LARGE-SCALE PROTEIN INTERACTOMICS. Karl Frontzek Institute of Neuropathology

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LARGE-SCALE PROTEIN INTERACTOMICS Karl Frontzek Institute of Neuropathology

STUDYING THE INTERACTOME A yeast 2 hybrid (DB: DNA binding domain, AD: activation domain) B tandem affinity purification (dashed line: TEV protease cutting site) C protein complementation assay (F+F > reporter protein fragments, e.g. DHFR) D luminescence-based mammalian interactome (LUMIER) (affinity tag + luciferase) E protein microarrays F Mammalian protein-protein interactiontrap (MAPPIT) (bait is coupled to signaling-deficient cytokine receptor, prey is coupled to signalling-competent gp130) Lievens et al., Expert Review of Proteomics 2010

WHY NEED ANOTHER TECHNIQUE? binary binding information (Y2H, PCA, LUMIER, microarrays, MAPPIT...), complex binding (AP/TAP) however, these techniques are qualitative, but not quantitative (binding affinities and subsequent ranking of interactions...)

PAPER #1

PULLING DOWN COMPLEXES WITH BEADS AND MULTIPLE WASHINGS NEGLECTS FAST-EXCHANGING INTERACTIONS UNDER NON-EQUILIBRIUM CONDITIONS Haris G. Vikis and Kun-Liang Guan, Methods Mol. Biol. 2004

PDZ-DOMAINS o PDZ = common structural domains of 80-90 aa s with GLGF motifs that bind to S/T-X-V motifs on C-terminus of other peptides ( dubbed PDZbinding motifs PBM) o PDZ = acronym for PSD95, Dlg1, zo-1 o to date around 180 unique PDZ-domain containing proteins are identified with several 1000 putative PBMs o a majority of PDZ-domain containing proteins are involved in cell polarisation Fanning&Anderson, JCI 1999 Ye&Zhang, Biochem J 2013

THE HOLDUP / CATCHUP CHROMATOGRAPHIC ASSAY Charbonnier S et al., Prot. Expr. And Purif. 2006

GENERATE PDZOME-BINDING PROFILES OF 2 HPV PBMS TOWARDS ALMOST THE FULL COMPLEMENT OF HUMAN PDZ DOMAINS Javier&Rice, J Virol 2011

THE HIGH-THROUGHPUT HOLDUP ASSAY o [ligand] : [domain] = 10-100 : 1 o domains are fused to maltose-bindingprotein o resin beads aresaturated with biotinylated peptides o resin holds-up domain-ligand complexes o negative control = biotinylated resin

RAKING AND NORMALIZATION OF BINDING AFFINITIES

VALIDATION OF THE AUTOMATED HOLD-UP ASSAY 5 MBP-fused PDZ constructs involving 42 biotinylated peptides = 210 interactions Luck et al., PLOS One 2011

BENCHMARKING THE HOLD-UP ASSAY VS SPR BIACORE

STRONG CORRELATION BETWEEN DOMAINS FROM CRUDE LYSATES VS. PURIFIED PROTEINS

PROBING 2 VIRAL (E.G. HPV 16, HPV 18) PEPTIDES AGAINST THE HUMAN PDZOME 1x 384-well plate in triplicates = 209 interactions = 79% of the PDZome GSNSGNGNS - none peptide

VALIDATION IN TERMS OF CORRELATION BETWEEN 96- AND 384-WELL PLATES AND OF DIFFERENT NEGATIVE CONTROLS

SETTING THE OPTIMAL THRESHOLD FOR STRONG AND MEDIUM BINDERS BI > 0.2 high-confidence binding pairs BI > 0.1 more relaxed threshold for weak, but relevant binding 100% of binders had s.d.(bi) < 0.2 95% of binders had s.d.(bi)<0.1 100% of negatives are below BI = 0.2 98% of negatives are below BI = 0.1

Kds each E6 PBM bound 1%, 4% or 20% of the PDZome with a KD below 5 μm, 25 μm or 250 μm, respectively

DETERMINANTS OF PDZ-E6 RECOGNITION, HIS119 IS FAVOURED FOR THR -2 BINDING

HIS<>THR-2 INTERACTION IS WELL-DOCUMENTED FROM OTHER PDZ-DOMAIN CONTAINING PROTEINS Doyle DA et al., Cell 1996

P0 BINDING POCKET IN PDZ-PBM COMPLEXES IS MOST HIGHLY CORRELATED DOMAIN IN TERMS OF BINDING AFFINITY -4-2 -1 0 HPV16_E6 SSRTRRETQL HPV18_E6 RLQRRRETQV

HOLD-UP ASSAY CONFIRMS 11/12 (HPV16) AND 8/10 (HPV18) PUBLISHED BINDERS WHILE DISCOVERING 40 NEW

STRONGLY E6-BINDING PDZ PROTEINS ARE INVOLVED IN MULTIPLE CANCERS

DISCUSSION TRANSIENT AND WEAK INTERACTIONS ARE IMPORTANT FOR CELL HOMEOSTATIS Perkins JR et al., Structure 2010

DISCUSSION o pro: assay gives a lot of realiable information about low-abundant and low-affinity interactions o pro: high-throughput (3x384 well plates with 2 peptides + 2 neg. controls = >200 unique interactions screened in 1 day, scale up > higher throughput) o contra: multitask liquid-handling robot and microfluidics capillary instrument are found in many laboratories where they serve other purposes (quite expensive hardware?!)

PAPER #2

NOTES ON INTERACTION

AFTER INTERACTION OCCURS o direction (edge direction) o sign (activation or inhibition) o mode (ubiquitination, phosphorylation...) o here: develop a computational framework to predict the signs (positive or negative) of physical interactions using RNAi screens o positive PPI: A+B ----(+)à A (or B) o negative PPI: A+B -----(-)à A (or B)

DATABASES USED FOR THE STUDY

AVERAGE FRACTION OF OVERLAPPING HITS = 14%

FRAMEWORK OF PPI PREDICTION

DATA SOURCES AND PRECISION Ø positive reference set Ø (positive reference set) =negative reference set

PERFORMANCE BETWEEN DIFFERENT DATABASES WAS SIMILAR, WHILE NEGATIVE INTERACTIONS SCORED WORSE THAN POSITIVE ONES

A MINIMUM OF 9 RNAI SCREENS IS NEEDED FOR HIGH ACCURACY

CONSTRUCTING A DROSOPHILA NETWORK PPIs only 47,293 PPIs among 9,107 proteins. PPIs + phenotypes 6,125 PPIs connecting 3,352 proteins, 4,135 PPIs are positive interactions and 1,990 PPIs are negative Predicted network consists of 13-fold more interactions than literature-based signed interactions (434 PPIs)

POSITIVE INTERACTIONS TEND TO CLUSTER

POSITIVE INTERACTIONS ARE RATHER INTRAMODULAR, AND ARE HIGHER CORRELATED

35% OF CONSERVED INTERACTIONS ARE DIRECTLY LINKED TO HUMAN DISEASE PROTEINS

NETWORK REPRESENTATION OF KNOWN SIGNALING PATHWAYS

FOCUS ON THE PROTEASOME http://bio1151.nicerweb.com/locked/media/ch18/proteasome.html 51 nodes, including 29 proteins that are part of the proteasome complex and 22 proteins that interact with it

HIGH-CONFIDENCE, PROTEASOME INTERACTION NETWORK

VALIDATION OF PREDICTED PROTEASOME REGULATORS

VALIDATION OF PREDICTED PROTEASOME REGULATORS (8/10 PREDICTED GENES SHOWED BEHAVIOUR CONSISTENT WITH PREDICTION)

VALIDATION OF PREDICTED PROTEASOME REGULATORS luminescence based assay

http://flipper.diff.org/app/items/info/4414 Liang et al. Genome Biology 2010

DISCUSSION PAPER #2 o pro: not only genetic interaction correlation or phenotype similarity but phenotype correlation to predict the function of physical interactions o pro: robust to inherent noise from RNAi screens and has high predictive power o contra: limited to context-dependent signs such as asymmetric bidirectional signs (like negative feedback loops etc.) o contra: due to limited RNAi datasets, only around 10% of known PPIs are covered