prediction of sh3 domain binding motifs presented by: siba ismael supervised by: mazen ahmad...
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Prediction of SH3 Domain Binding Motifs
Presented by: Siba IsmaelSupervised by: Mazen AhmadUniversity of Saarland
Saarbrücken, 17.10.08
Outline 2
Outline
SH3 motif and proline-rich domains- Motivation to find SH3 domains binding sites-Why proline-rich domains?
Binding Free Energy Method: What flanking sequences govern binding specificity
Materials and Methods; Bioinformatics
Results of Prediction
Conclusions and Outlook
Introduction- SH3 Motif and Proline-Rich Domains
3
SH3 DomainsMotivation- Assembly Comprise 60 residues
Play assembly and regulatory roles.
Assembly role: example; Grb2
Cascade: Growth factor receptor tyrosine kinase Grb2 SOS Ras MAPK
- Play roles in cell growth and differentiation
Introduction- SH3 Motif and Proline-Rich Domains
4
SH3 DomainsMotivation- Regulation Regulation: example; Src
Built-in SH2+SH3: inactivation (autoinhibition)
Disruption: External SH2 and SH3 domains interaction-result in kinase activation
SH3 interactions: week- typical dissociation constant- essential for reversible switching mechanism.
Introduction- SH3 Motif and Proline-Rich Domains
5
Repetitive Proline-Rich Sequences
in many cases, thought to function as docking sites for signaling modules
found in the context of larger multidomain signaling proteins.
Binding: assembly and targeting of protein complexes involved in:- cell growth
- cytoskeletal rearrangements- transcription - postsynaptic signaling processes
play a regulatory role and autoinhibitory interactions
Introduction- SH3 Motif and Proline-Rich Domains
6
Repetitive Proline-Rich Sequences
Why proline in interaction modules?
Proline: unique amino acid in: - constraints on dihedral angles imposed by cyclic side chain- its resulting secondary structural preferences
Introduction- SH3 Motif and Proline-Rich Domains
7
Repetitive Proline-Rich Sequences Why proline in interaction modules?
propensity to form a polyproline type II (PPII) helix.- extended left-handed helical structure with three residues per turn.
- useful recognition motif: - carbonyls point out from the helical axis into solution - restricted backbone: entropy cost of binding reduced
- twofold rotational pseudosymmetry:
- two binding possibilities
- orientational switching differing domain function
Introduction- SH3 Motif and Proline-Rich Domains
8
Repetitive Proline-Rich Sequences Why proline in interaction modules?
The only naturally occuring N-substituted amino acid:
- sequence-specific recognition without high-affinity interaction.
- specific and low affinity interactions: - reversibility - intracellular signalling
Stable cis conformation- high kinetic barrier- rate limiting step
Introduction- SH3 Motif and Proline-Rich Domains
9
Proline-Rich Sequences vs. SH3 Interaction
PxxP motif: flanked by different specificity elements:- K/RxxPxxP and PxxPxK/R classes of ligand motif- single recognition surface: two N- to C-terminal orientations ligand binding
SH3 fold: two antiparallel β sheets at right angles.- in fold RT and n-Src loops: flanking specificity pockets
Aromatic SH3 groove PPII helix ridges (a pair of residues)
10
So how to detect the binding affinity to SH3 domains?
Computational Analysis!!!Solvation Energy!!
„BIOPHYSICS“
Binding Free Energy Components 11
Binding Free Energy mechanical energy to disassemble a whole into
separate parts scalar
Binding free energy cycle:- in terms of transfer free energiesWhy? - from a homogeneous dielectric environment (interactions: Coulomb's law)- to an inhomogeneous dielectric environment:
differing internal and external dielectric constants.
GGGGGbind 1243 )( GGG coulsolvbind
Binding Free Energy Components 12
Binding Free EnergySolvation Energy Contribution Solvation energy for the complex and each of its parts
But how to calculate solvation energy?
GGGsolv 24
proteinsolv
ligandsolvcomplexsolvsolv
G
GGG
Remember!! This stands for Coulombic
Binding Free Energy Components 13
Binding Free EnergySolvation Energy Contribution
Full solvation energy cycle
- Step 1: Total Solvation
- Step 2: charging of the solute in solution inhomogeneous presence of mobile ions. -Step 3: attractive solute-solvent dispersive interaction - Step 4: repulsive solute-solvent interaction
- Steps 5 and 6: null steps. - but used to offset unwanted energies
charging of the solute in vacuum homogeneous absence of mobile ions.
Binding Free Energy Components 14
Binding Free EnergySolvation Energy Contribution
APBS??
GGG npsolv
GGGp 62
)11
(8 0
2
inoutBornp a
qG
AVpG 4
dyyyuGG att )()()(53
)( 534 GGGGn
ACC??
Binding Free Energy Components 15
Binding Free EnergyIncluding Coulombic Contribution
the sum of pairwise Coulombic interactions:- for all atoms in the molecule - for a particular uniform dielectric
Coulomb‘s Law:
Potential Dielectric Energy:
ligandcoulproteincoul
complexcoulcoul
GG
GGG
1
221
4
1
r
qqF
r
qqU 21
12 4
1
Coulomb??
Binding Free Energy Components 16
Binding Free Energy Entropy Entropy: a measure of the
unavailability of a system’s energy to do work
- measure of the randomness of molecules in a system - central to the second law of thermodynamicsSpontaneous changes Entropy (isolated systems)
Binding Free Energy Components 17
Binding Free Energy van der Waals van der Waals force: attractive or
repulsive forces between molecules and per molecule:
not covalent bonds or electrostatic interaction of ions, but:
- permanent dipole–permanent dipole forces
- permanent dipole–induced dipole forces
- instantaneous induced dipole-induced dipole
Binding Free Energy Components 18
Poisson-Boltzmann Equation Differential equation – describes electrostatic interactions between
molecules in ionic solutions
models implicit solvation (continuum solvation )
solution.in ions theto
r position ofity accessibildependent -position for thefactor a :r)(
re temperatuthe:T constant,Boltzmann :k
proton, a of charge the:q ion, theof charge the:z
solute thefrominfinity of distance aat iion theofion concentrat :c
solute theofdensity charge :r)(
potential ticelectrosta the:r)( ,dielectricdependent -position the:r)(
B
i
i
f
Methods 19
Methods APBS Package: Adaptive Poisson–Boltzmann Solver:
- numerical solution for the Poisson-Boltzmann equation - modeling biomolecular solvation In my work:* apbs: electrostatic potential and polar solvation* acc: SASA calculation: „solvent accessible surface area“ nonpolar solvation* coulomb: coulombic interactions in vacuum
Pdb2pqr Package: platform-independent utility - converts protein files in PDB format to PQR format
Methods 20
Methods PQR file: PDB file temperature and occupancy columns;
replaced by the per-atom charge (Q) and radius (R)
Jackal: package for protein structure modeling scap: protein side-chain program: predicts side-chain conformations and side chains of a whole
protein and in mutates specified residues in a protein
R language Package: Statistical Language environment
Methods 21
Methods To predict a binding motif of length 10:
- chose the crystal structure of the peptide APSYSPPPPP complexed with the Abl SH3 domain - mutate it to other sequences
Try: predicton of 10 very good out of the 600 candidates, and 15 of the nonbinders almost all have a PxxP domain!
late with tempcompare
motifsdifferent 1-20 toReduce - 8
!late! with tempcompare
motifsdifferent 1-20 :Total - 10
motif?! PxxP thehave all
Fix P at P0 and P3
Methods 22
Methods From
literature: Binding Free Energy Difference to the base sequence with the following mutations:
23
Results
Results 24
Correlation?!
Correlation :
0.4530898
Correlation:
0.9534504 Correlation:
0.722554
Results 25
Reproducibility?!
Without vdW or entropy
correlation:0.5435262
For both
Correlation: 0.3357690Second compared to base
sequence Why not much good?
Results 26
Peptide Binding-Solvation Polar
Easier barrier to break for binders
Results 27
Coulombic Interactions
Mean Coulombic Energy is less for binders!
Results 28
Nonpolar Solvation Contribution
Neglicted effect!
Results 29
van der Waals Contribution
Major contribution to binding specificity
Results 30
Entropy Contribution
Most non-Binders Lost more Entropy upon Binding than did Binders!
Results 31
Binding Free Energy
Less Binding Free Energy for Binders!Easier barrier to break
Results 32
Separation of Binders from non-bindersPrediction
Linear Discriminant Analysis!
Results 33
From LiteratureBinders
NonbindersSequence Gpred
SKKEMQPTHP 19.6
ASQKMEPRAP 43.3
WELSSQPTIP 26.3
LAPASTPTSP 13.6
ASTPTSPSSP 11.4
SSPGLSPVPP 13.8
RGVLIEPVYP 38.9
DEPNLEPSWP 26.4
RLVGARPLLP 24.6
RTESEVPPRP 26.6
LASRPLPLLP 20.1
ISQRALPPLP 30.8
ITMRPLPALP 17.3
RSGRPLPPIP 32.7
KWDSLLPALP 17.4
YWDMPLPRLP 4.2
YYQRPLPPLP 9.1
YFSRALPGLP 8.8
SLWDPLPPIP 15.2
DPYDALPETP 28.6
Results 34
Results concerning Prediction!
Proline preferece in the binding motif- Available experimental measurements at positions P3, P0, P−3, and P−5: - Particularly important for the peptide binding: - conserved Pro residues at P3 and P0: strong binding affinity (PxxP- work here) - residues at P−3, and P−5: the binding specificity (the other work)
Other residues, especially hydrophobic (Phe, Leu, Met, Val, and Trp), also favored
Conclusions and Outlook 35
Conclusion and Outlook!
Binding free energy: - nice method predictiong binding preferences- easy to deal with data
Can be used in prediction of different sets of protein-ligand interaction prediction
High throughput results in the fields of medicine, pharmacy, and biology
36
References Tingjun Hou, Ken Chen, William A McLaughlin, Benzhuo Lu, and Wei Wang.
Computational Analysis and Prediction of the Binding Motif and Protein Interacting Partners of the Abl SH3 Domain
Wikipedia T.Geyer, Dynamic Cell Simulation Jackal: supported by National Science Foundation and National Institute of Health;
developed in Honig Lab Baker NA, Sept D, Joseph S, Holst MJ, McCammon JA. APBS: Electrostatics of
nanosystems: application to microtubules and the ribosome. Proc. Natl. Acad. Sci. USA 98, 10037-10041 2001.
Dolinsky TJ, Nielsen JE, McCammon JA, Baker NA. PDB2PQR: an automated pipeline for the setup, execution, and analysis of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Research, 32, W665-W667 (2004).
R: Regulatory Compliance and Validation Issues A Guidance Document for the Use of R in Regulated Clinical Trial Environments
Google Machine Search
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