AI Article Synopsis
- Monte Carlo methods are crucial for studying protein folding but face inefficiencies at low temperatures due to complex energy landscapes.
- A new method called density guided importance sampling (DGIS) improves sampling efficiency by accurately guiding simulations toward under-sampled areas without needing parameter optimization.
- The DGIS method outperforms traditional Monte Carlo techniques in accuracy and efficiency while ensuring equilibrium is reached more effectively.
Article Abstract
Motivation: Monte Carlo methods are the most effective means of exploring the energy landscapes of protein folding. The rugged topography of folding energy landscapes causes sampling inefficiencies however, particularly at low, physiological temperatures.
Results: A hybrid Monte Carlo method, termed density guided importance sampling (DGIS), is presented that overcomes these sampling inefficiencies. The method is shown to be highly accurate and efficient in determining Boltzmann weighted structural metrics of a discrete off-lattice protein model. In comparison to the Metropolis Monte Carlo method, and the hybrid Monte Carlo methods, jump-walking, smart-walking and replica-exchange, the DGIS method is shown to be more efficient, requiring no parameter optimization. The method guides the simulation towards under-sampled regions of the energy spectrum and recognizes when equilibrium has been reached, avoiding arbitrary and excessively long simulation times.
Availability: Fortran code available from authors upon request.
Contact: m.j.parker@leeds.ac.uk.
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| http://dx.doi.org/10.1093/bioinformatics/bti421 | DOI Listing |
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