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SP4: In silico methods. Partner 16A EMBL (Russell, Bork) Partner 1 (CRG Serrano) Partner 5 (NKI Perrakis) Partner 10 (HU Margalit) Partner 12 (CCNet) Partner 17 IRB (Aloy) Partner 3A (Paris-Sud, Janin). SP4 In silico methods.

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slide1

SP4: In silico methods

Partner 16A EMBL (Russell, Bork)

Partner 1 (CRG Serrano)

Partner 5 (NKI Perrakis)

Partner 10 (HU Margalit)

Partner 12 (CCNet)

Partner 17 IRB (Aloy)

Partner 3A (Paris-Sud, Janin)

slide2

SP4 In silico methods

  • WP4.1: Target identification & annotationPartners: EMBL-Bork/Russell, HU, CCNet, IRB
  • WP4.2: Complex modelingPartners: EMBL-Russell, IRB, Gif, CRG
  • WP4.3: Interface to the scientific community & scientific data managementPartners: NKI, EMBL-Russell, CCNet
wp4 1 target identification annotation partners embl hd hu ccn irb
WP4.1: Target identification & annotationPartners EMBL-HD, HU, CCN, IRB

Activities in:

  • Interaction prediction (HU, EMBL-Bork)
  • Complex prediction & ranking, the ‘list of 20’ (IRB)
  • Complex visualisation (CCNet/EMBL)
  • Data gathering (CCNet)

(e.g. protein-chemical interactions)

  • Gel processing (EMBL)
the list of 20
The list of 20

Aloy group (IRB)

experimental tests on the 20
Experimental tests on the 20

Aloy, Seraphin, van Tilburgh & Dziembowski groups

wp4 2 complex modeling partners embl hd irb gif crg
WP4.2: Complex modelingPartners EMBL-HD, IRB, Gif, CRG

Activities in:

  • Complex modelling
    • Automated procedures (EMBL-Russell/IRB)
    • Interaction prediction via structure (EMBL-Russell/IRB)
  • New methods for modelling
    • FoldX (CRG/EMBL)
    • High-throughput Docking (IRB)
  • Analyses, individual models (Everybody)
slide8

Building a complex from pieces

For 636 complexes in yeast

3505 : proteins modelable

419 : complexes single subunit models

224 : 2+ subunit models

122 : 3+ subunit models

Aloy et al, Curr. Opin. Struct. Biol, 2005.

slide10

eIF2 /eIF2B complex

SUI3

GCD7

GCD2

GCN3

SUI2

GCD6

SUI4

GCD1

SUI4

GCD7

SUI4

SUI3

GCD1

GCN3

Sub-complexes

Preliminary reconstructions (Bettina Boettcher)

Intact complex MS

(Carol Robinson)

Modelling (Damien Devos)

slide13

Defining new interfaces:

424 candidate interfaces to date

Complex 1

Complex 2

Complex 1

Superimposition

Superimposition

Complex 2

Complex 3

Complex 1,2 & 3

Complex 1 & 2

New complex

New complex

A common shape denotes a similar fold

slide14

Example: Transcription factor SPX dimer

New interface

E.coli dimer in one protein, forms nice interface in B.subtilis – good evidence from other sources (Myco TAP)

Complex 3

Complex 2

Complex 1

Domain 1, 1z3eA.c.47.1.12-1-trans3 (chain A) Transcriptional regulator SPX (B.subtilis)

Domain 2, 1z3eA.c.47.1.12-1-trans4 (chain B) Transcriptional regulator SPX (B.subtilis)

Domain 3, 1z3eB.a.60.3.1-1-trans1p (chain C) RNA polymerase alpha (B.subtilis)

Domain 4, 1z3eB.a.60.3.1-1-trans2p (chain D) RNA polymerase alpha (B.subtilis)

Domain 5, 1lb2E.a.60.3.1-1-trans1 (chain E) RNA polymerase alpha (E.coli)

Domain 6, 1lb2B.a.60.3.1-1-trans2 (chain F) RNA polymerase alpha (E.coli)

slide15

Enabling/disabling loops can predict mulimerization state

Monomer

S. Cerevisiae

Guanylate kinase

Homodimer

E. Coli

Guanylate kinase

enabling loop

E. Coli Guanylate kinase

V. Cholerae Guanylate kinase

Yeast Guanylate kinase

Mouse Guanylate kinase

Pig Guanylate kinase

Bovine Guanylate kinase

Homodimers

Monomers

slide16

When modelling fails – docking?

  • The Aloy group (IRB) is currently running many tens of thousands of docking experiments using Mare nostrum, the largest supercomputer in Europe
  • Aim is to identify promising docking candidates to help model key interactions of interest
slide17

Modelling versus docking

  • We can model an interaction structure if there is a previously determined structure containing parts homologous to the two interacting proteins

homology

homology

  • We can predict an interaction structure by docking if we have structures or models for parts of the interacting proteins
slide18

Large-scale Docking

36 million possible yeast protein interactions

slide19

Large-scale Docking

Unrefined

Refined

slide20

Using FoldX to assess docked or modelled interactions

Good Interaction, but many clashes, Model is not so good but could be rescued

By backbone moves/further docking

wp4 3 interface to the scientific community and scientific data management partners embl hd nki ccn
WP4.3: Interface to the scientific community and scientific data managementPartners: EMBL-HD, NKI, [CCN]

Activities in:

  • Web site maintenance
    • New data (copy number, structural annotation)
    • Various optimisation
    • YeastWiz
  • Complex target DB
    • Resting period for software development
    • Needs data. Listen to Tassos.
wp4 3 web site
WP4.3: Web site

Matthew Betts (EMBL)

yeast wiz ccnet

Interactions of known structure

Yeast Wiz (CCNet)

www.3drepertoire.org/yeastwiz

Windows XP

Linux

Manual (rather beta)

Accounts enabled Monday for everybody

Suggestions for new data promising

Matthew Betts (EMBL), Tomasz Ignasiak (CCNet)

current data contributions
Current data contributions

EMBL

HU

STRING (interactions)

SMART (orthologues)

3D Interaction predictions

Orthology

Models

Etc.

Dom-dom profiles

Context interactions

Enabling loops

3DR

CCN-DB

IRB

Protein-protein (>10 sources, manual)

Protein-chemical (Manual, TM)

Docking solutions

List of 20

slide29

The exosome

Damien Devos (w Carol Robinson)

slide30

Analysis of a “gold set” of 61 models of known interactions by FoldX

Easy case : Good Interaction Energy, few clashes

Good Model

slide31

Analysis of a “gold set” of 61 models of known interactions by FoldX

Bad Interaction, loads of clashes, interpenetrating mainchains

Bad Model

slide32

Analysis of a “gold set” of 61 models of known interactions by FoldX

Bad Interaction, many clashes, but Model could be rescued by

some backbone moves/ further docking

slide33

Analysis of a “gold set” of 61 models of known interactions by FoldX

Good Interaction, many clashes, interpenetrating mainchains, gaps in the structure

Bad Model

slide34

Analysis of a “gold set” of 61 models of known interactions by FoldX

Easy case : Good Interaction Energy, few clashes : Good Model

Good Interaction, many clashes:

- interpenetrating mainchains, gaps in the structure : Bad Model

- mainchains too close on a large region but this can be solved by

backbone moves/further docking

Bad Interaction, many clashes

- interpenetrating mainchains, gaps in the structure : Bad Model

- mainchains too close on a large region but this can be solved by

backbone moves/further docking (could improve the model?)

The magnitude of the local clashes correlate with the possibility to rescue or not a model

(mild clashes on a lot of residues), but still there are exceptions.

Could we really skip a step of visualization?

from protein protein interactions to domain domain interactions and back

From protein-protein interactions to domain-domain interactions and back

Hanah Margalit

The Hebrew University of Jerusalem

slide36

Modularity in protein-protein interactions

fine tuners

No

No

Yes

Yes

domain pairs

protein-protein interactions

what are the fine tuners of domain domain recognition

positive dataset

reliable protein-

protein interactions

negative dataset

reliable pairs of proteins that do not interact

What are the fine tuners of domain-domain recognition?
homodimers and monomers provide an ideal dataset

homodimers:

  • co-localized
  • co-expressed
  • interact
  • monomers:
  • co-localized
  • co-expressed
  • do not interact
Homodimers and monomers provide an ideal dataset

Domains that mediate homodimerization are found also in monomers

Database of 50 homodimers/monomers

with the same domain for which structural data is available

different fine tuners determine the self interaction potential of domains

P

P

Interface residue

substitutions

Different fine-tuners determine theself-interaction potential of domains

homodimers

Phosphorylations

monomers

slide40

Enabling loops mediate homodimerization

Monomer

S. Cerevisiae

Guanylate kinase

Homodimer

E. Coli

Guanylate kinase

enabling loop

E. Coli Guanylate kinase

V. Cholerae Guanylate kinase

Yeast Guanylate kinase

Mouse Guanylate kinase

Pig Guanylate kinase

Bovine Guanylate kinase

Homodimers

Monomers

slide41

Disabling loops prevent homodimerization

Homodimer:

Bovine inositol

monophosphatase

Monomer: Bovine inositol polyphosphate 1-phosphatase

DL

Monomers

Homodimers

loop profiles

protein 1

homodimer

monomer

protein 2

Presence AND absence are informative

Loop profiles

A multiple-sequence alignment with locations of potential loops

enabling

loop

disabling

loop

the core set
The ‘core set’

64 / 73 are consistent (88%, p-value ≤ 3.2•10-6)

test set
Test set

80 proteins with documented oligomeric statebased on

experimental data

experimental oligomeric state

dimer

monomer

3

9

monomer

loop profile

63

5

dimer

72/80 are consistent (90%, p-value ≤ 5•10-6)

large scale prediction of domain domain interaction

core

test

DL

DL

DL

Monomer

EL

Homodimer

31 monomers

>1000

predictable

108 homodimers

Large-scale prediction of domain-domain interaction

pfkB carbohydrate kinase domain proteins

monomer

homodimer

slide46

Boundary loops

There are enabling/disabling loops that are located outside domain boundaries

slide47

Dominance of disabling over enabling loops

ccrA(B. Fragilis)

RNase Z (B. Subtilis)

Metallo-beta-lactamase domain

summary
Summary

1. Enabling/disabling loops are newly discovered

fine-tuners of domain-domain interaction

2. Their presence/absence is highly preserved in evolution, implying that prevention of unwanted interactions is an evolutionary constraint

3. Prediction of self-interaction potential of

domains according to loop profiles is highly accurate (~90%)

extension of the analysis

Homodimers of multi-domain proteins

Heterodimers of proteins with self-interacting domains

Heterodimers of proteins with different domains

Extension of the analysis