Local energy landscape flattening: Parallel hyperbolic Monte Carlo sampling of protein folding

2002 ◽  
Vol 48 (2) ◽  
pp. 192-201 ◽  
Author(s):  
Yang Zhang ◽  
Daisuke Kihara ◽  
Jeffrey Skolnick
Keyword(s):  
Monte Carlo ◽  
Protein Folding ◽  
Energy Landscape ◽  
Monte Carlo Sampling ◽  
Local Energy

scholarly journals Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias

2018 ◽  
Author(s):  
Yong Wang ◽  
Pengfei Tian ◽  
Wouter Boomsma ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Monte Carlo ◽  
Protein Folding ◽  
Conformational Changes ◽  
Conformational Dynamics ◽  
Coarse Graining ◽  
Monte Carlo Sampling ◽  
Implicit Solvent ◽  
Great Success ◽  
Native State ◽  
Dynamics Simulations

AbstractEnergy landscape theory suggests that native interactions are a major determinant of the folding mechanism of a protein. Thus, structure-based (Gō) models have, aided by coarse-graining techniques, shown great success in capturing the mechanisms of protein folding and conformational changes. In certain cases, however, non-native interactions and atomic details are also essential to describe the protein dynamics, prompting the development of a variety of structure-based models which include non-native interactions, and differentiate between different types of attractive potentials. Here, we describe an all-protein-atom hybrid model, termed ProfasiGo, that integrates an implicit solvent all-atom physics-based model (called Profasi) and a structure-based Gō potential, and its implementation in two software packages (PHAISTOS and ProFASi) that are developed for Monte Carlo sampling of protein molecules. We apply the ProfasiGo model to study the folding free energy landscapes of four topologically similar proteins, one of which can be folded by the simplified potential Profasi, and two that have been folded by explicit solvent, all-atom molecular dynamics simulations with the CHARMM22∗ force field. Our results reveal that the hybrid ProfasiGo model is able to capture many of the details present in the physics-based potentials, while retaining the advantages of Gō models for sampling and guiding to the native state. We expect that the model will be widely applicable to study the folding of more complex proteins, or to study conformational dynamics and integration with experimental data.

scholarly journals Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias

2018 ◽  
Vol 122 (49) ◽  
pp. 11174-11185 ◽  
Author(s):  
Yong Wang ◽  
Pengfei Tian ◽  
Wouter Boomsma ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Monte Carlo ◽  
Protein Folding ◽  
Monte Carlo Sampling ◽  
Native State

A COMBINATION OF REPLICA EXCHANGE MONTE CARLO AND ENERGY LANDSCAPE PAVING ALGORITHMS TO INCREASE THE EFFECTIVENESS OF CONFORMATIONAL SAMPLING

2004 ◽  
Vol 15 (07) ◽  
pp. 933-937 ◽  
Author(s):  
HANDAN ARKIN
Keyword(s):  
Monte Carlo ◽  
Hybrid Algorithm ◽  
Energy Landscape ◽  
Monte Carlo Sampling ◽  
Replica Exchange ◽  
Conformational Sampling ◽  
Sampling Techniques ◽  
Replica Exchange Monte Carlo ◽  
Energy Landscape Paving ◽  
Low Energy Conformations

A combination of replica exchange Monte Carlo sampling techniques and energy landscape paving approach is presented. This hybrid algorithm combines the features of the energy landscape paving (ELP) and replica exchange methods (REM). I have tested its performance in studying the low-energy conformations of the benchmark peptide Met-enkephalin.

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