINLAVADLLFVIFCVPFTATDYVMPRWPFGDWWCKVVQYFIVVTAHASVYTLVLMSLDR FMAVVHPIASMSIRTEKNALLAIACIWVVILTTAIPVGICHGEREYSYFNRNHSSCVFLEERGYSKLGFQ MSFFLSSYVIPLALISVLYMCMLTRLWKSAPGGRVSAESRRGRKKVTRMVVVVVVVFAVCWCPIQIILLV KALNKYHITYFTVTAQIVSHVLAYMNSCVNPVLYAFLSENFRVAFRKVMYCPPPYNDGFSGRPQATKTTR TGNGNSCHDIV
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Proteins are made of domains, which are independent evolutionary units
Unassigned regionNew domain or unstructured region
Single domain Two domain
HTH domain BAR domain
Sequence and structure based domain definition
Structure baseddomain definition
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http://pfam.janelia.org/family/bar
Database of protein domains
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How can I obtain all sequences containing a particular domain?
Click “Species” to obtain all proteins with a particular domain For Marwah Hassan
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http://supfam.org/SUPERFAMILY/cgi-bin/search.cgi
Structural domain assignments to completely sequenced genomes
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Genome assignment, sequence alignment, domain combinations and taxonomic distribution For Marwah Hassan
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Intrinsically Unstructured Proteins
IUPs: lack an unique 3-dimensional structure, either entirely or in parts. It is assumed that they sample a variety of conformations that
are in dynamic equilibrium under physiological conditions.
Structural continuum of intrinsically unstructured proteins
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Exposed peptide may mediate protein-peptide interaction, PTM, etc
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Identification of intrinsically unstructured proteins
Computational approaches to predict unstructured regions
Accuracies are similar to current secondary structure predictionmethods from sequence (~80%)
Computationally trained on existing structures in the PDB
DisoPred2: Support vector machinetrained on structure data
DisEMBL: Neural network trained on structure data
Sequence composition based on specific scales
FoldIndex: Identifies regions of lowhydrophobicity but high net chargeusing a sliding window
PONDR: Local amino-acid composition, flexibility and hydropathy
GlobPlot: amino-acid propensity to form globular domains using the russel-linding scale
HCA, IUPred, RONN, SEG, PreLink and several others
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http://en.wikipedia.org/wiki/Intrinsically_unstructured_proteins#Database_of_Protein_Disorder
Programs for identifying unstructured regions in a protein sequence
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http://elm.eu.org/
Identification of potential eukaryotic linear motif
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http://www.southampton.ac.uk/~re1u06/software/slimsuite/
Identification of potential eukaryotic linear motif
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http://smart.embl-heidelberg.de/
SMART combines domain assignment with prediction of linear motifs in unstructured regions
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How to characterise an unassigned region in a protein?
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1.Get a good text editor (textpad, bbedit, nedit)2.Run a domain analysis (pfam, superfamily, SMART)3.Identify regions that are of low complexity4.Run Linear Motif prediction5.Run a post-translation modification prediction6.Run a signal peptide prediction7.Run a PSI-BLAST search on individual domains8.Run a PSI-BLAST search on the whole sequence9.Ask if predicted regions are evolutionarily conserved
checklist
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How can I identify structurally similar proteins?
Which amino acids contact the ligand of interest in a structure?
What analyses can I do with a structure?
Question #2
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http://www.pdb.org/pdb/explore/explore.do?structureId=1YDV
Protein Data Bank: repository for protein structures
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http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/GetPage.pl
Comprehensive summary of deposited structures
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http://redpoll.pharmacy.ualberta.ca/vadar/
Interactions, surface area and volume calculations
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http://www.ebi.ac.uk/thornton-srv/software/LIGPLOT/
Ligand contacting residues
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http://www.cathdb.info/
http://scop.mrc-lmb.cam.ac.uk/scop/
Domain definition and evolutionary relationship of
domains
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http://consurf.tau.ac.il/overview.html
Evolutionary analysis of individual amino
acids on the structure
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Programs to retrieve similar structures
http://www.ebi.ac.uk/msd-srv/ssm/cgi-bin/ssmserver
http://ekhidna.biocenter.helsinki.fi/dali_server/
http://www.ncbi.nlm.nih.gov/Structure/VAST/vast.shtml
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http://supfam.mrc-lmb.cam.ac.uk/elevy/3dcomplex/About.cgi
Obtain related proteins with similar topology to investigate how interfaces evolve in homologous proteins
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http://www.ebi.ac.uk/pdbe-srv/emsearch/atlas/1740_visualization.html
EMDB: repository for electron microscopy structures
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Databases and tools for structure analysis
http://www.ebi.ac.uk/Databases/structure.html
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How can I find the ortholog of my protein in other genomes?
Which residues are functionally important?
Question set #3
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Ortholog detection is not trivial
Need to infer evolutionary history of the gene family
Obtain the tree
Investigate synteny
Orthologs are genes in different species that evolved from a common ancestral gene by speciation.
Paralogs are genes related by duplication within a genome.
Orthologs retain the same function in the course of evolution, whereas paralogs evolve new functions, even if these are related to the original one.
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ENSEMBL: a repository for genome sequence
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Go to the entry page of a gene of interest
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Tree of the evolutionary history of a gene
Click “Gene Tree” to get the tree for further detailed analysis
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Orthology relationship table
Click “orthologs” to obtain pre-computed orthology relationship table
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Synteny
Click “Genomic Alignments” to obtain details about synteny
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Alignment
Click “Alignment” to obtain alignments
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http://weblogo.berkeley.edu/examples.html
Weblogo for position specific analysis of an alignment of orthologs
Evolutionary conservation reflects structural or a functional constrain on a position
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http://www.mrc-lmb.cam.ac.uk/genomes/spial/
SPIAL: Specificity conferring residues in paralogous families
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Interface residues in glutamate receptor N-
terminal domain
Interface residues between stat4 and stat5
Residues that are different between paralogs but conserved amongorthologs are specificity conferring positions
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http://eggnog.embl.de/
Pre-computed ortholog databases
http://www.ncbi.nlm.nih.gov/COG/
http://inparanoid.sbc.su.se/cgi-bin/index.cgihttp://mbgd.genome.ad.jp/
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How can I know more about my favourite gene?
Splice forms, regulators, promoter structure, protein domains, SNPs, paralogs, genomic position, etc..
Question Set #4
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ENSEMBL entry for a gene has links to a lot of relevant information!
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What are the SNPs in my gene (or region) of interest?
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NCBIhttp://www.ncbi.nlm.nih.gov/sites/gquery
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How to use NCBI website effectivelyhttp://www.ncbi.nlm.nih.gov/guide/all/howto/
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In which tissues, cell lines, and conditions are my genes
expressed?
What are the transcript levels and protein levels for my gene?
Question Set #5
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http://biogps.gnf.org/#goto=welcome
A web-portal for data on expression levels of transcripts in 70 different human and mouse tissues and cancer cell lines
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http://www.ebi.ac.uk/microarray-as/ae/
Public repository for gene expression datasets at the EBI
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http://www.ncbi.nlm.nih.gov/geo/
Public repository for gene expression datasets at the NCBI
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http://www.proteinatlas.org/index.php
Human protein expression levels (Immunohistochemistry)
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Immunohistochemistry in normal cells
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Immunohistochemistry in cancer cells and cell lines
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Where can I obtain functional networks for my gene(s)?
Question Set #6
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What is a functional interaction network?
Gene A Gene B
Edge and its weight is a function of:Co-regulationCo-expression across several conditionsPhysical interactionCo-localisationCo-evolution (present/absent together in several genomes)Gene fusion in another organismSimilar phenotype when knocked outToxic when over expressedGenetically interactEpistatic interactionSynthetic lethalityCo-citation
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Functional interaction network
Guilty by association
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Functional interaction network for eukaryotes
http://www.functionalnet.org/
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http://string-db.org/
Functional interaction network for prokaryotes and eukaryotes
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http://funcoup.sbc.su.se/
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http://sonorus.princeton.edu/hefalmp/
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http://avis.princeton.edu/mouseNET/viewgraph.php?graphID=42
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http://pixie.princeton.edu/pixie/
GFD1
For Jack Dean
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I have a set of genes from a Y2H experiment, what can I
make of it?
I have a list of differentially expressed genes, what can I do
with that list?
Question Set #7
For Alex Hamilton
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Gene Ontology
The Gene Ontology is a controlled vocabulary, a set of standard terms—words and phrases—used for indexing and retrieving information. In addition to defining
terms, GO also defines the relationships between the terms, making it a structured vocabulary
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The Gene Ontology is a a set of standard terms
Every gene has a set of gene ontology terms associated with it
Given a set of genes, the objective is to ask if particular functions (or pathways) are enriched
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Functional Enrichment
http://david.abcc.ncifcrf.gov/tools.jsp
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Approach
Are they evolutionarily conserved?Presence/absence inHumans, worm, fly
What are their molecular functions?Sequence &structure
What are their expression patterns?Expression pattern & co-expression analysis
What are their localization patterns?MembraneNucleus
What are the morphological changes?
No. NucleusVesicle size
What are their interaction partners?Protein complexesEvolutionary conservation of partners
Which TFs regulate their expression?Transcription factors involvedEvolutionary conservation of regulators
Which signaling proteins are involved? Signaling proteins involvedEvolutionary conservation
Which metabolic pathways are involved?
Metabolic pathways involvedEvolutionary conservation
Do orthologs behave similarly?
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1.Formulate the big question2.Come up with several specific questions3.Prioritise questions and prepare a checklist4.Identify the database5.Identify the tools6.Be aware of the basic statistics7.Retrieve and integrate the information8.Formulate hypothesis and READ A LOT!9.Design experiments10.Publish work & be happy ever after ☺
General approach to investigate biological questions using computational approach
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THANK YOU!
questions welcome