Protein Interaction Networks Aalt-Jan van Dijk Applied Bioinformatics, PRI, Wageningen UR & Mathematical and Statistical Methods, Biometris, Wageningen University 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

ObligatoryTransient hemoglobinMitochondrial Cu transporters Protein Interaction Networks

Experimental approaches (1) Yeast two-hybrid (Y2H)

Experimental approaches (2) Affinity Purification + mass spectrometry (AP-MS)

Interaction Databases STRING

Interaction Databases

STRING HPRD

Interaction Databases

STRING HPRD MINT

Interaction Databases

STRING HPRD MINT INTACT

Interaction Databases

STRING HPRD MINT INTACT BIOGRID

Interaction Databases

Some numbers OrganismNumber of known interactions H. Sapiens113,217 S. Cerevisiae75,529 D. Melanogaster35,028 A. Thaliana13,842 M. Musculus11,616 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 prediction Applications:

Binding site prediction Applications: Understanding network evolution Understanding changes in protein function Predict protein interactions Manipulate protein interactions

Binding site prediction Applications: Understanding network evolution Understanding changes in protein function Predict protein interactions Manipulate protein interactions Input 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

Motif model Search Scoring

Predefined motifs

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

Correlated Motif Mining Find 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. χ 2

Correlated Motif Mining Find motifs in one set of proteins which interact with (almost) all proteins with another motif Search: Interaction driven Motif driven

Interaction driven approaches Mine for (quasi-)bicliques  most-versus-most interaction Then derive motif pair from sequences

Motif driven approaches Starting from candidate motif pairs, evaluate their support in the network (and improve them)

D-MOTIF Tan BMC Bioinformatics 2006

IMSS: application of D-MOTIF Van Dijk et al., Bioinformatics 2008 Van Dijk et al., Plos Comp Biol 2010 protein Y protein X Test error Number of selected motif pairs

Experimental validation protein Y protein X Test error Number of selected motif pairs Van Dijk et al., Bioinformatics 2008 Van Dijk et al., Plos Comp Biol 2010

protein Y protein X Van Dijk et al., Bioinformatics 2008 Van Dijk et al., Plos Comp Biol 2010 Test error Number of selected motif pairs Experimental validation

protein Y protein X Van Dijk et al., Bioinformatics 2008 Van Dijk et al., Plos Comp Biol 2010 Test error Number of selected motif pairs Experimental validation

SLIDER Boyen et al. Trans Comp Biol Bioinf 2011

Faster approach, enabling genome wide search Scoring: Chi 2 Search: steepest ascent SLIDER

Performance assessment on simulated data Performance assessment using using protein structures Validation

Extension I: better coverage of network Extensions of SLIDER Boyen et al. Trans Comp Biol Bioinf 2013

Extensions of SLIDER Extension I: better coverage of network Extension II: use of more biological information

bioSLIDER DGIFELELYLPDDYPMEAPKVRFLTKI

conservation bioSLIDER

DGIFELELYLPDDYPMEAPKVRFLTKI conservation accessibility bioSLIDER

DGIFELELYLPDDYPMEAPKVRFLTKI conservation accessibility bioSLIDER Thresholds for conservation and accessibility Extension of motif model: amino acid similarity (BLOSUM)

DGIFELELYLPDDYPMEAPKVRFLTKI conservation No conservation, no accessibility Conservation and accessibility Using human and yeast data for training and optimizing parameters Interaction-coverage accessibility bioSLIDER Motif-accuracy Leal Valentim et al., PLoS ONE 2012

Application to Arabidopsis Arabidopsis Interactome Mapping Consortium, Science 2011 Input data: 6200 interactions, 2700 proteins Interface 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 other than would be randomly expected

Summary Prediction of interaction sites using protein interaction 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… ( CompleteIncomplete ARCHAEA BACTERIA EUKARYA TOTAL

Protein Interaction Prediction Lots of genomes are being sequenced… ( CompleteIncomplete ARCHAEA BACTERIA EUKARYA TOTAL 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 B

Orthology based prediction

Phylogenetic profiles A B C D

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 duplicationInteraction loss 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 of Isomorphism 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 exhaustive search for L=4; followed by local search Sharan PNAS 2005

Multiple alignment: results Sharan PNAS 2005

Multiple alignment: results Applications include protein function prediction and 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

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

Exercise 1 Start by getting familiar with the basic Cytoscape features described in section 1 of the tutorial al:Introduction_to_Cytoscape al: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: PROTEINNumber_of_interactions Make a plot of those data

#two subroutines #input: filename #output: list with content of file sub read_list { my $infile=$_[0]; YOUR CODE } #input: protein list and interaction list #output: hash with “proteins”  list of their partners sub combine_prot_int($$) { my ($plist,$intlist) YOUR CODE return %inthash; }

#reading input data read_list($ARGV[0]); read_list($ARGV[1]); #obtaining hash with interactions YOUR CODE #loop over all proteins and print their name and their number of interactions

In “ orthology_relations.data” we have a set of predicted orthologs for the Arabidopsis proteins from exercise 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