Exploring proteins, chemicals
and their interactions
with STRING and STITCH
Michael Kuhn
EMBL Heidelberg

my usual diet work:
drugs, proteins, side effects

today
intro: interactions
small examples
the databases: STRING and STITCH
larger example: NetworKIN

STRING: version 7
interactions of proteins
STITCH: version 1
interactions of
proteins and chemicals

interactions of proteins
and chemicals

example
Tryptophan synthase beta chain
E. Coli K12

example
aspirin
Homo sapiens

content
(STRING 7)

373 genomes
(only completely sequenced genomes)

1.5 million genes
(not proteins)

68,000 chemicals
(including 2200 drugs)

many sources of
interactions

genomic context

gene neighborhood

gene fusion

phylogenetic profiles

curated knowledge

experimental evidence
T

co-expression
GEO: Gene Expression Omnibus

experimental databases

literature

variable quality
different “raw scores”

benchmarking
calibrate against “gold standard”
(KEGG)

probabilistic scores
e.g. “70% chance for an assocation”

combine all evidence

Bayesian scoring scheme

e.g.: two scores of 0.7
combined probability: ?

e.g.: two scores of 0.7
combined probability: 0.91
1- (1-0.7) 2 = 0.91

evidence spread
over many species

evidence transfer

transfer by orthology
(or “fuzzy orthology”)

von Mering et al., Nucleic Acids Research, 2005

von Mering et al., Nucleic Acids Research, 2005

two modes

proteins mode

von Mering et al., Nucleic Acids Research, 2005

maximum specificity
lower coverage
information will be relevant for
selected species

COG mode
“clusters of orthologous groups”

von Mering et al., Nucleic Acids Research, 2005

higher coverage
lower specificity
includes all available evidence
some orthologous groups are too
large to be meaningful

a real application
Resource
Systematic Discovery of
In Vivo Phosphorylation Networks
Rune Linding,1,2,7,* Lars Juhl Jensen,3,7 Gerard J. Ostheimer,2,4,7 Marcel A.T.M. van Vugt,2,5 Claus Jørgensen,1
Ioana M. Miron,1 Francesca Diella,3 Karen Colwill,1 Lorne Taylor,1 Kelly Elder,1 Pavel Metalnikov,1
Vivian Nguyen,1 Adrian Pasculescu,1 Jing Jin,1 Jin Gyoon Park,1 Leona D. Samson,4 James R. Woodgett,1
Robert B. Russell,3 Peer Bork,3,6,* Michael B. Yaffe,2,* and Tony Pawson1,*
1
Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Canada
2
Center for Cancer Research, Massachusetts Institute of Technology, Cambridge, USA
3
European Molecular Biology Laboratory, Heidelberg, Germany
4
Center for Environmental Health Sciences, Massachusetts Institute of Technology, Cambridge, USA
5
Department of Cell Biology and Genetics, Erasmus University, Rotterdam, The Netherlands
6
¨
Max-Delbruck-Centre for Molecular Medicine, Berlin, Germany
7
These authors contributed equally to this work.
*Correspondence: linding@mshri.on.ca (R.L.), bork@embl.de (P.B.), myaffe@mit.edu (M.B.Y.), pawson@mshri.on.ca (T.P.)
DOI 10.1016/j.cell.2007.05.052
SUMMARY by creating binding sites for protein interaction domains
(for example, SH2 or BRCT) that selectively recognize

phosphoproteomics

in vivo phosphorylation sites

kinases are unknown

computational methods

overprediction

context

scaffolders
Alberts, Molecular Biology of the Cell

interaction networks

NetworKIN

benchmarking

DNA damage response

experimental validation

take home message
STRING and STITCH integrate
information and predict interactions
you can always go to the sources
it’s useful!

Acknowledgements
The STRING/STITCH team
Lars Juhl Jensen
Peer Bork
Christian von Mering & group in Zurich
NetworKIN
Lars Juhl Jensen
Rune Linding
(and many other people)

Thank you for your
attention

string.embl.de
von Mering et al., NAR Database Issue 2007
stitch.embl.de
Kuhn et al., NAR Database Issue 2008