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Prediction of protein disorder
Zsuzsanna Dosztányi
Institute of Enzymology, Budapest, Hungary
[email protected]
Protein Structure/Function Paradigm
Dominant view: 3D structure is a prerequisite for protein function
Amino acid sequence
Structure
Function
But….
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Heat stability
Protease sensitivity
Failed attempts to crystallize
Lack of NMR signals
“Weird” sequences …
IDPs
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Intrinsically disordered proteins/regions
(IDPs/IDRs)
Do not adopt a well-defined structure in
isolation under native-like conditions
Highly flexible ensembles, little secondary
structure, no folded structure
Functional proteins
LDR (40<) protein, %
Protein disorder is prevalent
60
E
40
A
20
B
0
kingdom
Protein disorder is important
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Prion protein
Prion disease
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CFTR
Cystic fibrosis
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t
Alzheimer’s
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-synuclein
Parkinson’s
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p53, BRCA1
cancer
Protein disorder is functional
regulatory
signaling
80
biosynthetic
metabolic
protein (%)
60
40
20
0
30<
40<
50<
60 <
length of disordered region
Iakoucheva et al. (2002) J. Mol. Biol. 323, 573
p53 tumor suppressor
transactivation
DNA-binding
TAD
DBD
tetramerization regulation
TD
RD
Wells et al. PNAS 2008; 105: 5762
Heterogeneity in protein disorder
Transient
structures
Flexible loop
RC-like
Compact
Modularity in proteins
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Many proteins contains multiple domains
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Composed of ordered and disordered segments
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Average length of a PDB chain is < 300
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Average length of a human proteins ~ 500
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Average length of cancer-related proteins > 900
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Structural properties of full length proteins …
Bioinformatics of protein disorder
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Part 1
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Databases
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Prediction of protein disorder
Part 2
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Prediction of functional regions within IDPs
Datasets
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Ordered proteins in the PDB
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over 94000 structures
few 1000 folds
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Some structures in the PDB classify as disordered!
only adopt a well-defined structure in complex
in crystals, with cofactors, proteins, …
Disorder in the PDB
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Missing electron density regions from the PDB
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NMR structures with large structural variations
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Less than 10% of all positions
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Usually short (<10 residues), often at the termini
Disprot
www.disprot.org
Current release: 6.02
Release date: 05/24/2013
Number of proteins: 694
Number of disordered regions: 1539
Experimentally verified disordered
proteins collected from literature
(X-ray, NMR, CD, proteolysis, SAXS,
heat stability, gel filtration, …)
Additional databases
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Combining experiments and predictions
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Genome level annotations
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MobiDB: http://mobidb.bio.unipd.it
D2P2: http://d2p2.pro
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IDEAL: http://www.ideal.force.cs.is.nagoya-u.ac.jp/IDEAL
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Amino acid compositions
He et al. Cell Res. 2009; 19: 929
Sequence properties of disordered proteins
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Amino acid compositional bias
High proportion of polar and charged amino acids
(Gln, Ser, Pro, Glu, Lys)
Low proportion of bulky, hydrophobhic amino acids
(Val, Leu, Ile, Met, Phe, Trp, Tyr)
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Low sequence complexity
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Signature sequences identifying disordered proteins
Protein disorder is encoded in the amino acid sequence
Mean net charge
Uversky plot: charge-hydrophobicity (two
parameters)
Mean hydrophobicity
Uversky (2002) Eur. J. Biochem. 269, 2
Making it position specific: FoldIndex
http://bip.weizmann.ac.il/fldbin/findex
p53
Prilusky (2005) Bioinformatics 21, 3435
Disorder Prediction Methods
Amino acid propensity scales
GlobPlot
Compare the tendency of amino acids:
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to be in coil (irregular) structure.
to be in regular secondary structure elements
Linding (2003) NAR 31, 3701
GlobPlot
GlobPlot
From position specific predictions
Where are the ordered domains?
Longer disordered segments?
Noise vs. real data
GlobPlot: http://globplot.embl.de/
downhill regions correspond to
putative domains (GlobDom)
up-hill regions
correspond to predicted
protein disorder
Disorder Prediction Methods
Physical principles
IUPred
If a residue cannot form enough favorable
interactions within its sequential environment,
it will not adopt a well defined structure
it will be disordered
Dosztanyi (2005) JMB 347, 827
Energy description of proteins
Estimation of interaction energies based on
statistical potentials:
Calculated from the frequency of amino acid interactions in
globular proteins alone, based on the Boltzmann hypothesis.
For example:
 L-I interaction is frequent (hydrophobic effect)
L-I interaction energy is low (favorable)
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K-R interaction is rare (electrostatic repulsion)
K-R interaction energy is high (unfavorable)
Predicting protein disorder - IUPred
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The algorithm:
…PSVEPPLSQETFSDL WKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPRVA PAPAAPTPAA...
Based only on the composition of environment of D’s
we try to predict if it is in a disordered region or not:
Amino acid
composition
of environment:
A – 10%
C – 0%
D – 12 %
E – 10 %
F–2%
etc…
Estimate the
interaction
energy between
the residue and
its environment
Decide the
probability of the
residue being
disordered
based on this
IUPred: http://iupred.enzim.hu/
Disorder Prediction Methods
Machine learning
DISOPRED2
Binary classification problem
Ward (2004) JMB 337, 635
DISOPRED2
…..AMDDLMLSPDDIEQWFTED…..
SVM with linear kernel
Assign label: D or O
F(inp)
D
O
DISOPRED2
Cutoff value!
PONDR VSL2
Differences in short and long disorder
 amino acid composition
 methods trained on one type of dataset tested on
other dataset resulted in lower efficiencies
PONDR VSL2: separate predictors for short and long
disorder combined
length independent predictions
Peng (2006) BMC Bioinformatics 7, 208
PONDR-FIT
Disorder prediction methods
Meta-predictor
PONDR VLXT
PONDR VL3
PONDR VSL2
Sequence
IUPred
ANN
Prediction
FoldIndex
TopIDP
Xue et al. Biochem Biophys Acta. 2010; 180: 996
Complexity of protein disorder
Prediction of protein disorder
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Disordered residues can be predicted from
the amino acid sequence
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Methods can be specific to certain type of
disorder
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~ 80% at the residue level
accordingly, accuracies vary depending on
datasets
Predictions are based on binary
classification of disorder