Experimental Overview

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 ofone 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. METHODSFolding@Home • 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 • 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. METHODSSimulation 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. METHODSP-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

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 • 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