part ii. prediction of functional regions within disordered proteins zsuzsanna dosztányi mta-elte...
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PART II.PART II.Prediction of functional Prediction of functional regions within disordered regions within disordered proteinsproteins
Zsuzsanna DosztányiMTA-ELTE Momentum Bioinformatics GroupDepartment of Biochemistry Eotvos Lorand University, Budapest, [email protected]
Protein disorder is prevalent
20
40
60
0
LD
R (
40<
) p
rote
in, %
kingdom
B
A
E
Protein disorder is functional
Xie H et al. J Proteome Res. 2007, 6, 1882-1898
Protein disorder is important
Prion protein Prion disease
CFTR Cystic fibrosis
Alzheimer’s
-synuclein Parkinson’s
p53, BRCA1 cancer
Functions of intrinsically disordered proteins
I Entropic chains
II Linkers
III Molecular recognition
IV Protein modifications (e.g. phosphorylation)
V Assembly of large multiprotein complexes
Interactions of IDPsComplex between p53 and MDM2
Coupled folding and binding
Interactions of proteins
Interactions of proteins
Coupled folding and binding
Functional advantages
Weak transient, yet specific interactions Post-translational modifications Flexible binding regions that can overlap Evolutionary plasticity
SignalingRegulation
Coupled folding and binding Experimental difficulties
Highly flexible Weak interactions Short half-life
Complexes of IDPs in the PDB: ~ 200 Known instances: ~ 2,000 Estimated number of such interactions in the
human proteome: ~ 100,000
Interactions of IDPs
F19, W23 and L26
p27
Cyclin-dependent kinase (Cdk) inhibitor, p27Kip1 (p27) Cell cycle regulation Binds to cdk-cyclin komplex and inhibitis their activity Fully disordered protein
p27
p27
Partial unfolding enables the phosphorylation of Tyr88, starting a series of signaling events that leads to the beginning of S phase.
Prediction of functional regions within IDPs
Disordered binding regions (ANCHOR) Linear motifs (ELM, SlimPred) Morfs (Morfpred)
Specific conservation pattern
Disordered protein complexes
Complex between p53 and MDM2
• Interaction sites are usually linear (consist of only 1 part)
• enrichment of interaction prone amino acids
Sequence
Binding sites
No need for structure, binding sites can be
predicted from sequence alone
Prediction of disordered binding regions – ANCHOR
What discriminates disordered binding regions? A cannot form enough favorable interactions with their
sequential environment It is favorable for them to interact with a globular protein
Based on simplified physical model Based on an energy estimation method using statistical
potentials Captures sequential context
ANCHOR
Human p53 C –terminal region
ANCHOR and linear motifsNCOA2 transcription co-activator
The regions between 600-800 is disordered Contains 3 receptor binding motifs: xLxxLLx (LIG_NRBOX)
LMs and Disordered Binding Regions
Linear motifs and disordered binding regions often overlap
Complementary approaches
Prediction of disordered binding regions can help to increase likelihood of true instances
LMs and Disordered Binding Regions
Linear motifs and disordered binding regions often overlap
Complementary approaches
Prediction of disordered binding regions can help to increase likelihood of true instances
Machine learning approaches
SlimPred: trained on ELM database
Morfpred: trained on short chains in complex
Very small datasets
Negative datasets
Conservation
Conservation patterns of linear motifs
No evolutionary constraints to keep the structure Strong constraints on functional site
Island-like conservation
SlimPrints
Generates sequence alignments of orthologous sequences
Relative conservation score per position Filters out less reliable regions Fails if sequences are too divergent, or too similar http://bioware.ucd.ie/slimprints.html.