1. Experimental Overview
Coupled distributed computing and molecular dynamics (MD) to study biomolecular folding at ensemble level
Simulate in all-atom detail using an explicit TIP solvent representation and counterions
Compare explict model results to other collected using the GB/SA implict model

2. METHODS A Refresher Molecular Dynamics
Molecular modeling method used to predict structure and physical properties of a biomolecule by simulating the time-dependent changes of the system
Example: predicting the folding and unfolding of the titin molecule

3. METHODS Classical Molecular Dynamics
Advantages
Dynamic changes
Simplification of one single molecule representing an ensemble
PROBLEM!!!
Current computer technology causes limitations

4. METHODS Solution! Ensemble Molecular Dynamics Simulation
Coupling of distributed computing and molecular dynamics to simulate the atomistic details of a process
Ability to simulate the whole folding process

5. Experimental Overview
Coupled distributed computing and molecular dynamics (MD) to study biomolecular folding at ensemble level
Simulate in all-atom detail using an explicit TIP solvent representation and counterions
Compare explict model results to other collected using the GB/SA implict model

6. METHODS What is Distributed Computing?
Example: building a house
The solution is to create work units, where thousands of computers around the world will contribute to the simulation. All they have to do is just download a program.
Linear relationship - twice the amount of computers on the server, twice as fast to simulate protein folding

7. METHODS
When one of the Trial simulations crosses a free energy barrier (exhibits a state transition), all the other Trials in that particular Series are transferred to the configuration-space location of the one that just made the transition and then all the simulations are independently restarted again from there. This process is repeated as many times as needed to traverse the remaining statistical energy barriers.

8. Experimental Overview
Coupled distributed computing and molecular dynamics (MD) to study biomolecular folding at ensemble level
Simulate in all-atom detail using an explicit TIP solvent representation and counterions
Compare explict model results to other collected using the GB/SA implict model

9. METHODS Setup Two explicit solvent models TIP3P TIP4P
A rigid water monomer that is represented by 3 interaction sites.
Positive charges on the hydrogens and negative charge on the oxygen
TIP4P
A rigid water monomer that is represented by 4 interaction sites
The negative charge is moved off the oxygen and towards the hydrogens at a point on the bisector of the HOH angle.

10. METHODS Simulation Setup
Pressure and temperature was held constant at 1 atm and 300 K via independently coupling both the solute and ionic solvent to an external heat bath with a relaxation time of 0.1ps
Same nucleic acid potential set that was used for the simulations of the RNA hairpin in the GB/SA implicit solvent model
Native and unfolded starting structures were each centered in 50Å cubic boxes
150mM sodium ions were added to the boxes to neutralize the structures
Each system at the starting point was solvated in ~3920 TIP3P water molecules, energy-minimized, and annealed for 1ns of MD with the solute held fixed

11. METHODS P-fold Calculations
P-fold is the folding probability based on the fraction of simulations which fold before unfolding in a given time
P-fold value of 0 represents the unfolded state
P-fold value of 1 represents the native state
Transition state conformations have a P-fold value of between 0.4 and 0.6
P-fold calculations were conducted on 40 conformations taken from previous simulations using implicit solvent model for comparison

12. Experimental Overview
Coupled distributed computing and molecular dynamics (MD) to study biomolecular folding at ensemble level
Simulate in all-atom detail using an explicit TIP solvent representation and counterions
Compare explict model results to other collected using the GB/SA implict model

13. Ensemble Simulations Two simulated ensembles were generated
Native ensemble
Aggregate simulation time: 110.6μs
Folding ensemble
Aggregate simulation time: 168.1μs
RMSD in Explicit Solvent
RMSD in Implicit Solvent
Native Ensemble
1.81 (+ 0.73) Å
1.89 (+0.62)) Å
Folding Ensemble
7.79(+1.97) Å
12.35 (+1.82) Å

14. Probing the conformational Free Energy Landscape
P-fold simulations were conducted to study effects ions have on folding
Ions used: Na+, Mg+2, and an implicit ion model
Used on both solvents, TIP3P and TIP4P to test dependence on the solvent model
P-fold simulations found a cumulative sampling time of ~200μs

15. Native Character Mean NC Values Native ensemble 0.742 (+0.052)
Defining native contacts
NC = f-nat – f-non
Fraction of atomic contacts that are non-native
Fraction of atomic contacts present in the conformation that are native
Mean NC Values
Native ensemble
0.742 (+0.052)
Folding ensemble
(+0.097)