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Enhancing Collaborative Molecular Dynamics Simulations in Ion Channels

Dive into collaborative work in molecular dynamics to improve simulations in ion channels. Explore the challenges, progress, and future steps in this groundbreaking research. Follow the innovative methods and results discussed by experts in the field.

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Enhancing Collaborative Molecular Dynamics Simulations in Ion Channels

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  1. Supporting Collaborative Work in Molecular Dynamics (See-Wing Chiu) Following slide shows molecular dynamics simulation of porin by See-Wing Chiu, NCSA/UIUC/Beckman Institute Computational Biology/Nanoscience group Simulations are done using Gromacs md software on NCSA IA32 and IA64 Linux superclusters

  2. Results of initial molecular dynamics simulations, reported as part of paper at San Juan meeting. Left hand figure above is msd correlation function for ions at different positions in the channel. Right hand figure is mobilities as derived from curves like those at left.

  3. Problems with early simulations: • We could not run the simulations long enough prior to San Juan submission deadline to get good enough statistics for accurate determination of mobilities. • We have discovered artefacts in form of fluctuations computed by md when cutoffs are used for electrostatics in situations with high ionic strength. • Current activity: • We are doing longer runs on this system and using Particle Mesh implementation of Ewald sums to compute long-range electrostatic forces.

  4. Related Work on Brownian Dynamics—The random walk method for solving the PNP equation (Yuzhou Tang, UIUC/NCSA/Beckman Institute Computational Biology/Nanoscience Group) Current status: We have done a successful coarse-grained BD/electrostatics calculation that reproduces the electrophysiology of the KcsA potassium channel. (Mashl et al, 2001) Next steps: Enhance methodology to the point where we can attempt to compute selectivity. Electrostatics refinement is discussed in electrostatics section. Brownian dynamics enhancement is in the form of a new algorithm, shown in next couple of slides.

  5. Defining Relationships of Brownian motion (D: Diffusion constant; G: Potential Energy; m: Mass; T: Temperature; F’(t): Random Force)  (1) Short-Step Algorithm: (valid when t<<1/f) • Invariant Algorithm:

  6. (2) Long-Step Algorithm: (valid when t>>1/f) (2) Short-Step Algorithm: (t<<1/) (3) Invariant Algorithm: (valid for all time steps)

  7. Next Step: Combine new Brownian dynamics algorithm with enhanced electrostatics to attempt very accurate BD simulation of ion motion through narrow channels.

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