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Annealed importance sampling of peptides
Edward Lyman1, Daniel M Zuckerman
1Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA.
The Journal of Chemical Physics
|August 21, 2007
Summary
Annealed importance sampling (AIS) efficiently calculates equilibrium averages from nonequilibrium simulations using simulated annealing. This method provides an "adiabatic signature" and is effective even with limited overlap between high and low-temperature distributions.
Area of Science:
- Computational chemistry
- Statistical mechanics
- Biophysics
Background:
- Simulated annealing is often used to explore conformational landscapes.
- Calculating equilibrium properties from nonequilibrium simulations presents a challenge.
- Annealed importance sampling (AIS) offers a potential solution by reweighting nonequilibrium samples.
Purpose of the Study:
- To demonstrate the effectiveness of annealed importance sampling (AIS) for calculating equilibrium averages.
- To evaluate AIS performance on peptide systems (dileucine and alanine 5-mer).
- To assess AIS utility when high and low-temperature distributions have minimal overlap.
Main Methods:
- Application of annealed importance sampling (AIS) to a 50-atom dileucine peptide.
- Implementation of AIS on an alanine 5-mer.
- Comparison of AIS efficiency against constant temperature simulations.
Main Results:
- Equilibrium distributions were successfully attained with manageable cooling schedules for both peptide systems.
- AIS showed modest efficiency gains over constant temperature simulations for dileucine.
- AIS demonstrated success for the alanine 5-mer, even with limited overlap between unfolded and folded states.
Conclusions:
- Annealed importance sampling (AIS) is a viable and computationally inexpensive method for obtaining equilibrium averages from simulated annealing protocols.
- AIS is particularly useful when exploring systems with significant conformational changes or limited distribution overlap.
- The presented AIS implementation can be further optimized for enhanced efficiency.
