propagation of noise and perturbations in protein binding networks n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Propagation of noise and perturbations in protein binding networks PowerPoint Presentation
Download Presentation
Propagation of noise and perturbations in protein binding networks

Loading in 2 Seconds...

play fullscreen
1 / 56

Propagation of noise and perturbations in protein binding networks - PowerPoint PPT Presentation


  • 99 Views
  • Uploaded on

Propagation of noise and perturbations in protein binding networks. Sergei Maslov Brookhaven National Laboratory. Experimental interaction data are binary instead of graded  it is natural to study topology Very heterogeneous number of binding partners (degree)

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Propagation of noise and perturbations in protein binding networks' - benny


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
propagation of noise and perturbations in protein binding networks

Propagation of noise and perturbations in protein binding networks

Sergei Maslov

Brookhaven National Laboratory

slide2

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
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
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 Ciof 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
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 k ij

Free concentrations: Fi

Bound concentrations: Dij

Spearman rank correlation: 0.89

Pearson linear correlation: 0.98

Spearman rank correlation: 0.89

Pearson linear correlation: 0.997

Robustness with respect to assignment of Kij
numerical study of propagation of perturbations
Numerical study of propagation of perturbations
  • We simulate a twofold increase of the abundance C0 of just one protein
  • Proteins with equilibrium free concentrations Fichanging by >20% are significantly perturbed
  • We refer to such proteins i as concentration-coupled to the protein 0
  • Look for cascadingperturbations
resistor network analogy
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
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
Exponential decay of perturbations

O – real

S - reshuffled

D – best

propagation

slide11

HHT1

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

slide12
What conditionsmake some

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

slide16

Perturbations propagate along dimers with large concentrations

They cascade down theconcentration gradient and thus directional

Free concentrations of intermediate proteins are low

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

cross talk via small world topology is suppressed but
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
Intra-cellular noise
  • Noise is measured for total concentrations Ci (Newman et al. Nature (2006))
  • Needs to be converted in biologically relevantbound (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
Could it be used for regulation and signaling?
  • 3-step chains exist inbacteria: 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?
kinetics non specific vs specific
KineticsNon-specific vs specific
  • How quickly theequilibrium is approached and restored?
  • Dynamical aspects ofnoise
  • How specific interactions peacefully coexistwith many non-specific ones
genetic interactions
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 of772 such pairs (18% of the total, P-value 10-216)

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

genome wide protein binding networks
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

S. cerevisiae curated PPI network used in our study

going beyond topology and modeling the binding equilibrium and propagation of perturbations
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)

what did we learn from topology
What did we learn from topology?
  • 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
Protein binding networkshave 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
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
slide41

2

1

3

slide42

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 3 rd neighbors
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 concentrationssame network, Total=16

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* |

propagation to 4 th neighbors in krogan nc

'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]

Propagation to 4th neighborsin 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
Weight of links
  • Perturbations sign-alternate
  • j Dij/Ci=1-Fi /Ci <1thus perturbations always decay
resistor network analogy1
Resistor network analogy
  • j~Fj/Fj – potentials, Dij , Fj , Ci –currents
  • Dij – conductivity between interacting nodes
  • Fi– shunt conductivity to the ground
slide47

<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
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
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

slide53

TAP-

Mass-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