Protein Interaction Networks

Aalt-Jan van DijkApplied Bioinformatics, PRI, Wageningen UR

& Mathematical and Statistical Methods, Biometris, Wageningen Universityaaltjan.vandijk@wur.nl

Feb. 21, 2013

My research

• Protein complex structures– Protein-protein docking– Correlated mutations

• Interaction site prediction/analysis

– Protein-protein interactions– Enzyme active sites– Protein-DNA interactions

• Network modelling– Gene regulatory networks– Flowering related

Overview

• Introduction: protein interaction networks• Sequences & networks: predicting interaction sites• Predicting protein interactions• Sequence and network evolution• Interaction network alignment

Protein Interaction Networks

Obligatory

hemoglobin

Obligatory Transient

hemoglobin Mitochondrial Cu transporters

Protein Interaction Networks

Experimental approaches (1)

Yeast two-hybrid (Y2H)

Experimental approaches (2)

Affinity Purification + mass spectrometry (AP-MS)

Interaction Databases• STRING http://string.embl.de/

Interaction Databases

Interaction Databases• STRING http://string.embl.de/• HPRD http://www.hprd.org/

Interaction Databases

Interaction Databases• STRING http://string.embl.de/• HPRD http://www.hprd.org/• MINT http://mint.bio.uniroma2.it/mint/

Interaction Databases

Interaction Databases• STRING http://string.embl.de/• HPRD http://www.hprd.org/• MINT http://mint.bio.uniroma2.it/mint/• INTACT http://www.ebi.ac.uk/intact/

Interaction Databases

Interaction Databases• STRING http://string.embl.de/• HPRD http://www.hprd.org/• MINT http://mint.bio.uniroma2.it/mint/• INTACT http://www.ebi.ac.uk/intact/• BIOGRID http://thebiogrid.org/

Interaction Databases

Some numbers

Organism Number of known interactions

H. Sapiens 113,217S. Cerevisiae 75,529D. Melanogaster 35,028A. Thaliana 13,842M. Musculus 11,616

Biogrid (physical interactions)

Overview

• Introduction: protein interaction networks• Sequences & networks: predicting interaction

sites• Predicting protein interactions• Sequence and network evolution• Interaction network alignment

Binding site

Binding site predictionApplications:

Binding site predictionApplications:•Understanding network evolution•Understanding changes in protein function•Predict protein interactions•Manipulate protein interactions

Binding site predictionApplications:•Understanding network evolution•Understanding changes in protein function•Predict protein interactions•Manipulate protein interactionsInput data:•Interaction network•Sequences (possibly structures)

Sequence-based predictions

Sequences and networks

• Goal: predict interaction sites and/or motifs

Sequences and networks

• Goal: predict interaction sites and/or motifs

• Data: interaction networks, sequences

Sequences and networks

• Goal: predict interaction sites and/or motifs

• Data: interaction networks, sequences

• Validation: structure data, “motif databases”

Motif search in groups of proteins• Group proteins which have same interaction partner• Use motif search, e.g. find PWMs

Neduva Plos Biol 2005

Correlated Motifs

Correlated Motifs

• Motif model

• Search

• Scoring

Predefined motifs

Predefined motifs

Predefined motifs

Predefined motifs

Predefined motifs

Correlated Motif MiningFind motifs in one set of proteins which interact with(almost) all proteins with another motif

Correlated Motif MiningFind motifs in one set of proteins which interact with(almost) all proteins with another motif

Motif-models:•PWM – so far not applied•(l,d) with l=length, d=number of wildcards

Score: overrepresentation, e.g. χ2

Correlated Motif MiningFind motifs in one set of proteins which interact with(almost) all proteins with another motif

Search:•Interaction driven•Motif driven

Interaction driven approachesMine for (quasi-)bicliques most-versus-most interactionThen derive motif pair from sequences

Motif driven approachesStarting from candidate motif pairs, evaluate theirsupport in the network (and improve them)

D-MOTIF

Tan BMC Bioinformatics 2006

IMSS: application of D-MOTIF

Van Dijk et al., Bioinformatics 2008Van Dijk et al., Plos Comp Biol 2010

proteinYprotein

XTe

st e

rror

Number of selected motif pairs

Experimental validationprotein

YproteinX

Test

err

or

Number of selected motif pairs

Van Dijk et al., Bioinformatics 2008Van Dijk et al., Plos Comp Biol 2010

proteinYprotein

X

Van Dijk et al., Bioinformatics 2008Van Dijk et al., Plos Comp Biol 2010

Test

err

or

Number of selected motif pairs

Experimental validation

proteinYprotein

X

Van Dijk et al., Bioinformatics 2008Van Dijk et al., Plos Comp Biol 2010

Test

err

or

Number of selected motif pairs

Experimental validation

SLIDER

Boyen et al. Trans Comp Biol Bioinf 2011

SLIDER

Validation

Extensions of SLIDER

Boyen et al. Trans Comp Biol Bioinf 2013

Extensions of SLIDERExtension I: better coverage of networkExtension II: use of more biological information

bioSLIDERDGIFELELYLPDDYPMEAPKVRFLTKI

DGIFELELYLPDDYPMEAPKVRFLTKIconservation

bioSLIDER

DGIFELELYLPDDYPMEAPKVRFLTKIconservationaccessibility

bioSLIDER

DGIFELELYLPDDYPMEAPKVRFLTKIconservationaccessibility

bioSLIDER

Thresholds for conservation and accessibilityExtension of motif model: amino acid similarity (BLOSUM)

DGIFELELYLPDDYPMEAPKVRFLTKIconservation

No conservation, no accessibilityConservation and accessibility

Using human and yeast data for training and optimizing parameters

0.0 0.3 0.6Inte

ract

ion-

cove

rage

0.0 0.3 0.6

0.50.40.30.20.10.0

accessibility

bioSLIDER

Motif-accuracyLeal Valentim et al., PLoS ONE 2012

Application to Arabidopsis

Arabidopsis Interactome Mapping Consortium, Science 2011

Input data: 6200 interactions, 2700 proteinsInterface predictions for 985 proteins (on average 20 residues)

Ecotype sequence data (SNPs)SNPs tend to ‘avoid’ predicted binding sites

In 263 proteins there is a SNP in a binding site these proteins are much more connected to each otherthan would be randomly expected

Summary

• Prediction of interaction sites using proteininteraction networks and protein sequences• Correlated motif approaches

Overview

• Introduction: protein interaction networks• Sequences & networks: predicting interaction sites• Predicting protein interactions• Sequence and network evolution• Interaction network alignment

Protein Interaction Prediction

Lots of genomes are being sequenced…(www.genomesonline.org)

Complete IncompleteARCHAEA 182 264BACTERIA 3767 14393EUKARYA 183 2897TOTAL 4132 17514

Protein Interaction Prediction

Lots of genomes are being sequenced… (www.genomesonline.org)

Complete IncompleteARCHAEA 182 264BACTERIA 3767 14393EUKARYA 183 2897TOTAL 4132 17514

But how do we know how the proteins in there work together?!

Protein Interaction Prediction

• Interactions of orthologs: interologs

• Phylogenetic profiles

• Domain-based predictions

A 1 0 1 1 0 0 1

B 1 0 1 1 0 0 1

Orthology based prediction

Orthology based prediction

Phylogenetic profiles

A 1 0 1 1 0 0 1

B 1 0 1 1 1 0 1

C 1 0 1 1 1 0 1

D 0 1 0 1 0 0 1

Domain Based Predictions

Domain Based Predictions

Overview

• Introduction: protein interaction networks• Sequences & networks: predicting interaction sites• Predicting protein interactions• Sequence and network evolution• Interaction network alignment

Duplications

Duplications and interactions

Gene duplication

Duplications and interactions

Gene duplication

Duplications and interactions

0.1 Myear-1

Gene duplication Interaction loss

0.001 Myear-1

Duplications and interaction loss

Duplicate pairs share interaction partners

Interaction network evolution

Science 2011

Overview

• Introduction: protein interaction networks• Sequences & networks: predicting interaction sites• Predicting protein interactions• Sequence and network evolution• Interaction network alignment

Network alignment

Local Network Alignment: find multiple, unrelated regions ofIsomorphism

Global Network Alignment: find the best overall alignment

PATHBLAST

Kelley, PNAS 2003

PATHBLAST: scoring

Kelley, PNAS 2003

homology

interaction

PATHBLAST: results

Kelley, PNAS 2003

PATHBLAST: results

Kelley, PNAS 2003

For yeast vs H.pylori, with L=4, all resulting paths with p<=0.05 can be merged into just five network regions

Multiple alignment

Scoring: Probabilistic model for interaction subnetworks

Sub-networks: bottom-up search, starting with exhaustivesearch for L=4; followed by local search

Sharan PNAS 2005

Multiple alignment: results

Sharan PNAS 2005

Multiple alignment: results

Applications include protein function predictionand interaction prediction

Sharan PNAS 2005

Global alignment

Singh PNAS 2008

Global alignment

Singh PNAS 2008

Global alignment

Alignment: greedy selection of matches

Singh PNAS 2008

Network alignment: the future?

Sharan & Ideker Nature Biotech 2006

Summary• Interaction network evolution: mostly

“comparative”, not much mechanistic• Approaches exist to integrate and model network

analysis within context of phylogeny (not discussed)

• Outlook: combine interaction site prediction with network evolution analysis

ExercisesThe datafiles “arabidopsis_proteins.lis” and “interactions_arabidopsis.data” contain Arabidopsis MADS proteins (which regulate various developmental processes including flowering), and their mutual interactions, respectively.

AGL24 LFYSOC1

Exercise 1

• Start by getting familiar with the basic Cytoscape features described in section 1 of the tutorial http://opentutorials.cgl.ucsf.edu/index.php/Tutorial:Introduction_to_Cytoscape

• Load the data into Cytoscape• Visualize the network and analyze the number of

interactions per proteins – which proteins do have a lot of interactions?

Exercise 2

Write a script that reads interaction data and implements a datastructure which enables further analysis of the data (see setup on next slides).Use the datafiles “arabidopsis_proteins.lis” and “interactions_arabidopsis.data” and let the script print a table in the following format:PROTEIN Number_of_interactionsMake a plot of those data

#two subroutines

#input: filename#output: list with content of filesub read_list { my $infile=$_[0]; YOUR CODEreturn @newlist;}

#input: protein list and interaction list#output: hash with “proteins” list of their partners sub combine_prot_int($$) { my ($plist,$intlist) = @_; YOUR CODEreturn %inthash;}

#reading input datamy @plist= read_list($ARGV[0]);my @intlist= read_list($ARGV[1]);

#obtaining hash with interactions%inthash=combine_prot_int(\@plist,\@intlist);

YOUR CODE#loop over all proteins and print their name and their number of interactions

In “orthology_relations.data” we have a set of predictedorthologs for the Arabidopsis proteins fromexercise 1. “protein_information.data” describes a.o. from which species these proteins are. Finally, “interactions.data“ contains interactions between those proteins.Use the Arabidopsis interaction data from exercise 1 to “predict” interactions in other species using the orthology information. Compare your predictions with the real interaction data and make a plot that visualizes how good your predictions are.

Exercise 3