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Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

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Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II. Prof. Corey O ’ Hern Department of Mechanical Engineering & Materials Science Department of Physics Yale University. 1. What did we learn about proteins?. - PowerPoint PPT Presentation
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Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II Prof. Corey O’Hern Department of Mechanical Engineering & Materials Science Department of Physics Yale University 1
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Page 1: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Bioinformatics: Practical Application of Simulation and Data

Mining

Protein Folding II

Prof. Corey O’HernDepartment of Mechanical Engineering & Materials

ScienceDepartment of Physics

Yale University

1

Page 2: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

What did we learn about proteins?•Many degrees of freedom; exponentially growing # of energy minima/structures•Folding is process of exploring energy landscape to find global energy minimum•Need to identify pathways in energy landscape; # of pathways grows exponentially with # of structures•Coarse-graining/clumping required

energy minimum

transition

•Transitions are temperature dependent 2

Page 3: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

J. D. Honeycutt and D. Thirumalai, “The nature of foldedstates of globular proteins,” Biopolymers 32 (1992) 695.

T. Veitshans, D. Klimov, and D. Thirumalai, “Protein folding kinetics: timescales, pathways and energy landscapes

in terms of sequence-dependent properties,” Folding & Design 2 (1996)1.

Coarse-grained (continuum, implicit solvent, C) models for proteins

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Page 4: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

3-letter C model: B9N3(LB)4N3B9N3(LB)5L

B=hydrophobic

N=neutral

L=hydrophilic

Nsequences= 3 ~ 1022

Np ~ exp(aNm)~1019 Number of structuresper sequence

Number of sequences forNm=46

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Page 5: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

different mapping?

and dynamics

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Page 6: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Molecular Dynamics: Equations of Motion

for i=1,…Natoms

Coupled 2nd order Diff. Eq.

How are they coupled?

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Page 7: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

(iv) Bond length potential

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Page 8: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Pair Forces: Lennard-Jones Interactions

ij

Parallelogramrule

-dV/drij > 0; repulsive-dV/drij < 0; attractive

force on i due to j

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Page 9: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

‘Long-range interactions’

BB

V(r)

r/

NB, NL, NN

LL, LB

r*=21/6

hard-core

attractions-dV/dr < 0

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Page 10: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Bond Angle Potential

0=105

i jkijk

ijk=[0,]

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Page 11: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Dihedral Angle Potential

Vd(ijkl)

Vd(ijkl)

ijkl

Successive N’s

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Page 12: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Bond Stretch Potential

i j

for i, j=i+1, i-1

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Page 13: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Equations of Motion

velocityverletalgorithm

Constant Energy vs. Constant Temperature (velocity rescaling, Langevin/Nosé-Hoover thermostats)

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Page 14: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Collapsed Structure

T0=5h; fast quench; (Rg/)2= 5.48

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Page 15: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Native State

T0=h; slow quench; (Rg/)2= 7.78

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Page 16: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

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Page 17: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

start end

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Page 18: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

native states

Total Potential Energy

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Page 19: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

slow quench

unfolded

native state

Radius of Gyration

Tf

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Page 20: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Construct the backbone in 2D

Assign sequence of hydrophobic (B) and neutral (N) residues, B residues experience an effective attraction. No bond bending potential.

Evolve system under Langevin dynamics at temperature T

Collapse/folding induced by decreasing temperatureat rate r.

BN

2-letter C model: (BN3)3B

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Page 21: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II
Page 22: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Energy Landscape

end-to-end distance end-to-end distance

5 contacts4 contacts 3 contacts

E/CE/C

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Page 23: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Rate Dependence

5 contacts

4 contacts

3 contacts2 contacts

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Page 24: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Misfolding

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Page 25: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Reliable Folding at Low Rate

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Page 26: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Slow rate

Page 27: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Fast rate

Page 28: Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding II

Next…

•Thermostats…Yuck!•More results on coarse-grained models•Results for atomistic models•Homework

So far…

•Uh-oh, proteins do not fold reliably…•Quench rates and potentials

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