Tackling Sampling Challenges in Biomolecular Simulations
Molecular dynamics (MD) simulations are a powerful tool to give an atomistic insight into the structure and dynamics of proteins. However, the time scales accessible in standard simulations, which often do not match those in which interesting biological processes occur, limit their predictive capabilities. Many advanced sampling techniques have been proposed over the years to overcome this limitation. This chapter focuses on metadynamics, a method based on the introduction of a time-dependent bias potential to accelerate sampling and recover equilibrium properties of a few descriptors that are able to capture the complexity of a process at a coarse-grained level. The theory of metadynamics and its combination with other popular sampling techniques such as the replica exchange method is briefly presented. Practical applications of these techniques to the study of the Trp-Cage miniprotein folding are also illustrated. The examples contain a guide for performing these calculations with PLUMED, a plugin to perform enhanced sampling simulations in combination with many popular MD codes.
Key wordsEnhanced sampling Metadynamics PLUMED Replica exchange methods Molecular dynamics Collective variables Free energy
AB thanks the Swiss National Science Foundation for financial support under the Ambizione grant PZ00P2_136856. J. P. acknowledges the support of NSF award CMMI-1032368. The simulations of Trp-Cage miniprotein were made possible in part by the National Science Foundation through TeraGrid resources provided by NICS. These simulations were also facilitated through the use of computational, storage, and networking infrastructure provided by the Hyak supercomputer system, supported in part by the University of Washington eScience Institute.
- 2.Beberg AL, Ensign DL, Jayachandran G, Khaliq S, Pande VS (2009) Folding@home: lessons from eight years of volunteer distributed computing, IEEE International Symposium on, Parallel & Distributed Processing, 2009. IPDPS 2009, 23-29 May 2009, Rome, pp. 1624–1631Google Scholar
- 4.Dellago C, Bolhuis PG (2009) Transition path sampling and other advanced simulation techniques for rare events. Adv Polym Sci 221:167–233Google Scholar
- 19.Tribello GA, Bonomi M, Branduardi D, Camilloni C, Bussi G (2014) PLUMED 2: new feathers for an old bird, Comput Phys Commun 185:604–613Google Scholar