Computational BiologySignaling networks and drug repositioning

Lars Juhl Jensen

sequence analysis

Jensen, Gupta et al., Journal of Molecular Biology, 2002

data integration

de Lichtenberg, Jensen et al., Science, 2005

text mining

Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009

human variation

signaling networks

phosphoproteomics

in vivo phosphosites

kinases are unknown

sequence motifs

Miller, Jensen et al., Science Signaling, 2008

NetPhorest

data organization

Miller, Jensen et al., Science Signaling, 2008

automated pipeline

Miller, Jensen et al., Science Signaling, 2008

compilation of datasets

training and evaluation

motif atlas

179 kinases

89 SH2 domains

8 PTB domains

BRCT domains

WW domains

14-3-3 proteins

sequence specificity

kinase-specific

in vitro

network context

Linding, Jensen, Ostheimer et al., Cell, 2007

STRING

Jensen, Kuhn et al., Nucleic Acids Research, 2009

630 genomes

2.5 million proteins

genomic context

gene fusion

Korbel et al., Nature Biotechnology, 2004

phylogenetic profiles

Korbel et al., Nature Biotechnology, 2004

primary experimental data

physical interactions

Jensen & Bork, Science, 2008

gene coexpression

curated knowledge

Letunic & Bork, Trends in Biochemical Sciences, 2008

literature mining

not comparable

confidence scores

von Mering et al., Nucleic Acids Research, 2005

cross-species integration

Linding, Jensen, Ostheimer et al., Cell, 2007

putting it all together

NetworKIN

Linding, Jensen, Ostheimer et al., Cell, 2007

>2x better accuracy

use case

DNA damage response

Linding, Jensen, Ostheimer et al., Cell, 2007

experimental validation

ATM phosphorylates Rad50

Linding, Jensen, Ostheimer et al., Cell, 2007

drug repositioning

new uses for old drugs

drug–drug network

shared target(s)

chemical similarity

Tanimoto coefficients

Campillos & Kuhn et al., Science, 2008

Campillos & Kuhn et al., Science, 2008

similar drugs share targets

only trivial predictions

phenotypic similarity

chemical perturbations

phenotypic readouts

drug treatment

side effects

no database

package inserts

Campillos & Kuhn et al., Science, 2008

text mining

side-effect ontology

backtracking

Campillos & Kuhn et al., Science, 2008

side-effect correlations

Campillos & Kuhn et al., Science, 2008

GSC weighting

side-effect frequencies

Campillos & Kuhn et al., Science, 2008

raw similarity score

Campillos & Kuhn et al., Science, 2008

p-values

Campillos & Kuhn et al., Science, 2008

side-effect similarity

chemical similarity

Campillos & Kuhn et al., Science, 2008

confidence scores

drug–drug network

Campillos & Kuhn et al., Science, 2008

categorization

Campillos & Kuhn et al., Science, 2008

experimental validation

20 drug–drug relations

in vitro binding assays

Ki<10 µM for 11 of 20

cell assays

9 of 9 showed activity

summary

computational biology

network analysis

testable predictions

save much time in the lab

AcknowledgmentsNetPhorest.info

– Rune Linding– Martin Lee Miller– Francesca Diella– Claus Jørgensen– Michele Tinti– Lei Li– Marilyn Hsiung– Sirlester A. Parker– Jennifer Bordeaux– Thomas Sicheritz-Pontén– Marina Olhovsky– Adrian Pasculescu– Jes Alexander– Stefan Knapp– Nikolaj Blom– Peer Bork– Shawn Li– Gianni Cesareni– Tony Pawson– Benjamin E. Turk– Michael B. Yaffe– Søren Brunak

STRING-DB.org– Christian von Mering– Damian Szklarczyk– Michael Kuhn– Manuel Stark– Samuel Chaffron– Chris Creevey– Jean Muller– Tobias Doerks– Philippe Julien– Alexander Roth– Milan Simonovic– Jan Korbel– Berend Snel– Martijn Huynen– Peer Bork

Side effect– Monica Campillos– Michael Kuhn– Christian von Mering– Anne-Claude Gavin– Peer Bork

NetworKIN.info– Rune Linding– Gerard Ostheimer– Heiko Horn– Martin Lee Miller– Francesca Diella– Karen Colwill– Jing Jin– Pavel Metalnikov– Vivian Nguyen– Adrian Pasculescu– Jin Gyoon Park– Leona D. Samson– Rob Russell– Peer Bork– Michael Yaffe– Tony Pawson

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