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Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department of Physics Yale University 1

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Page 1: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

Bioinformatics: Practical Application of Simulation and Data

Mining

Markov Modeling II

Prof. Corey O’HernDepartment of Mechanical Engineering

Department of PhysicsYale University

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Page 2: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

“Describing protein folding kinetics by Molecular DynamicsSimulations. 1. Theory” W. C. Swope, J. W. Pitera, and

F. Suits, J. Phys. Chem. B 108 (2004) 6571.

Markov Modeling of Proteins

“Describing protein folding kinetics by Molecular DynamicsSimulations. 2. Example applications to Alanine Dipeptide and a -hairpin peptide” W. C. Swope, J. W. Pitera, et al.,

J. Phys. Chem. B 108 (2004) 6582.

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Page 3: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

I. Alanine Dipeptide

6 backbone atoms; 3 dihedral angles (, , )

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Page 4: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

T=500K

Macrostate Definition

1

1

1

1 2

3 4

5

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Page 5: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

Kinetics at T=500 K

•10,000 separate trajectories sampled 200 times at0.5ps intervals (100ps) using AMBER+Shake

K , 4, 4,5, 4,5, 3,3,K

Ω3( ) t( ) = K ,0,0,0,0,0,1,1,K

Ω4( ) t( ) = K ,1,1,0,1,0,0,0,K

Ω5( ) t( ) = K ,0,0,1,0,1,0,0,K

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Page 6: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

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Page 7: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

MS Lifetime DistributionsMS1 MS5

1/(1-Tii)

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Page 8: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

Transition Matrix Eigenvalues

−t

lnμ i

−t

lnμ i

F ~ 550ps

spurious

Markovian

Non-Markovian

F >> tkin= 100ps

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Page 9: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

II. -hairpin motif of protein G

G41EWTYDDATKTFTVTE56

1 2 3 4 5 6

Hydrogenbonding

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Page 10: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

•287 conformations run at NVE (310 K) for 0.5 ns using explicit water and Na+ counterions•Order parameters: Rg, number and order of hydrogen bonds

Macrostate Definition

000000111111

000001

turntermini

Hydrogen bonds

5.25A ≤Rg ≤9.5ARadius of gyration

S,M,L,E

26*422-35 macrostates00011X

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Page 11: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

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Page 12: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

MS Lifetime Distributions

000000E 00111X

Non-Markovian Markovian > 50ps

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Page 13: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

Transition Matrix Eigenvalues

TimereversedNon-

Markovian

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Page 14: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

Predicted Folding Time

F ~ 20 ns << 6 s

1. Short 0.5 ns trajectories (4 orders of magnitude difference)2. Long-lived conformations

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Page 15: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

Long-lived Conformations

Misregistered H-bonds

Misregistered H-bonds

splayed, ionassociation

misformedturn

tightturn15

Page 16: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

“Using massively parallel simulation and MarkovianModels to study protein folding: Examining the dynamics

Of the villin headpiece,” J. Chem. Phys. 124 (2006) 164902.

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Page 17: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

Villin headpiece-HP-36

MLSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF: PDB 1 VII

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Page 18: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

50,000 trajectories *10ns/trajectory = 500 s

•Gromacs with explicit solvent (5000 water molecules)and eight counterions; Amber + bond constraints

Simulation Details

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Page 19: Bioinformatics: Practical Application of Simulation and Data Mining Markov Modeling II Prof. Corey O’Hern Department of Mechanical Engineering Department

Native State Ensemble

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