STUDIES OF FINDING LOW ENERGY CONFIGURATIONS IN OFF-LATTICE PROTEIN MODELS

2006 ◽  
Vol 05 (03) ◽  
pp. 587-594 ◽  
Author(s):  
JINGFA LIU ◽  
WENQI HUANG
Keyword(s):  
Lower Energy ◽  
Gradient Descent ◽  
Energy Landscape ◽  
Three Dimensional ◽  
Ground States ◽  
Low Energy ◽  
Protein Models ◽  
Energy Configurations ◽  
Energy Landscape Paving ◽  
Lattice Protein Models

We studied two three-dimensional off-lattice protein models with two species of monomers, hydrophobic and hydrophilic. Low energy configurations in both models were optimized using the energy landscape paving (ELP) method and subsequent gradient descent. The numerical results show that the proposed methods are very promising for finding the ground states of proteins. For all sequences with lengths 13 ≤ n ≤ 55, the algorithm finds states with lower energy than previously proposed putative ground states.

scholarly journals THE STRUCTURE OF THE FREE ENERGY SURFACE OF COARSE-GRAINED OFF-LATTICE PROTEIN MODELS

2007 ◽  
Vol 18 (01) ◽  
pp. 99-106 ◽  
Author(s):  
ETHEM AKTÜRK ◽  
HANDAN ARKIN ◽  
TARIK ÇELİK
Keyword(s):  
Energy Surface ◽  
Energy Landscape ◽  
Low Energy ◽  
Coarse Grained ◽  
Free Energy Surface ◽  
Protein Model ◽  
Energy Part ◽  
Folding Pathways ◽  
Protein Models ◽  
Lattice Protein Models

We have performed multicanonical simulations of hydrophobic-hydrophilic heteropolymers with a simple effective, coarse-grained off-lattice model to study the structure and the topology of the energy surface. The multicanonical method samples the whole rugged energy landscape, in particular the low-energy part, and enables one to better understand the critical behaviors and visualize the folding pathways of the considered protein model.

SIMULATIONS OF PEPTIDE MODELS IN A SOLVENT

2004 ◽  
Vol 15 (02) ◽  
pp. 223-231 ◽  
Author(s):  
HANDAN ARKIN
Keyword(s):  
Aqueous Solution ◽  
Energy Landscape ◽  
Three Dimensional ◽  
Solvation Energy ◽  
Low Energy ◽  
Energy Term ◽  
Nmr Data ◽  
Peptide Models ◽  
Ramachandran Plots ◽  
Energy Landscape Paving

The three-dimensional structures of the heptapeptide deltorphin ( H - Tyr 1- D - Met 2- Phe 3- His 4- Leu 5- Met 6- Asp 7- NH 2) are studied in aqueous solution using Energy Landscape Paving (ELP) method. The effect of a solvation energy term on the conformations are determined by analyzing Ramachandran plots. The structures are compared with experimental NMR data. By minimizing the energy structures, the low-energy microstates of the molecule in aqueous solution are determined.

A FAST AND EFFECTIVE CONFORMATIONAL SEARCH METHOD FOR PEPTIDES

2003 ◽  
Vol 14 (07) ◽  
pp. 985-991 ◽  
Author(s):  
HANDAN ARKIN ◽  
TARIK ÇELIK
Keyword(s):  
Monte Carlo ◽  
Hybrid Algorithm ◽  
Energy Landscape ◽  
Search Method ◽  
Low Energy ◽  
Conformational Search ◽  
Energy Landscape Paving ◽  
Low Energy Conformations

We propose a hybrid algorithm, which combines the features of the energy landscape paving (ELP) and Monte Carlo Minimization (MCM) methods. We have tested its performance in studying the low-energy conformations of the heptapeptide deltorphin.

scholarly journals Finding low-energy conformations of lattice protein models by quantum annealing

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Alejandro Perdomo-Ortiz ◽  
Neil Dickson ◽  
Marshall Drew-Brook ◽  
Geordie Rose ◽  
Alán Aspuru-Guzik
Keyword(s):  
Low Energy ◽  
Quantum Annealing ◽  
Protein Models ◽  
Lattice Protein Models ◽  
Low Energy Conformations

scholarly journals An energy landscape approach to locomotor transitions in complex 3D terrain

2020 ◽  
Vol 117 (26) ◽  
pp. 14987-14995 ◽  
Author(s):  
Ratan Othayoth ◽  
George Thoms ◽  
Chen Li
Keyword(s):  
Potential Energy ◽  
Complex Terrain ◽  
Statistical Physics ◽  
Energy Landscape ◽  
Three Dimensional ◽  
Single Mode ◽  
Physical Interaction ◽  
Landscape Approach ◽  
3D Terrain ◽  
Potential Energy Landscape

Effective locomotion in nature happens by transitioning across multiple modes (e.g., walk, run, climb). Despite this, far more mechanistic understanding of terrestrial locomotion has been on how to generate and stabilize around near–steady-state movement in a single mode. We still know little about how locomotor transitions emerge from physical interaction with complex terrain. Consequently, robots largely rely on geometric maps to avoid obstacles, not traverse them. Recent studies revealed that locomotor transitions in complex three-dimensional (3D) terrain occur probabilistically via multiple pathways. Here, we show that an energy landscape approach elucidates the underlying physical principles. We discovered that locomotor transitions of animals and robots self-propelled through complex 3D terrain correspond to barrier-crossing transitions on a potential energy landscape. Locomotor modes are attracted to landscape basins separated by potential energy barriers. Kinetic energy fluctuation from oscillatory self-propulsion helps the system stochastically escape from one basin and reach another to make transitions. Escape is more likely toward lower barrier direction. These principles are surprisingly similar to those of near-equilibrium, microscopic systems. Analogous to free-energy landscapes for multipathway protein folding transitions, our energy landscape approach from first principles is the beginning of a statistical physics theory of multipathway locomotor transitions in complex terrain. This will not only help understand how the organization of animal behavior emerges from multiscale interactions between their neural and mechanical systems and the physical environment, but also guide robot design, control, and planning over the large, intractable locomotor-terrain parameter space to generate robust locomotor transitions through the real world.

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