Predicting the low energy landscape of nanoscale silica using interatomic potentials

2006 ◽  
Vol 203 (6) ◽  
pp. 1319-1323 ◽  
Author(s):  
S. T. Bromley
Keyword(s):  
Energy Landscape ◽  
Low Energy ◽  
Interatomic Potentials

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 Role of bacteriophage T4 baseplate in regulating assembly and infection

2016 ◽  
Vol 113 (10) ◽  
pp. 2654-2659 ◽  
Author(s):  
Moh Lan Yap ◽  
Thomas Klose ◽  
Fumio Arisaka ◽  
Jeffrey A. Speir ◽  
David Veesler ◽  
...  
Keyword(s):  
Bacteriophage T4 ◽  
Energy Landscape ◽  
Structural Data ◽  
High Energy ◽  
Low Energy ◽  
Energy Structure ◽  
Host Bacterium ◽  
Energy Star

Bacteriophage T4 consists of a head for protecting its genome and a sheathed tail for inserting its genome into a host. The tail terminates with a multiprotein baseplate that changes its conformation from a “high-energy” dome-shaped to a “low-energy” star-shaped structure during infection. Although these two structures represent different minima in the total energy landscape of the baseplate assembly, as the dome-shaped structure readily changes to the star-shaped structure when the virus infects a host bacterium, the dome-shaped structure must have more energy than the star-shaped structure. Here we describe the electron microscopy structure of a 3.3-MDa in vitro-assembled star-shaped baseplate with a resolution of 3.8 Å. This structure, together with other genetic and structural data, shows why the high-energy baseplate is formed in the presence of the central hub and how the baseplate changes to the low-energy structure, via two steps during infection. Thus, the presence of the central hub is required to initiate the assembly of metastable, high-energy structures. If the high-energy structure is formed and stabilized faster than the low-energy structure, there will be insufficient components to assemble the low-energy structure.

Atomistic Structure of High Index Surfaces

1996 ◽  
Vol 440 ◽  
Author(s):  
Batsirai Mutasa ◽  
Diana Farkas
Keyword(s):  
Structural Unit ◽  
Structural Features ◽  
Low Energy ◽  
High Index ◽  
Interatomic Potentials ◽  
Embedded Atom ◽  
Unit Model ◽  
Ordered Alloys ◽  
Energy Surfaces ◽  
Atomistic Structure

AbstractInteratomic potentials of the embedded atom (EAM) type were used to study the atomistic structure of high index surfaces in metals and ordered alloys. The results show that a structural unit model can be developed to model the structure of the high index surfaces on the basis of the structure of a few low energy surfaces. The model can predict the structural features and give an estimate of the energies of the higher index surfaces. We present examples of Fe, B2 FeAl and NiAl.

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 Solvent inclusion in the crystal structure of bis[(adamantan-1-yl)methanaminium chloride] 1,4-dioxane hemisolvate monohydrate explained using the computed crystal energy landscape

2016 ◽  
Vol 72 (9) ◽  
pp. 1348-1352 ◽  
Author(s):  
Sharmarke Mohamed
Keyword(s):  
Crystal Structure ◽  
Hydrogen Bonding ◽  
Structure Prediction ◽  
Unit Cell Volume ◽  
Energy Landscape ◽  
Lattice Energy ◽  
Low Energy ◽  
Energy Minimum ◽  
Crystal Structure Prediction ◽  
Hydrogen Bonding Interactions

Repeated attempts to crystallize 1-adamantanemethylamine hydrochloride as an anhydrate failed but the salt was successfully crystallized as a solvate (2C11H20N+·2Cl−·0.5C4H8O2·H2O), with water and 1,4-dioxane playing a structural role in the crystal and engaging in hydrogen-bonding interactions with the cation and anion. Computational crystal-structure prediction was used to rationalize the solvent-inclusion behaviour of this salt by computing the solvent-accessible voids in the predicted low-energy structures for the anhydrate: the global lattice-energy minimum structure, which has the same packing of the ions as the solvate, has solvent-accessible voids that account for 3.71% of the total unit-cell volume and is 6 kJ mol−1more stable than the next most stable predicted structure.

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.

FunHunt: model selection based on energy landscape characteristics

2008 ◽  
Vol 36 (6) ◽  
pp. 1418-1421 ◽  
Author(s):  
Nir London ◽  
Ora Schueler-Furman
Keyword(s):  
Model Selection ◽  
Interface Design ◽  
Learning Algorithm ◽  
Protein Complexes ◽  
Energy Landscape ◽  
Minimum Energy ◽  
Low Energy ◽  
General Applicability ◽  
Landscape Characteristics ◽  
Low Energy Conformations

Protein folding and binding is commonly depicted as a search for the minimum energy conformation in a vast energy landscape. Indeed, modelling of protein complex structures by RosettaDock often results in a set of low-energy conformations near the native structure. Ensembles of low-energy conformations can appear, however, in other regions of the energy landscape, especially when backbone movements occur upon binding. What then characterizes the energy landscape near the correct orientation? We have applied a machine learning algorithm to distinguish ensembles of low-energy conformations around the native conformation from other low-energy ensembles. FunHunt, the resulting classifier, identified the native orientation for 50/52 protein complexes in a test set, and for all of 12 recent CAPRI targets. FunHunt is also able to choose the near-native orientation among models created by algorithms other than RosettaDock, demonstrating its general applicability for model selection. The features used by FunHunt teach us about the nature of native interfaces. Remarkably, the energy decrease of trajectories toward near-native orientations is significantly larger than for other orientations. This provides a possible explanation for the stability of association in the native orientation. The FunHunt approach, discriminating models based on ensembles of structures that map the nearby energy landscape, can be adapted and extended to additional tasks, such as ab initio model selection, protein interface design and specificity predictions.

Sign in / Sign up

Export Citation Format

Share Document