propagation of noise and perturbations in protein binding networks sergei maslov brookhaven national...
Post on 20-Jan-2016
217 Views
Preview:
TRANSCRIPT
Propagation of noise and perturbations
in protein binding networks
Sergei MaslovBrookhaven National Laboratory
Experimental interaction data are binary instead of graded it is natural to study topology
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!
Law of Mass Action equilibrium
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
What is needed to model? A reliable network of reversible (non-catalytic)
protein-protein binding interactions 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
Total concentrations Ci of all proteins CHECK! genome-wide data for yeast in 3 Nature papers
(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
in vivo dissociation constants Kij OOPS! . High throughput experimental techniques are
not there yet
Let’s hope it doesn’t matter
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
Robustness with respect to assignment of Kij
Spearman rank correlation: 0.89
Pearson linear correlation: 0.98
Bound concentrations: Dij
Spearman rank correlation: 0.89
Pearson linear correlation: 0.997
Free concentrations: Fi
Numerical study of propagation of perturbations
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 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;
What did we learn from this mapping?
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 – realS - reshuffledD – best propagation
SM, I. Ispolatov, PNAS in press (2007)
HHT1
What conditionsmake some
long chains good conduits
for propagation of concentration perturbations
while suppressing it along side branches?
Perturbations propagate along dimers with large concentrations
They cascade down the concentration gradient and thus directional
Free concentrations of intermediate proteins are lowSM, I. Ispolatov, PNAS in press (2007)
Implications of our results
Cross-talk via small-world topology is suppressed, but… Good news: on average perturbations via
reversible binding rapidly decay Still, the absolute number of
concentration-coupled 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
Intra-cellular noise 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?
3-step chains exist in bacteria: anti-anti-sigma-factors anti-sigma-factors sigma-factors 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.) Other (catalytic) mechanisms spread perturbations
even further Feedback control of the overall protein
abundance?
Future work
KineticsNon-specific vs specific How quickly the equilibrium is
approached and restored? Dynamical aspects of noise
How specific interactions peacefully coexist with many non-specific ones
Kim Sneppen NBI, Denmark
Iaroslav IspolatovResearch scientistAriadne Genomics
THE END
Genetic interactions 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)
S. cerevisiae curated PPI network used in our study
Genome-wide protein binding networks
Nodes - proteins Edges - protein-
protein bindings Experimental data
are binary while real interactions are graded one deals only with topology
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)
100
101
102
103
104
105
10610
0
101
102
103
104
concentration (molecules/cell)
hist
ogra
mKij=max(Ci,Cj)/20
total concentration Ci
bound concentrations Dij
free concentration Fi
Indiscriminate cross-talk is suppressed
What did we learn from topology?
1. Broad distribution of the degree K of individual nodes
2. Degree-degree correlations and high clustering
3. 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 networkshave small-world property
Large-scale Y2H experiment
86% of proteins could be connected
Curated dataset used in our study
83% in this plot
S. cerevisiae
Why small-world matters? 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
2
1
3
mRNA polyadenylation;
protein sumoylation
unfolded protein binding
G2/M transition of cell cycle
mRNA, protein, rRNA export from
nucleus
RNA polymerase I, III
RNA polymeras
e II
35S primary transcript processing
protein phosphatase type 2A
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
Propagation to 3rd neighbors
CDC55| 2155 | 8600 | 1461 | protein biosynthesis* | protein phosphatase type 2A activity | CPR6| 4042 | 18600 | 114 | protein folding | unfolded protein binding* | HSC82| 4635 | 132000 | 4961 | telomere maintenance* | unfolded protein binding* | HSP82| 6014 | 445000 | 115 | response to stress* | unfolded protein binding* | KAP95| 4176 | 51700 | 41 | protein import into nucleus | protein carrier activity | MTR10| 5535 | 6340 | 6 | protein import into nucleus* | nuclear localization sequence binding | NUP60| 102 | 4590 | 1693 | telomere maintenance* | structural constituent of nuclear pore | PPH21| 874 | 5620 | 95 | protein biosynthesis* | protein phosphatase type 2A activity | PPH22| 930 | 4110 | 72 | protein biosynthesis* | protein phosphatase type 2A activity | PPH3| 1069 | 2840 | 200 | protein amino acid dephosphorylation* | protein phosphatase type 2A activity | RPD3| 5114 | 3850 | 269 | chromatin silencing at telomere* | histone deacetylase activity | RTS1| 5389 | 300 | 80 | protein biosynthesis* | protein phosphatase type 2A activity | SAP30| 4714 | 704 | 80 | telomere maintenance* | histone deacetylase activity | SAP4| 2195 | 279 | 20 | G1/S transition of mitotic cell cycle | protein serine/threonine phosphatase activity | SDF1| 6101 | 5710 | 451 | signal transduction | molecular function unknown | SSA1| 33 | 269000 |40441 | translation* | ATPase activity* | SSA2| 3780 | 364000 |83250 | response to stress* | ATPase activity* |
• 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 concentrationssame network, Total=16
'RPS10A' 'SPC72' [ 1.4732] 'SEC27' 'URA7' [ 1.2557] 'HTB2' 'YBR273C' [ 1.3774] 'HTB2' 'TUP1' [ 1.2796] 'RPS10A' 'AIR2' [ 2.3619] 'HTB2' 'UFD2' [ 1.3717] 'HTB2' 'YDR049W' [ 1.3645] 'HTB2' 'PLO2' [ 1.2640] 'HTB2' 'YDR330W' [ 1.3774] 'RPN1' 'GAT1' [ 1.4277] 'HTB2' 'YFL044C' [ 1.3774] 'SEC27' 'STT3' [-1.2321] 'GIS2' 'STT3' [ 1.3437] 'HTB2' 'YGL108C' [ 1.3774] 'HTB2' 'UFD1' [ 1.3744] 'RPS10A' 'AIR1' [ 2.3833] 'HTB2' 'FBP1' [ 1.3576] 'HTB2' 'YMR067C' [ 1.3510]
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 | cytosolYBR273C| 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 unknownYMR067C| 4506 | ubiquitin-dependent protein catabolism* | molecular function unknown | cytoplasm*
Propagation to 4th neighborsin Krogan nc
Weight of links
Perturbations sign-alternate j Dij/Ci=1-Fi /Ci <1
thus perturbations always decay
Resistor network analogy
• j~Fj/Fj – potentials, Dij , Fj , Ci –currents
• Dij – conductivity between interacting nodes
• Fi – shunt conductivity to the ground
<1/Kd>=1/5.2nMclose to our choice of 10nM
Data from PINT database (Kumar and Gromiha, NAR 2006)
How much data is out there?
Species Set nodes edges # of sources
S.cerevisiae HTP-PI 4,500 13,000 5
LC-PI 3,100 20,000 3,100
D.melanogaster HTP-PI 6,800 22,000 2
C.elegans HTP-PI 2,800 4,500 1
H.sapiens LC-PI 6,400 31,000 12,000
HTP-PI 1,800 3,500 2 H. pylori HTP-PI 700 1,500 1
P. falciparum HTP-PI 1,300 2,800 1
Breakup by experimental technique in yeast
BIOGRID database S. cerevisiae
Affinity Capture-Mass Spec 28172
Affinity Capture-RNA 55
Affinity Capture-Western 5710
Co-crystal Structure 107
FRET 43
Far Western 41
Two-hybrid 11935
Total 46063
Sprinzak et al., JMB, 327:919-923, 2003
Christian von Mering*, Roland Krause†, Berend Snel*, Michael Cornell‡, Stephen G. Oliver‡, Stanley Fields§ & Peer Bork*NATURE |VOL 417, 399-403| 23 MAY 2002
TAP-Mass-Spec
Yeast 2-hybrid
HHT1
top related