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Protein simulations
Greg Bowman
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Molecular Dynamics
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Molecular Dynamics
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Sampling Challenge: Simulating Long Timescales is Extremely Hard
100
seconds 10-6
micro 10-15
femto 10-12
pico 10-9
nano 10-3
milli
Bond vibration
Isomer- ization
Water dynamics
Helix forms
Fast folding
Slow conf change
103
seconds
protein oligomerization
time on 1 fast CPU
3,000,000 years
~age of the
earth
3,000 years
3 years
1 day
long MD run
MD step
where we’d love to be
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Bowman et al. Springer 2014.Knoverek et al. Trends Biochem Sci 2018.
Markov state models (MSMs) are quantitative maps of a protein’s energy
landscape
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MSMs are Discrete-time Master Equation Models
!!(!)!! = !"!
! ! = !!"!(0)!
! ! = !!! !
!!!(!)!! = !!(!)×!!" − !!(!)×!!"
!!
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Alanine Dipeptide: The State of the Art as of 2007
Chodera et al. JCP 2007.
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The Villin Headpiece: The Unconquerable Frontier
Ensign et al. JMB 2007.Kubelka et al. JMB 2006.
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A Simple Tutorial on Building MSMs
Dobson et al. Nature 2003.
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Sampling with Stochastic Simulations
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Clustering Gives a High-resolution Model
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Clustering Gives a High-resolution Model
(This is a cartoon, since it’s hard to draw 10,000 states in a talk)
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Clustering Gives a High-resolution Model
(This is a cartoon, since it’s hard to draw 10,000 states in a talk)
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Lumping Provides Human Intuition
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The Villin Headpiece: The Unconquerable Frontier
Bowman et al. JCP 2009.
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Fre
e E
nerg
y (k
T)
native state prediction
MSM Retrodicts Native Structure and Folding Rate
Bowman et al. JCP 2009.
p(t+Δ&t)&=&T(Δ&t)p(t)&
Obs(t)&=&p(t)!Obs&
∆Gfold = -0.5 ± 0.5 kcal/mol
τfold = 1 (0.5, 5) μs
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Testing for Two-state Behavior with Mean First Passage Times (MFPTs)Ui →N Ui →Uk
Bowman and Pande. PNAS 2010.
0.88 ± 0.27 μs 370 ± 220 μs
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The Native State is a Kinetic Hub
Bowman and Pande. PNAS 2010.
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The Unfolded Ensemble Brings Us Back to Levinthal’s Paradox
Bowman and Pande. PNAS 2010.
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β-lactamase confers bacteria with antibiotic resistance
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MSMs capture cryptic pockets
Pocketopening
Bowman and Geissler. PNAS 2012Bowman et al. PNAS 2015
Bowman. J Comput Chem 2016.Bowman et al. J Phys Chem B 2014.
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Fluctuation amplification of specific traits (FAST)
Geometric observable of interest
Statistical uncertainty
Weighting factor
Start
Target
< < <Zimmerman and Bowman. J Chem Theory Comput 2015.
Zimmerman et al. J Chem Theory Comput 2018.
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Pathways discovered by FAST-SASA
Zimmerman and Bowman. J Chem Theory Comput 2015.
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Pathways discovered by FAST-SASA
Zimmerman and Bowman. J Chem Theory Comput 2015.
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Pathways discovered by FAST-SASA
Zimmerman and Bowman. J Chem Theory Comput 2015.
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Pathways discovered by FAST-SASA
Zimmerman and Bowman. J Chem Theory Comput 2015.
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Pathways discovered by FAST-SASA
Zimmerman and Bowman. J Chem Theory Comput 2015.
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Pathways discovered by FAST-SASA
Zimmerman and Bowman. J Chem Theory Comput 2015.
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FAST outperforms conventional simulations and other adaptive schemes
Zimmerman and Bowman. J Chem Theory Comput 2015. Zimmerman… and Bowman. arXiv 2018.
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FAST outperforms conventional simulations and other adaptive schemes
Zimmerman and Bowman. J Chem Theory Comput 2015. Zimmerman… and Bowman. arXiv 2018.
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FAST outperforms conventional simulations and other adaptive schemes
Zimmerman and Bowman. J Chem Theory Comput 2015. Zimmerman… and Bowman. arXiv 2018.
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FAST outperforms conventional simulations and other adaptive schemes
Zimmerman and Bowman. J Chem Theory Comput 2015. Zimmerman… and Bowman. J Chem Theory Comput 2018.