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Modeling W ater to C alculate its C hemical P otential

Modeling W ater to C alculate its C hemical P otential. Daniel Dorman Department of Chemistry and Department of Electrical and Computer Engineering The University of Maine in Orono Advisers: Dr. Jay Rasaiah and Dr. Guogang Feng. Introduction.

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Modeling W ater to C alculate its C hemical P otential

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  1. Modeling Water to Calculate its Chemical Potential Daniel Dorman Department of Chemistry and Department of Electrical and Computer Engineering The University of Maine in Orono Advisers: Dr. Jay Rasaiah and Dr. Guogang Feng

  2. Introduction • Water molecules have been found to exist in hydrophobic protein cavities. • These water molecules play an important role in the function of these proteins.

  3. Ultimate Research Goals • Model behavior of water molecules in hydrophobic cavities • Understand the properties of water in hydrophobic cavities • However, before the hydrophobic cavity case is considered…

  4. My Research Goals • The bulk case must be considered. • My research objectives: • Create a model of water in the bulk phase • Run a sequence of simulations on the data using Amber • Analyze the data to calculate the excess chemical potential of water in the bulk phase

  5. How will my research contribute? • By validating the accuracy of the TIP3P water molecule model • By providing a comparison for when the chemical potential of water in the hydrophobic cavity is calculated

  6. Background and Related Research • Water molecules were observed in the hydrophobic cavities of tetrabrachion by X-ray crystallography and nuclear magnetic resonance. • Dr. Rasaiah led research using molecular dynamics simulations to study the behavior of water molecules filling one of the cavities of tetrabrachion. • Research has been conducted on the density of the TIP3P water molecule using MD simulation, and the results were found to be close to that of real water

  7. Amber • A software suite that can run MD simulations on biomolecules • Developed by the University of California • Stands for: Assisted Model Building with Energy Refinement • Sander-The basic energy minimizer and MD program

  8. Amber Simulations • Require four input files: • Cartesian coordinates for all the atoms • Parameter file containing connectivity, atom names, atom types, residue names, and charges of the molecules • File containing user specific commands for running the simulation • Bash script for submitting simulation to cluster

  9. How Sander Works • Minimizes the model by relaxing the structure and moving the atoms down the energy gradient until a sufficiently low average gradient is obtained • Simulated by solving the Newtonian equations of motion for all the atoms in the system

  10. VMD • Visual Molecular Dynamics • Can create interactive visual representations of atoms • Can read Amber files to visualize a model

  11. My Research Procedures • Creating the model • Running a series of simulations on it • NVT simulation • NPT simulation • Particle insertion and removal simulation

  12. The Model • A box of 4,096 water molecules • Received the files from my adviser • Coordinate file giving starting coordinates and velocities for every atom in the model • File that gives the parameters for building the TIP3P water molecule model

  13. NVT Simulation • Constant number of particles • Constant volume • Constant temperature • Variable pressure • Final coordinate file becomes input coordinate file for next simulation

  14. NPT Simulation • Constant number of particles • Constant pressure • Constant temperature • Variable volume • Output coordinate file used as input for next simulation

  15. Particle Insertion/Removal Simulation • Inserts and removes a particle many times at each coordinate • For each insertion or removal, it calculates the change in energy • Huge output file with all the insertions and removals and their corresponding energy change • Data used to calculate chemical potential

  16. Calculations and Results • Opened output data in excel • Split into two separate files-insertions and removals • Created normalized histogram with bin=1 for each file • Identified the overlapping region of the two histograms • For each overlapping point, I calculated: Log(Pin/Prm)

  17. More Calculations • Plotted graph of the overlapping points and their corresponding log value • Fit a linear regression line to the plot • y=0.14712x + 3.6756 • Excess chemical potential=-1.0(intercept/slope) • Excess chemical potential = -24.984 kJ/mol

  18. My Graph

  19. My Research Conclusion • My value for the excess chemical potential of TIP3P water in the bulk phase is close to that of real water • My value is close to that of other TIP3P calculations • I have found and verified the excess chemical potential of TIP3P water in the bulk phase.

  20. Some Other Research • Optimizing speed of running Amber jobs • Myrinet is faster than ethernet • Optimal Myrinet is 16 processors • Optimal Ethernet is 8 processors

  21. Timing Graphs

  22. My REU Experience Conclusion • Good learning experience • Enjoyed the opportunity to do research • Hope to participate in more research in college, grad school, and in my career

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