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Unraveling signal transduction networks through data integration

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Lars Juhl Jensen
Lars Juhl Jensen

The document discusses methods for integrating different types of biological data to build networks that model signal transduction pathways. It describes using protein sequence motifs to predict kinase-substrate relationships, and combining this with protein interaction and expression data to provide context. Validation studies on ATM and Cdk1 signaling pathways showed this approach could accurately predict phosphorylation sites and the kinases that target them. Future work involves improving scoring methods and expanding to other types of post-translational modifications and model organisms.

TechnologySpiritual
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Unraveling signal transduction networks through data integration Lars Juhl Jensen
the problem
phosphoproteomics
Linding, Jensen, Ostheimer et al., Cell , 2007
in vivo phosphosites
kinases are unknown
functions are unknown
sequence specificity
peptide assays
Miller, Jensen et al., Science Signaling , 2008
domain-specific
in vitro
no context
what could happen
not what does happen
machine-learning methods
sequence motifs
Miller, Jensen et al., Science Signaling , 2008
domain-specific
group-specific
no context
what could happen
not what does happen
in vitro
in vivo
context
co-activators
protein scaffolds
subcellular localization
spatial expression
temporal expression
association networks
Linding, Jensen, Ostheimer et al., Cell , 2007
the idea
Linding, Jensen, Ostheimer et al., Cell , 2007
the sequence motifs
NetPhorest
automated pipeline
Miller, Jensen et al., Science Signaling , 2008
data organization
Miller, Jensen et al., Science Signaling , 2008
compilation of datasets
redundancy reduction
training and evaluation
classifier selection
motif atlas
 
179 kinases
89 SH2 domains
8 PTB domains
BRCT domains
WW domains
14-3-3 proteins
use cases
Miller, Jensen et al., Science Signaling , 2008
the context network
guilt by association
 
STRING
Jensen, Kuhn et al., Nucleic Acids Research , 2009
functional associations
data integration
primary experimental data
protein interactions
Jensen & Bork, Science , 2008
gene coexpression
 
genomic context methods
Korbel et al., Nature Biotechnology , 2004
Korbel et al., Nature Biotechnology , 2004
literature mining
 
curated knowledge
Letunic & Bork, Trends in Biochemical Sciences , 2008
too easy …
… to be true
different formats
different identifiers
variable quality
spread over 630 genomes
parsers
thesaurus
raw quality scores
benchmarking
von Mering et al., Nucleic Acids Research , 2005
transfer by orthology
von Mering et al., Nucleic Acids Research , 2005
combine all evidence
context network
Linding, Jensen, Ostheimer et al., Cell , 2007
the results
NetworKIN
Linding, Jensen, Ostheimer et al., Cell , 2007
benchmarking
Phospho.ELM
Linding, Jensen, Ostheimer et al., Cell , 2007
context is crucial
much context still missing
subcellular localization
Linding, Jensen, Ostheimer et al., Cell , 2007
ATM signaling
Linding, Jensen, Ostheimer et al., Cell , 2007
small-scale validation
ATM phosphorylates Rad50
Linding, Jensen, Ostheimer et al., Cell , 2007
Cdk1 phosphorylates 53BP1
Linding, Jensen, Ostheimer et al., Cell , 2007
high-throughput validation
multiple reaction monitoring
Linding, Jensen, Ostheimer et al., Cell , 2007
the future
new scoring scheme
two separate scores
one combined score
path length penalty
model organisms
S. cerevisiae
D. melanogaster
C. elegans
( S. pombe )
other modifications
phosphatases
ubiquitylation
acetylation
glycosylation
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larsjuhljensen

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Unraveling signal transduction networks through data integration

Editor's Notes

  1. Atlas of human kinases Atlases for phospho-binding proteins Atlases for model organisms Ubiquitination would be welcome