LARGE-SCALE PROTEIN INTERACTOMICS. Karl Frontzek Institute of Neuropathology

Transcription

1 LARGE-SCALE PROTEIN INTERACTOMICS Karl Frontzek Institute of Neuropathology

2 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

3 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...)

4 PAPER #1

5 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

6 PDZ-DOMAINS o PDZ = common structural domains of 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

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

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

9 THE HIGH-THROUGHPUT HOLDUP ASSAY o [ligand] : [domain] = : 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

10 RAKING AND NORMALIZATION OF BINDING AFFINITIES

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

12 BENCHMARKING THE HOLD-UP ASSAY VS SPR BIACORE

13 STRONG CORRELATION BETWEEN DOMAINS FROM CRUDE LYSATES VS. PURIFIED PROTEINS

14 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

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

16 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) < % of binders had s.d.(bi)< % of negatives are below BI = % of negatives are below BI = 0.1

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

18

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

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

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

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

23 STRONGLY E6-BINDING PDZ PROTEINS ARE INVOLVED IN MULTIPLE CANCERS

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

25 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?!)

26 PAPER #2

27 NOTES ON INTERACTION

28 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)

29 DATABASES USED FOR THE STUDY

30 AVERAGE FRACTION OF OVERLAPPING HITS = 14%

31 FRAMEWORK OF PPI PREDICTION

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

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

34 A MINIMUM OF 9 RNAI SCREENS IS NEEDED FOR HIGH ACCURACY

35 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)

36

37 POSITIVE INTERACTIONS TEND TO CLUSTER

38 POSITIVE INTERACTIONS ARE RATHER INTRAMODULAR, AND ARE HIGHER CORRELATED

39 35% OF CONSERVED INTERACTIONS ARE DIRECTLY LINKED TO HUMAN DISEASE PROTEINS

40 NETWORK REPRESENTATION OF KNOWN SIGNALING PATHWAYS

41 FOCUS ON THE PROTEASOME 51 nodes, including 29 proteins that are part of the proteasome complex and 22 proteins that interact with it

42 HIGH-CONFIDENCE, PROTEASOME INTERACTION NETWORK

43 VALIDATION OF PREDICTED PROTEASOME REGULATORS

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

45 VALIDATION OF PREDICTED PROTEASOME REGULATORS luminescence based assay

46 Liang et al. Genome Biology 2010

47

48 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