作者: Mahmoud Moradi , Curtis Goolsby , Ashkan Fakharzadeh
DOI: 10.1101/2021.01.19.427358
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摘要: We have formulated a Riemannian framework for describing the geometry of collective vari-able spaces biomolecules within context molecular dynamics (MD) simulations. Theformalism provides theoretical to develop enhanced sampling techniques, path-findingalgorithms, and transition rate estimators consistent with treatment collec-tive variable space, where quantities interest such as potential mean force (PMF)and minimum free energy path (MFEP) remain invariant under coordinate transformation. Spe-cific algorithms this are discussed umbrella sampling,the string method, Riemannian-Bayesian estimator diffusionconstant, which can be used estimate along an MFEP