biophysics and thermodynamics a pproaches for modeling and testing nf- b binding s ites
DESCRIPTION
Marcin Pacholczyk , Silesian University of Technology. Biophysics and Thermodynamics A pproaches for Modeling and Testing NF- B Binding S ites . Physics-based Laws of Physics – electrostatics, van der Waals, molecular flexibility, geometry of hydrogen bonds - PowerPoint PPT PresentationTRANSCRIPT
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Biophysics and Thermodynamics Approaches for Modeling and Testing NF-B Binding Sites
Marcin Pacholczyk, Silesian University of Technology
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Physics vs Knowledge – based potentials
Physics-based Laws of Physics – electrostatics, van der
Waals, molecular flexibility, geometry of hydrogen bonds
Computationally intensive, some effects difficult to model (desolvation)
Knowledge-based Relatively simple, based on observation Training set!
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Physics-Based Potential
e=80
Na+
Cl-
e=2- 4
h
e=80
e=2- 4
C VdWG E E=
Poisson-Boltzmann equation + Lenard-Jones potential
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Knowledge-Based Statistical Potential
ln ln , ,P DN N
ij i ji j
G P C D P C d t t- =-
Robertson and Varani 2007
Gibbs energy probability of „correctness”
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Knowledge-Based Statistical Potential
, ,, ,
, ,ij i j
ij i jij i j
P d t t CP C d t t P C
P d t t=
Probability of individual atomic contact
P(C) – Bayesian prior of observing native-like protein-DNA complex – set to 1.
Robertson and Varani 2007
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Knowledge-Based Statistical Potential
, ,
, , , ,, ,
ij
obs ij i jij i j ij i j
obs ij i jd
N d t tP d t t C f d t t
N d t t =
Probability function
Continous dij is mapped to a set of discrete distance bins b0, b1, … , bn with distance cutoffs db0, db1, … , dbn
A count is assigned to bi if dbi-1 dij < dbi
3 Å, 4 Å, 5 Å, 6 Å, 7 Å, 8 Å, 9 Å, 10 Å
Robertson and Varani 2007
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Knowledge-Based Statistical Potential
, ,
, , i j
obs ij i jt t
ij i j ijC
N d t tP d t t f d
N =
Marginal distribution
NC – total number of observed contacts between interface atoms of all types, at all distances in the training set
Robertson and Varani 2007
Training set – Nucleic Acid Database ndbserver.rutgers.edu
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Computation of the PWMs
Almanova et al. 2010
Three members of the NF-B family of TF p50p50 homodimer (1NFK) p50RelB heterodimer (2V2T) p50p65 heterodimer (1VKX)
Complexes with DNA fragmentsDNA chains were mutated (MMTSB – Multiscale Modeling Tools for Structural Biology) one base pair at each step (backbone fixed)
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Computation of the PWMs
Almanova et al. 2010
Three members of the NF-B family of TF p50p50 homodimer (1NFK) p50RelB heterodimer (2V2T) p50p65 heterodimer (1VKX)
Complexes with DNA fragments (PDB)
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NF-B family
p50p50 p50p65
p50RelB
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Computation of the PWMs
AX b=
DNA chains were mutated (MMTSB – Multiscale Modeling Tools for Structural Biology) one base pair at each step (backbone fixed) 4N + R
All weights w(i, u) in the PWM predicted by solving the linear equation:
X is a vector of 4N dimensions of the estimated weights
A is a binary matrix of dimensions (4N, 4N + R), with all random DNA sequences whose free binding energy was computed.
The free binding energy vector b consists of 4N + R values obtained with the protein-DNA scoring procedure
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Almanova et al. 2010
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p50p50
p50RelB
p50p65
TRANSFACV$NFKAPPAB_01
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Comparison
Almanova DDNA2 TRANSFAC
p50p50 2.65 -
2.29p50RelB 2.84 -
p50p65 2.38 3.28
Relative entropy
Almanova et al. 2010
, ,,
logi j i ji j
H p p=-
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Comparison
69 human genes regulated by NF-B with 124 promoter sequences (TRANSPRO)
Experimentally confirmed 31 out of 124 promoters belonging to 25 genes
Matrix scan with Match on 58 confirmed binding sites
Almanova TRANSFAC
p50p50 30 (5) 25 V$P50P50_Q3
p50p65 25 (5) 26 (6) V$P50RELAP65_Q5_01
Binding site discovery
Almanova et al. 2010
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Almanova et al. 2010
Comparison
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Comparison
Almanova DDNA2 TRANSFAC
p50p50 0.825 0.833 0.855
p50p65 0.798 0.792 0.863
AUC
Almanova et al. 2010
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Implementation
http://cogangs.biobase.de/3dtf/
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Conclusions
Discovery of novel NF-B binding sites Investigation of postranslational modifications like RelA Ser276 phosphorylation (Nowak et al. 2008)
It is possible to compute PWMs which perform comparably to the ones derived from experimental data (TRANSFAC)
Thermodynamic based models of transcriptional regulation including Synergistic Activation, Cooperative Binding and Short-Range Repression (He et al. 2010)