Transcript Slide 1

Intrinsically Disordered Proteins:
from lack of structure to
pleiotropy of functions
Lilia Iakoucheva
University of California, San Diego
Ordered Proteins
Disordered Proteins
Uversky and Dunker, 2012, Anal Chem
Outline
 Characterization and properties of IDPs
 Functional repertoire of IDPs
 Post-translational modifications and disorder
 Importance for molecular recognition
 Disorder and diseases
Structure is required for function
1894
“Lock-and-key”
Emil Fischer
1950
“Configurational
adaptability”
Fred Karush
1958
“Induced fit”
Daniel Koshland
1965
“Conformational
selection”
Monod-Wyman-Changeux
Protein structure-function paradigm
Amino Acid Sequence
3D Structure
Function
Examples of disordered proteins
Some proteins/regions could function
without being folded…
 Tail of histone H5 (Aviles et al, Eur. J. Biochem. 1978)
… and later tails of other histones
 95-residue long disordered segment
of calcineurin (Kissinger et al, Nature, 1995)
 Cyclin-dependent kinase inhibitor p21Waf1/Cip1/Sdi1
(Kriwacki et al, PNAS, 1996)
Re-assessing structure-function paradigm
Amino Acid Sequence
Amino Acid Sequence
3D Structure
Order
Disorder
Function
Function
What is disorder?
Protein regions (or entire proteins) lacking
stable II and III structure and existing in
the ensemble of conformations with
dynamically changing Ramachandran angles
Disorder is experimentally detected by
• X-ray crystallography
• NMR spectroscopy
• Circular Dichroism (CD)
• Limited proteolysis (LP)
• Hydrodynamic methods
Bracken et al, Curr Opin Struct Biol. 2004, 570; Receveur-Bréchot et al, Proteins, 2006, 24
Compositional bias
DisProt – database of disordered proteins
DisProt-Order/Order
0.6
0.4
DisProt 4.9 (2009)
DisProt 3.4 (2006)
0.2
0.0
-0.2
-0.4
-0.6
-0.8
CWY I F V L H TNA G DMK R SQ P E
↓Aromatic,
hydrophobic
Order-promoting
Residues
↑Charged,
hydrophilic
Disorder-promoting
Dunker et al, 2001, JMGM; Radivojac et al, 2007, Biophys J
Charge-hydrophobicity bias
↑ Net charge
↓ Hydrophobicity
↓ Net charge
↑ Hydrophobicity
Uversky et al, 2000, Proteins 41:415-427
Disorder prediction
Amino acid sequence codes for protein structure
Does amino acid sequence code for
the lack of structure?
Keith Dunker group – first Predictor Of Natural
Disordered Regions PONDR
Nature, 2011
Protein Disorder Predictors
The PONDR-FIT meta-predictor combines several methods. Use it and other predictors
here.
Xue, B., R. L. DunBrack, R.W. Williams, A.K. Dunker, and V. N. Uversky (2010)
"PONDR-Fit: A meta-predictor of intrinsically disordered amino acids," Biochim.
Biophys. Acta (in press) doi:10.1016/j.bbapap.2010.01.011
PONDRFITTM
Linding R, Jensen LJ, Diella F, Bork P, Gibson TJ, Russell RB. "Protein disorder
prediction: implications for structural proteomics." Structure. 2003;11(11):1453-9, PMID: DisEMBLTM
14604535
Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT. "Prediction and functional
analysis of native disorder in proteins from the three kingdoms of life." J Mol Biol.
2004;337(3):635-45, PMID: 15019783
DISOPRED2
MacCallum B. "Order/Disorder Prediction With Self Organising Maps." CASP 6 meeting,
DRIPPRED
Online paper
Cheng J, Sweredoski M, Baldi P. "Accurate Prediction of Protein Disordered Regions by
Mining Protein Structure Data" Data Mining and Knowledge Discovery. 2005; 11(3):213- DISpro
222, Online Paper
Prilusky J, Felder CE, Zeev-Ben-Mordehai T, Rydberg EH, Man O, Beckmann JS, Silman
I, Sussman JL. "FoldIndex: a simple tool to predict whether a given protein sequence is
FoldIndex©
intrinsically unfolded." Bioinformatics. 2005;21(16):3435-8, PMID: 15955783
Linding R, Russell RB, Neduva V, Gibson TJ. "GlobPlot: Exploring protein sequences for
GlobPlot 2
globularity and disorder." Nucleic Acids Res. 2003;31(13):3701-8, PMID: 12824398
Dosztanyi Z, Csizmok V, Tompa P, Simon I. "IUPred: web server for the prediction of
intrinsically unstructured regions of proteins based on estimated energy content."
Bioinformatics. 2005;21(16):3433-4, PMID: 15955779
IUPred
Romero P, Obradovic Z, Li X, Garner EC, Brown CJ, Dunker AK. "Sequence complexity
of disordered protein." Proteins. 2001;42(1):38-48, PMID: 11093259
PONDR®
Coeytaux K, Poupon A. "Prediction of unfolded segments in a protein sequence based on
amino acid composition." Bioinformatics. 2005;21(9):1891-900, PMID: 15657106
PreLink
Yang ZR, Thomson R, McNeil P, Esnouf RM. "RONN: the bio-basis function neural
network technique applied to the detection of natively disordered regions in proteins."
RONN
http://www.disprot.org/
predictors.php
Eukaryotic proteomes are more disordered
Dunker et al, Gen Inf, 2000
IUpred
Pancsa et al, PLoS One, 2012
“This large jump in putatively
disordered proteins in
multicelled, rather than
singlecelled, organisms is
both remarkable and
unexpected”
194 eukaryotes
69 bacteria
18 archaea
Disorder and Functions
Examples
Function
Description
Protein
modification
Phosphorylation, acetylation,
glycosylation, methylation,
ubiquitination, fatty
acylation
histones, 4-E BP,
CFTR, Bcl-2,
neuromodulin,
HMG-I(Y), p53
Molecular
recognition
Protein-DNA, protein-RNA,
protein-protein, proteinligand interactions
p53, max, fos, jun,
myc, α-synuclein,
CDK inhibitors p21,
p57, p27, TF
Phages, viruses, bacterial
Macromolecular
flagellum, ribosome,
assembly
spliceosome, nuclear pore
Flexible linkers, entropic
Entropic chains
springs, bristles
flagellin, SR
proteins, ribosomal
prot, Nups
fd g3p, RPA, titin,
neurofilament H
Dunker et al, 2002, Biochemistry
Protein modifications - phosphorylation
•
Reversible PTM of
phosphate transfer from
ATP to S, T or Y
• ~ ⅓ of eukaryotic proteins
are phosphorylated
• Disordered regions often
carry phosphorylation sites
charged
exposed
hydrophylic
flexible
Phos-sites are enriched in IDRs
DisPhos
http://www.dabi.temple.edu/disphos/
KINASES & TARGETS
More kinases
that target IDPs
More kinase
targets are IDPs
S – structured
M – moderately structured
U - unstructured
Iakoucheva et al, NAR, 2004
Gsponer et al, Science, 2008
Ub substrates are disordered
β-catenin peptide:
15 out of 26 aa
are disordered
Wu et al, Mol Cell, 2003
p27 peptide:
14 out of 24 aa
are disordered
Hao et al, Mol Cell, 2005
pSic1 protein:
Sic1 is disordered
even in the complex
with Cdc4
Mittag et al, Structure, 2010
Molecular recognition
Disordered regions are commonly used for binding
to multiple partners
C-terminus of p53
Oldfield et al, BMC Genomics, 2008
NCBD domain of CBP/p300
Wright and Dyson, Curr Opin Struct Biol, 2009
Mechanisms of binding for IDPs
How do disordered proteins bind to their targets?
Induced folding
First binding
then folding
Conformational selection
First folding
then binding
Coupled/synergistic
Simultaneous folding and binding,
or even binding without folding (Sic1)
Molecular Recognition Features (MoRFs)
p53
Antigen CD2
Dunker, Structure, 2007
MoRFs - short disorder-to-order
transition binding regions
MoRFpred
http://biomine.ece.ualberta.ca/MoRFpred/
Summary
 Proteins can carry intrinsically disordered regions
 These regions can be predicted from sequence
 Eukaryotic proteins are more disordered
 IDRs perform important functional roles: posttranslational modifications, molecular recognition etc
 Disordered regions can undergo disorder-to-order
transition and form MoRFs
Disorder and disease
Cancer
Signaling
Swiss-Prot
PDB
Disorder and disease
Individual examples of IDPs/IDRs involved
in human diseases:
p53 (cancer), BRCA1 (cancer), a-synuclein (PD,
AD, dementia, Down syndrome), amyloid b (AD),
tau (AD), prion (TSEs), amylin (Type II diabetes),
hirudin and thrombin (CVD), HPV (cancer) etc
Disease-associated mutations
Disease mutations impact protein
Structure:
Function:
- Folding
- Post-translational modifications
- Oligomerization
- Binding to partners
- Stability
- Intracellular localization
…
- Activity
…
Disease-associated mutations
 Many predictors of the functional impact of
SNPs are available (SIFT, POLYPHEN, SNP3D
etc)
 Majority rely on known protein 3D structure
and evolutionary conservation
Do disease mutations occur
in the regions of disorder?
Disease mutations and disorder
Disease mutations are enriched in ordered regions
100
***
IDR
OR
mutations, %
80
60
40
20
0
DM
Poly
NES
Disease mutations cause disorder-to-order transition
OR
25
***
DM
Poly
NES
20
mutations, %
IDR
15
10
5
0
D->O
O->D
Vacic et al, PLoS CB, 2012
Disease mutations, sec structure and MoRFs
Transitions from helix/strand to loop and
vice versa are enriched in disease
DMs cause loss of MoRFs
D→O and O→D
D→O
Substitution
R→W
R→C
R→H
E→K
R→Q
O→D
D→O disease
mutations, %
13.1
10.3
7.6
6.7
6.3
44%
Substitution
L→P
C→R
G→R
W→R
F→S
O→D disease
mutations, %
11.9
6.6
6.1
4.1
3.6
32.2%
Hypothetical mechanism?
Codons for
Arginine:
CGG
CGT
CGC
CGA
AGA
AGG
CpG methylation
TGG
TGT
TGC
TGA
AGA
AGG
R-> W
R-> C
R-> C
R-> Stop
N/A
N/A
R-> W and R-> C are among the most frequent
mutations in the disease dataset
AMD simulations of D->O mutation
Disorder predictions for p63 DBD
Red – more heavily sampled by mutant
Black – by WT
AMD simulations for p63 DBD
Disease Models
Disorder-centric view at disease mutations complements
structure-centric view
Acknowledgements
Rockefeller University
Indiana University
Columbia University
UCSD
Jurg Ott
Chad Haynes
Fei Ji
Vladimir Vacic
Keith Dunker
Predrag Radivojac
Vladimir Uversky
Phineus Markwick
Andy McCammon
Funding:
Disordered Proteins Database DisProt
http://www.DisProt.org
List of Disorder Predictors
http://www.disprot.org/predictors.php
Phos Sites Predictor DisPhos
http://www.dabi.temple.edu/disphos/
Ub Sites Predictor UbPred
http://www.ubpred.org/
MoRF predictor
http://biomine.ece.ualberta.ca/MoRFpred/
[email protected] – Lilia Iakoucheva,
http://psychiatry.ucsd.edu/faculty/lIakoucheva.html
Advantages of being disordered
 Low-affinity/high-specificity binding
 Broad binding diversity
 Ability to form large interaction surfaces
 Greater capture radius (“fly-casting”
mechanism)
 Facilitate alternative splicing
 Facilitate post-translational modifications
Current predictors and IDR mutations
PolyPhen (and SIFT) under-predicts disease relevance of IDR mutations
Disorder in interaction networks
Are network hubs disordered?
Yeast interactome
hubs
ends
order
proteins, %
80
60
40
20
0
>=30
>=40
>=50
>=60
>=70
>=80
>=90 >=100
length of predicted disordered region, aa
Haynes et al, 2006, PLoS CB
Ordered hubs – disordered partners
14-3-3 proteins
– >200 binding targets
14-3-3 TARGETS predicted as disordered
(Bustos et al, Proteins, 2006)
All targets bind to the same region of 14-3-3
Differences in 14-3-3 side chains conformations
in different structures
Peptides are highly hydrated in the bound state
(i.e. likely disordered in the unbound state)
Oldfield et al, BMC Genomics, 2008