Free Energy Landscape of Protein−Protein Encounter Resulting from Brownian Dynamics Simulations of Barnase:Barstar

2005 ◽  
Vol 1 (4) ◽  
pp. 723-736 ◽  
Author(s):  
Alexander Spaar ◽  
Volkhard Helms
Keyword(s):  
Free Energy ◽  
Brownian Dynamics ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Dynamics Simulations ◽  
Brownian Dynamics Simulations

scholarly journals Free-energy landscape of a hyperstable RNA tetraloop

2016 ◽  
Vol 113 (24) ◽  
pp. 6665-6670 ◽  
Author(s):  
Jacob C. Miner ◽  
Alan A. Chen ◽  
Angel E. García
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
High Performance ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Left Handed ◽  
Rna Conformation ◽  
Dynamics Simulations ◽  
Secondary Structure Motif

We report the characterization of the energy landscape and the folding/unfolding thermodynamics of a hyperstable RNA tetraloop obtained through high-performance molecular dynamics simulations at microsecond timescales. Sampling of the configurational landscape is conducted using temperature replica exchange molecular dynamics over three isochores at high, ambient, and negative pressures to determine the thermodynamic stability and the free-energy landscape of the tetraloop. The simulations reveal reversible folding/unfolding transitions of the tetraloop into the canonical A-RNA conformation and the presence of two alternative configurations, including a left-handed Z-RNA conformation and a compact purine Triplet. Increasing hydrostatic pressure shows a stabilizing effect on the A-RNA conformation and a destabilization of the left-handed Z-RNA. Our results provide a comprehensive description of the folded free-energy landscape of a hyperstable RNA tetraloop and highlight the significant advances of all-atom molecular dynamics in describing the unbiased folding of a simple RNA secondary structure motif.

scholarly journals The histone H3 N-terminal tail: a computational analysis of the free energy landscape and kinetics

2015 ◽  
Vol 17 (20) ◽  
pp. 13689-13698 ◽  
Author(s):  
Yuqing Zheng ◽  
Qiang Cui
Keyword(s):  
Free Energy ◽  
Energy Landscape ◽  
Critical Role ◽  
Histone H3 ◽  
Free Energy Landscape ◽  
Chromatin Dynamics ◽  
Markov State Models ◽  
State Models ◽  
Dynamics Simulations ◽  
Kinetics Of

Extensive molecular dynamics simulations and Markov State models are used to characterize the free energy landscape and kinetics of the histone H3 N-terminal tail, which plays a critical role in regulating chromatin dynamics and gene activity.

scholarly journals Computational methods for exploring protein conformations

2020 ◽  
Vol 48 (4) ◽  
pp. 1707-1724
Author(s):  
Jane R. Allison
Keyword(s):  
Free Energy ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Conformational Sampling ◽  
Conformational Ensemble ◽  
Collective Variables ◽  
Conformational States ◽  
Simulation Techniques ◽  
Wide Range ◽  
Dynamics Simulations

Proteins are dynamic molecules that can transition between a potentially wide range of structures comprising their conformational ensemble. The nature of these conformations and their relative probabilities are described by a high-dimensional free energy landscape. While computer simulation techniques such as molecular dynamics simulations allow characterisation of the metastable conformational states and the transitions between them, and thus free energy landscapes, to be characterised, the barriers between states can be high, precluding efficient sampling without substantial computational resources. Over the past decades, a dizzying array of methods have emerged for enhancing conformational sampling, and for projecting the free energy landscape onto a reduced set of dimensions that allow conformational states to be distinguished, known as collective variables (CVs), along which sampling may be directed. Here, a brief description of what biomolecular simulation entails is followed by a more detailed exposition of the nature of CVs and methods for determining these, and, lastly, an overview of the myriad different approaches for enhancing conformational sampling, most of which rely upon CVs, including new advances in both CV determination and conformational sampling due to machine learning.

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