Peptoid conformational free energy landscapes from implicit-solvent molecular simulations in AMBER

Biopolymers ◽  
2010 ◽  
Vol 96 (5) ◽  
pp. 639-650 ◽  
Author(s):  
Vincent A. Voelz ◽  
Ken A. Dill ◽  
Ilya Chorny
Keyword(s):  
Free Energy ◽  
Molecular Simulations ◽  
Implicit Solvent ◽  
Energy Landscapes ◽  
Free Energy Landscapes ◽  
Conformational Free Energy

scholarly journals Conformational Free-Energy Landscapes for a Peptide in Saline Environments

2012 ◽  
Vol 103 (12) ◽  
pp. 2513-2520 ◽  
Author(s):  
Timothy J. Gaborek ◽  
Christophe Chipot ◽  
Jeffry D. Madura
Keyword(s):  
Free Energy ◽  
Energy Landscapes ◽  
Saline Environments ◽  
Free Energy Landscapes ◽  
Conformational Free Energy

scholarly journals The complete conformational free energy landscape of β-xylose reveals a two-fold catalytic itinerary for β-xylanases

2015 ◽  
Vol 6 (2) ◽  
pp. 1167-1177 ◽  
Author(s):  
Javier Iglesias-Fernández ◽  
Lluís Raich ◽  
Albert Ardèvol ◽  
Carme Rovira
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Energy Landscapes ◽  
Free Energy Landscapes ◽  
Dynamics Simulations ◽  
Conformational Free Energy

Ab initio conformational free energy landscapes, together with molecular dynamics simulations, enable to predict the catalytic itineraries of β-xylanase enzymes.

scholarly journals Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin

2016 ◽  
Vol 113 (5) ◽  
pp. 1150-1155 ◽  
Author(s):  
Patrick Shaffer ◽  
Omar Valsson ◽  
Michele Parrinello
Keyword(s):  
Free Energy ◽  
Sampling Methods ◽  
Molecular Simulations ◽  
High Dimensionality ◽  
High Dimensional ◽  
Energy Landscapes ◽  
Enhanced Sampling ◽  
Collective Variables ◽  
Targeted Sampling ◽  
Free Energy Landscapes

The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute free-energy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin.

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