scholarly journals Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction

2006 ◽  
Vol 103 (26) ◽  
pp. 9885-9890 ◽  
Author(s):  
P. Das ◽  
M. Moll ◽  
H. Stamati ◽  
L. E. Kavraki ◽  
C. Clementi
Keyword(s):  
Free Energy ◽  
Protein Folding ◽  
Dimensionality Reduction ◽  
Nonlinear Dimensionality Reduction ◽  
Energy Landscapes ◽  
Free Energy Landscapes ◽  
Low Dimensional

Navigating ligand–protein binding free energy landscapes: universality and diversity of protein folding and molecular recognition mechanisms

2001 ◽  
Vol 336 (5-6) ◽  
pp. 495-503 ◽  
Author(s):  
Gennady M. Verkhivker ◽  
Paul A. Rejto ◽  
Djamal Bouzida ◽  
Sandra Arthurs ◽  
Anthony B. Colson ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Folding ◽  
Molecular Recognition ◽  
Protein Binding ◽  
Binding Free Energy ◽  
Energy Landscapes ◽  
Free Energy Landscapes

scholarly journals Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

2017 ◽  
Vol 114 (28) ◽  
pp. E5494-E5503 ◽  
Author(s):  
Eliodoro Chiavazzo ◽  
Roberto Covino ◽  
Ronald R. Coifman ◽  
C. William Gear ◽  
Anastasia S. Georgiou ◽  
...  
Keyword(s):  
Free Energy ◽  
Added Value ◽  
Coarse Grained ◽  
Atomistic Simulations ◽  
Computer Assisted ◽  
Energy Landscapes ◽  
Learning Techniques ◽  
Free Energy Landscapes ◽  
Low Dimensional ◽  
Energy Surfaces

We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES.

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