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Area of Science:

  • Computational Biology
  • Biophysics
  • Molecular Dynamics

Background:

  • Simulating atomistic protein folding at room temperature is limited to microseconds with conventional molecular dynamics (MD).
  • Advancements in GPU computing offer potential for longer timescale simulations.

Purpose of the Study:

  • To report the folding of atomistic protein systems using the weighted ensemble (WE) strategy combined with GPU computing.
  • To enable statistically unbiased estimation of rate constants for rare events like protein folding.

Main Methods:

  • Employed the weighted ensemble (WE) strategy with GPU-accelerated MD simulations.
  • Organized ensembles of trajectory segments using pruning and replication for unbiased estimates.
  • Analyzed folding times using WE probability flux and history-augmented Markov analysis.

Main Results:

  • Successfully simulated folding of atomistic, implicitly solvated proteins with folding times from microseconds to milliseconds.
  • Examined NTL9 (0.8-9 μs and 0.2-2 ms) and Protein G (3-200 ms) at varying viscosities.
  • WE simulations provided folding time, uncertainty, and ensemble properties, requiring less computation than conventional MD for Protein G.

Conclusions:

  • The WE strategy with GPU computing is feasible for simulating protein folding on longer timescales.
  • This method allows for statistically unbiased estimation of kinetic quantities, including folding times and uncertainties.
  • The findings suggest feasibility in using and calibrating force fields and solvent models for precise kinetic estimations.