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Binding equilibrium in Protein-Protein Interaction networks

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Binding equilibrium in Protein-Protein Interaction networks Powered By Docstoc
					Propagation of perturbations in protein binding networks
Sergei Maslov Brookhaven National Laboratory

Experimental interaction data are binary instead of graded  it is natural to study topology
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Very heterogeneous number of binding partners (degree) One large cluster containing ~80% proteins Perturbations were analyzed from purely topological standpoint

Ultimately one want to quantify the equilibrium and dynamics: time to go beyond topology!
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Law of Mass Action equilibrium
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dDAB/dt = r(on)AB FA FB – r(off)AB DAB In equilibrium DAB=FA FB/KAB where the dissociation constant KAB= r(off)AB/ r(on)AB has units of concentration Total concentration = free concentration + bound concentration  CA= FA+FA FB/KAB  FA=CA/(1+FB/KAB)
In a network Fi=Ci/(1+neighbors j Fj/Kij) Can be numerically solved by iterations

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What is needed to model?
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A reliable network of reversible (non-catalytic) proteinprotein binding interactions
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 CHECK! e.g. physical interactions between yeast proteins in
the BIOGRID database with 2 or more citations. Most are reversible: e.g. only 5% involve a kinase

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Total concentrations Ci and sub-cellular localizations of all proteins
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in vivo dissociation constants Kij
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CHECK! genome-wide data for yeast in 3 Nature papers (2003, 2003, 2006) by the group of J. Weissman @ UCSF. VERY BROAD distribution: Ci ranges between 50 and 106 molecules/cell Left us with 1700 yeast proteins and ~5000 interactions OOPS! . High throughput experimental techniques are not there yet

Let’s hope it doesn’t matter
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The overall binding strength from the PINT database: <1/Kij>=1/(5nM). In yeast: 1nM ~ 34 molecules/cell Simple-minded assignment Kij=const=10nM (also tried 1nM, 100nM and 1000nM) Evolutionary-motivated assignment: Kij=max(Ci,Cj)/20: Kij is only as small as needed to ensure binding given Ci and Cj All assignments of a given average strength give ROUGHLY THE SAME RESULTS

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Robustness with respect to assignment of Kij
Bound concentrations: Dij Free concentrations: Fi

Spearman rank correlation: 0.89 Pearson linear correlation: 0.98

Spearman rank correlation: 0.89 Pearson linear correlation: 0.997

Numerical study of propagation of perturbations
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We simulate a twofold increase of the abundance C0 of just one protein Proteins with equilibrium free concentrations Fi changing by >20% are significantly perturbed We refer to such proteins i as concentration-coupled to the protein 0 Look for cascading perturbations

Resistor network analogy
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Conductivities ij – dimer (bound) concentrations Dij
Losses to the ground iG – free (unbound) concentrations Fi Electric potentials – relative changes in free concentrations (-1)L Fi/Fi Injected current – initial perturbation C0
SM, K. Sneppen, I. Ispolatov, arxiv.org/abs/q-bio.MN/0611026;

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What did we learn from this mapping?
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The magnitude of perturbations` exponentially decay with the network distance (current is divided over exponentially many links) Perturbations tend to propagate along highly abundant heterodimers (large ij ) Fi/Ci has to be low to avoid “losses to the ground” Perturbations flow down the gradient of Ci Odd-length loops dampen the perturbations by confusing (-1)L Fi/Fi

Exponential decay of perturbations
O – real S - reshuffled D – best propagation

HHT1

SM, I. Ispolatov, PNAS in press (2007)

What conditions make some long chains good conduits for propagation of concentration perturbations while suppressing it along side branches?

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Perturbations propagate along dimers with large concentrations

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They cascade down the concentration gradient and thus directional
Free concentrations of intermediate proteins are low SM, I. Ispolatov, PNAS in press (2007)

Implications of our results

Cross-talk via small-world topology is suppressed, but…
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Good news: on average perturbations via reversible binding rapidly decay Still, the absolute number of concentrationcoupled proteins is large In response to external stimuli levels of several proteins could be shifted. Cascading changes from these perturbations could either cancel or magnify each other. Our results could be used to extend the list of perturbed proteins measured e.g. in microarray experiments

Genetic interactions
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Propagation of concentration perturbations is behind many genetic interactions e.g. of the “dosage rescue” type We found putative “rescued” proteins for 136 out of 772 such pairs (18% of the total, P-value 10-216)
SM, I. Ispolatov, PNAS in press (2007)

SM, I. Ispolatov, PNAS in press (2007)

Intra-cellular noise
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Noise is measured for total concentrations Ci (Newman et al. Nature (2006)) Needs to be converted in biologically relevant bound (Dij) or free (Fi) concentrations Different results for intrinsic and extrinsic noise Intrinsic noise could be amplified (sometimes as much as 30 times!)

Could it be used for regulation and signaling?
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3-step chains exist in bacteria: anti-antisigma-factors  anti-sigma-factors  sigmafactors  RNA polymerase Many proteins we find at the receiving end of our long chains are global regulators (protein degradation by ubiquitination, global transcriptional control, RNA degradation, etc.)
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Other (catalytic) mechanisms spread perturbations even further Feedback control of the overall protein abundance?

Future work

Kinetics Non-specific vs specific
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How quickly the equilibrium is approached and restored? Dynamical aspects of noise How specific interactions peacefully coexist with many non-specific ones

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Iaroslav Ispolatov Research scientist Ariadne Genomics

Kim Sneppen NBI, Denmark

THE END

Genome-wide protein binding networks
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Nodes - proteins Edges - proteinprotein bindings Experimental data are binary while real interactions are graded  one deals only with topology

S. cerevisiae curated PPI network used in our study

Going beyond topology and modeling the binding equilibrium and propagation of perturbations

SM, K. Sneppen, I. Ispolatov, arxiv.org/abs/q-bio.MN/0611026; SM, I. Ispolatov, PNAS in press (2007)

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Kij=max(Ci,Cj)/20 total concentration Ci bound concentrations Dij free concentration Fi

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histogram

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10 0 10

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10 10 10 concentration (molecules/cell)

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Indiscriminate cross-talk is suppressed

What did we learn from topology?
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Broad distribution of the degree K of individual nodes Degree-degree correlations and high clustering Small-world-property: most proteins are in the same cluster and are separated by a short distance (follows from 1. for <K2>/<K> > 2 )

Protein binding networks have small-world property
86% of proteins could be connected 83% in this plot

S. cerevisiae
Large-scale Y2H experiment Curated dataset used in our study

Why small-world matters?
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Claims of “robustness” of this network architecture come from studies of the Internet where breaking up the network is undesirable For PPI networks it is the OPPOSITE: interconnected pathways are prone to undesirable cross-talk In a small-world network equilibrium concentrations of all proteins in the same component are coupled to each other

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RNA polymerase II

mRNA polyadenylation; protein sumoylation

G2/M transition of cell cycle

unfolded protein binding

mRNA, protein, rRNA export from nucleus

RNA polymerase I, III
35S primary transcript processing protein phosphatase type 2A

Propagation to 3rd neighbors
HSP82  SSA1  KAP95  NUP60 : -1.13 SSA2  HSP82  SSA1  KAP95: -1.51 HSC82  CPR6  RPD3  SAP30: -1.20 SSA2  HSP82  SSA1  MTR10: -1.57 CDC55  PPH21  SDF1  PPH3: -2.42 CDC55  PPH21  SDF1  SAP4: -2.42 PPH22  SDF1  PPH21  RTS1: -1.18 • Only 7 pairs in the DIP core network

• But in Krogan et al. dataset there are 84 pairs at d=3, 17 pairs at d=4, and 1 pair at d=5 (sic!). Total=102 • Reshuffled concentrations same network, Total=16

CDC55| CPR6| HSC82| HSP82| KAP95| MTR10| NUP60| PPH21| PPH22| PPH3| RPD3| RTS1| SAP30| SAP4| SDF1| SSA1| SSA2|

2155 | 8600 | 1461 | protein biosynthesis* | protein phosphatase type 2A activity | 4042 | 18600 | 114 | protein folding | unfolded protein binding* | 4635 | 132000 | 4961 | telomere maintenance* | unfolded protein binding* | 6014 | 445000 | 115 | response to stress* | unfolded protein binding* | 4176 | 51700 | 41 | protein import into nucleus | protein carrier activity | 5535 | 6340 | 6 | protein import into nucleus* | nuclear localization sequence binding | 102 | 4590 | 1693 | telomere maintenance* | structural constituent of nuclear pore | 874 | 5620 | 95 | protein biosynthesis* | protein phosphatase type 2A activity | 930 | 4110 | 72 | protein biosynthesis* | protein phosphatase type 2A activity | 1069 | 2840 | 200 | protein amino acid dephosphorylation* | protein phosphatase type 2A activity | 5114 | 3850 | 269 | chromatin silencing at telomere* | histone deacetylase activity | 5389 | 300 | 80 | protein biosynthesis* | protein phosphatase type 2A activity | 4714 | 704 | 80 | telomere maintenance* | histone deacetylase activity | 2195 | 279 | 20 | G1/S transition of mitotic cell cycle | protein serine/threonine phosphatase activity | 6101 | 5710 | 451 | signal transduction | molecular function unknown | 33 | 269000 |40441 | translation* | ATPase activity* | 3780 | 364000 |83250 | response to stress* | ATPase activity* |

'RPS10A' 'SEC27' 'HTB2' 'HTB2' 'RPS10A' 'HTB2' 'HTB2' 'HTB2' 'HTB2' 'RPN1' 'HTB2' 'SEC27' 'GIS2' 'HTB2' 'HTB2' 'RPS10A' 'HTB2' 'HTB2'

'SPC72' [ 1.4732] 'URA7' [ 1.2557] 'YBR273C' [ 1.3774] 'TUP1' [ 1.2796] 'AIR2' [ 2.3619] 'UFD2' [ 1.3717] 'YDR049W' [ 1.3645] 'PLO2' [ 1.2640] 'YDR330W' [ 1.3774] 'GAT1' [ 1.4277] 'YFL044C' [ 1.3774] 'STT3' [-1.2321] 'STT3' [ 1.3437] 'YGL108C' [ 1.3774] 'UFD1' [ 1.3744] 'AIR1' [ 2.3833] 'FBP1' [ 1.3576] 'YMR067C' [ 1.3510]

Propagation to th neighbors 4 in Krogan nc

AIR1| 2889 | mRNA export from nucleus* | molecular function unknown | nucleus* AIR2| 916 | mRNA export from nucleus* | molecular function unknown | nucleus* FBP1| 4207 | gluconeogenesis | fructose-bisphosphatase activity | cytosol GAT1| 1857 | transcription initiation from RNA polymerase II promoter* | specific RNA polymerase II transcription factor activity* | nucleus* GIS2| 5039 | intracellular signaling cascade | molecular function unknown | cytoplasm HTB2| 136 | chromatin assembly or disassembly | DNA binding | nuclear nucleosome PLO2| 1291 | telomere maintenance* | histone deacetylase activity | nucleus* RPN1| 2608 | ubiquitin-dependent protein catabolism | endopeptidase activity* | cytoplasm* RPS10A| 5667 | translation | structural constituent of ribosome | cytosolic small ribosomal subunit (sensu Eukaryota) SEC27| 2102 | ER to Golgi vesicle-mediated transport* | molecular function unknown | COPI vesicle coat SPC72| 78 | mitotic sister chromatid segregation* | structural constituent of cytoskeleton | outer plaque of spindle pole body STT3| 1987 | protein amino acid N-linked glycosylation | dolichyl-diphosphooligosaccharide-protein glycotransferase activity | oligosaccharyl transferase c. TUP1| 710 | negative regulation of transcription* | general transcriptional repressor activity | nucleus UFD1| 2278 | ubiquitin-dependent protein catabolism* | protein binding | endoplasmic reticulum UFD2| 932 | response to stress* | ubiquitin conjugating enzyme activity | cytoplasm* URA7| 174 | phospholipid biosynthesis* | CTP synthase activity | cytosol YBR273C| 534 | ubiquitin-dependent protein catabolism* | molecular function unknown | endoplasmic reticulum* YDR049W| 1043 | biological process unknown | molecular function unknown | cytoplasm* YDR330W| 1328 | ubiquitin-dependent protein catabolism | molecular function unknown | cytoplasm* YFL044C| 1880 | protein deubiquitination* | ubiquitin-specific protease activity | cytoplasm* YGL108C| 2073 | biological process unknown | molecular function unknown | cellular component unknown YMR067C| 4506 | ubiquitin-dependent protein catabolism* | molecular function unknown | cytoplasm*

Weight of links

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Perturbations sign-alternate thus perturbations always decay

j Dij/Ci=1-Fi /Ci <1

Resistor network analogy

• j~Fj/Fj – potentials, Dij , Fj , Ci –currents
• Dij – conductivity between interacting nodes • Fi – shunt conductivity to the ground

<1/Kd>=1/5.2nM close to our choice of 10nM

Data from PINT database (Kumar and Gromiha, NAR 2006)

How much data is out there?
Species Set HTP-PI LC-PI nodes 4,500 3,100 6,800 2,800 6,400 1,800
700 1,300

edges

# of sources 5 3,100 2 1 12,000 2
1 1

S.cerevisiae

13,000 20,000 22,000 4,500 31,000 3,500
1,500 2,800

D.melanogaster HTP-PI C.elegans H.sapiens
HTP-PI LC-PI HTP-PI
H. pylori P. falciparum
HTP-PI HTP-PI

Breakup by experimental technique in yeast
BIOGRID database S. cerevisiae 28172 55 5710 Affinity Capture-Mass Spec Affinity Capture-RNA Affinity Capture-Western

Co-crystal Structure
FRET Far Western Two-hybrid Total

107
43 41 11935 46063

Sprinzak et al., JMB, 327:919-923, 2003

TAPMass-Spec

Yeast 2-hybrid

Christian von Mering*, Roland Krause†, Berend Snel*, Michael Cornell‡, Stephen G. Oliver‡, Stanley Fields§ & Peer Bork* NATURE |VOL 417, 399-403| 23 MAY 2002

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