Transcript Functional Genomics Modeling I

Modeling Functional Genomics
Datasets
CVM8890-101
Lesson 6
11 July 2007
Bindu Nanduri
Lesson 6: Functional
genomics modeling II: a
pathway analysis example.
Introduction to protein interaction networks
Cancer
Programmed Cell Death
Quiescence
Cell
Differentiation
Differentiation
Proliferation
Lymphoma
Anergy
Quiescence
Programmed Cell Death
CD4 +
T ‘helper”
Lymphocyte
Differentiation
Activation
Proliferation
Agbase protein annotation process
Protein identifiers or
Fasta format
GORetriever
Proteins with no
annotations
GOanna
Annotated
Proteins
GOSlimViewer
Potential CD4+ T lymphocyte Biological Processes
Activation
100%
Anergy
20%
Apoptosis
Angiogenesis
56%
31% 44%
80% 69%
Cell Cycle
79%
Senescence
21%
33%
67%
Proliferation
Migration
Differentiation
Quiescence
67%
92%
8%
92%
8%
68%
32%
33%
Integrin Signaling Pathway
AP-1
AP-1 dependent gene
expression
Tumor
invasion
Metastasis
Hypothesis driven data analysis
Exploration of data to identify pathways
of interacting proteins
Protein protein interaction networks (PPI)
Why study PPIs
Proteins do not function alone!!!!!
PPI are inherent to the function of
multiprotein complexes
PPIs can help infer function : where
functional information is available for
one partner
Changes in normal PPI can result in
disease
Types of PPI
PPI categories based on composition, affinity and
timescale of interaction
Homo and hetero oligomeric complexes: interactions between identical or
non-identical chains
Obligate PPI: protomers do not exist in as stable structures in vivo
these are functionally obligate
Non-obligate PPI: protomers can exist as stable structures, may co-localize
for function /are co-localized
c
Arc repressor dimer
necessary for DNA binding
Non-obligate homo dimer
Sperm lysin
PPI based on the life time of the complex: transient or permanent
Permanaent interactions are stable and exist only as complex
Transient interactions are marked by association/dissociation
cycles in vivo
Weak interactions (sperm lysin) associate and dissociate
Strong transient interactions require a
molecular trigger
heterotrimeric G protein dissociates to
G-alpha and g-beta and g-gamma
when it binds to GTP , GDP-bound
form is a trimer
Control of protein oligomerization
PPI interactions are a continuum of obligate and non-obligate states
Interactions of complexes driven by concentration and free energy
of complex relative to alternate states
Take home message of PPI types
PPI interactions are a continuum of obligate and non-obligate states
Interactions of complexes driven by concentration and free energy
of complex relative to alternate states
How to identify PPI
Experimental
Yeast two hybrid (Y2H)
TAP assays
Gene Coexpression
Protein arrays
Computational
Phylogenetic profile
Gene Cluster
Sequence coevolution
Rosetta stone method
Text mining
Y2H Assay
Eukaryotic transcription factors have
DNA binding and activation domain
Physical association of these domains
activates transcription
Cretae chimeric proteins with
either BD or AD tranfect yeast
Gal4/LexA based reporters
In vivo method that can detect
transient PPI
PLoS Computational Biology March 2007, Volume 3 e42
TAP Assay
TAP tag consists of two IgG binding
domains of Staphylococcus protein A
and calmodulin binding peptide
seperated by tobacco etch virus
protease cleavage site
TAP provides direct information on
protein complexes
O. Puig et al,Methods, 2001
Gene Coexpression
Expression profile similarity
correlation coefficient between relative
expression levels of two genes/proteins
the normalized difference between their
absolute expression levels
The distribution for target proteins is compared with the distributions
for random noninteracting protein pairs
Expression levels of physically interacting proteins coevolve
coevolution of gene expression is a better predictor of protein
interactions than coevolution of amino acid sequences
Good for studying permanent complexes : ribosome, proteasome
PLoS Computational Biology March 2007, Volume 3 e42
Protein microarrays/chips
Protein chips are disposable arrays
of microwells in silicone elastomer
sheets placed on top of microscope slides
Target proteins are over expressed
immobilized and probed with fluorescently
labeled proteins
H Zhu et al (2000) “Analysis of yeast
protein kinases using protein chips”
Nature Genetics 26: 283-289
can detect PPI between actual proteins
PLoS Computational Biology March 2007, Volume 3 e42
Database/URL/FTP
Type
DIP
http://dip.doe-mbi.ucla.edu
BIND http://bind.ca
MPact/MIPS http://mips.gsf.de/services/ppi
STRING http://string.embl.de
MINT http://mint.bio.uniroma2.it/mint
IntAct http://www.ebi.ac.uk/intact
BioGRID http://www.thebiogrid.org
HPRD http://www.hprd.org
ProtCom http://www.ces.clemson.edu/compbio/ProtCom
3did, Interprets http://gatealoy.pcb.ub.es/3did/
Pibase, Modbase http://alto.compbio.ucsf.edu/pibase
CBM ftp://ftp.ncbi.nlm.nih.gov/pub/cbm
SCOPPI http://www.scoppi.org/
iPfam http://www.sanger.ac.uk/Software/Pfam/iPfam
InterDom http://interdom.lit.org.sg
DIMA http://mips.gsf.de/genre/proj/dima/index.html
Prolinks http://prolinks.doe-mbi.ucla.edu/cgibin/functionator/pronav/
Predictome
http://predictome.bu.edu/
E,S
E,C,S
E,C,F
E,P,F
E,C
E,C
E,C
E,C
S,H
S,H
S,H
S
S
S
P
F,S
F
F
PLoS Computational Biology March 2007, Volume 3 e42
Database/URL/FTP
Type
DIP
http://dip.doe-mbi.ucla.edu
BIND http://bind.ca
MPact/MIPS http://mips.gsf.de/services/ppi
STRING http://string.embl.de
E,S
E,C,S
E,C,F
E,P,F
Type of data (high-throughput experimental data (E),
structural data (S), manual curation(C), functional
predictions (F), and interface homology modeling (H)
Unit of interaction :P is protein
IntAct http://www.ebi.ac.uk/intact
BioGRID http://www.thebiogrid.org
HPRD http://www.hprd.org
ProtCom http://www.ces.clemson.edu/compbio/ProtCom
3did, Interprets http://gatealoy.pcb.ub.es/3did/
Pibase, Modbase http://alto.compbio.ucsf.edu/pibase
CBM ftp://ftp.ncbi.nlm.nih.gov/pub/cbm
PLoS Computational Biology March 2007, Volume 3 e42
E,C
E,C
E,C
S,H
S,H
S,H
S
PPI database comparisons
Proteins: Structure, Function and Bioinformatics 63:490-500 2006
Experimental PPI dataset overlap is small
High FP rate in high- throughput exp
…….difficult to confirm by multiple sources
How to identify PPI
Experimental
Yeast two hybrid (Y2H)
TAP assays
Gene Coexpression
Protein arrays
Computational
Phylogenetic profile
Gene Cluster/neighborhood
Sequence coevolution
Rosetta stone method
Text mining
Phylogenetic profile (PP)
Hypothesis: functionally linked and
potentially interacting
nonhomologous proteins co-evolve and
have orthologs in the same subset
of fully sequenced organisms
PLoS Computational Biology March 2007, Volume 3 e43
Gene Cluster, Gene Neighborhood
Genes in the gene cluster/operon are
co-regulated and participate in the
same biological function
PLoS Computational Biology March 2007, Volume 3 e43
Sequence Co-evolution
interacting proteins very often co-evolve
changes in one protein ( loss of function or
Interaction) compensated by the
correlated changes in another protein.
The orthologs of co-evolving proteins
tend to interact, thereby making it possible to
infer unknowninteractions in other genomes
co-evolution can be reflected in terms of the
similarity between phylogenetic trees of two
non-homologous
interacting protein families
PLoS Computational Biology March 2007, Volume 3 e43
Rosetta Stone method
interacting proteins/domains have
homologs in other genomes
fused into one protein chain,
a Rosetta Stone protein
Gene fusion occurs to optimize
co-expression of genes encoding for
interacting proteins.
PLoS Computational Biology March 2007, Volume 3 e43
Text Mining
Utilizing the wealth of publicly available data
..search Medline or PubMed for words or word
combinations
co-occurrence of words together is a simple metric, however
prone to high false positive rates
Natural Language Processing (NLP) methods are specific
“A binds to B”; “A interacts with B”; “A associates with B”
difficult to detect so it has a higher false negative rate
Normally requires a list of known gene names or protein
names for a given organism
GO ToolBox
Genome Biol. 2004;5(12):R101.
ProtQuant tool