Low-mass molecular dynamics simulation: A simple and generic technique to enhance configurational sampling
•Reducing atomic masses by 10-fold vastly improves sampling in MD simulations.•CLN025 folded in 4 of 10×0.5-μs MD simulations when masses were reduced by 10-fold.•CLN025 folded as early as 96.2ns in 1 of the 4 simulations that captured folding.•CLN025 did not fold in 10×0.5-μs MD simulations when st...
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Veröffentlicht in: | Biochemical and biophysical research communications 2014-09, Vol.452 (3), p.588-592 |
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Sprache: | eng |
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Zusammenfassung: | •Reducing atomic masses by 10-fold vastly improves sampling in MD simulations.•CLN025 folded in 4 of 10×0.5-μs MD simulations when masses were reduced by 10-fold.•CLN025 folded as early as 96.2ns in 1 of the 4 simulations that captured folding.•CLN025 did not fold in 10×0.5-μs MD simulations when standard masses were used.•Low-mass MD simulation is a simple and generic sampling enhancement technique.
CLN025 is one of the smallest fast-folding proteins. Until now it has not been reported that CLN025 can autonomously fold to its native conformation in a classical, all-atom, and isothermal–isobaric molecular dynamics (MD) simulation. This article reports the autonomous and repeated folding of CLN025 from a fully extended backbone conformation to its native conformation in explicit solvent in multiple 500-ns MD simulations at 277K and 1atm with the first folding event occurring as early as 66.1ns. These simulations were accomplished by using AMBER forcefield derivatives with atomic masses reduced by 10-fold on Apple Mac Pros. By contrast, no folding event was observed when the simulations were repeated using the original AMBER forcefields of FF12SB and FF14SB. The results demonstrate that low-mass MD simulation is a simple and generic technique to enhance configurational sampling. This technique may propel autonomous folding of a wide range of miniature proteins in classical, all-atom, and isothermal–isobaric MD simulations performed on commodity computers—an important step forward in quantitative biology. |
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ISSN: | 0006-291X 1090-2104 |
DOI: | 10.1016/j.bbrc.2014.08.119 |