Mechanical Properties of α-Helices Estimated Using Molecular Dynamics and Finite Element Simulations

2008 ◽  
Author(s):  
Peyman Honarmandi ◽  
Philip Bransford ◽  
Roger D. Kamm
Keyword(s):  
Mechanical Properties ◽  
Molecular Dynamics ◽  
Finite Element ◽  
Solid Mechanics ◽  
Normal Mode Analysis ◽  
Computational Cost ◽  
Md Simulations ◽  
Elastic Stiffness ◽  
Mode Analysis ◽  
Molecular Volume

Mechanical properties of biomolecules and their response to mechanical forces may be studied using Molecular Dynamics (MD) simulations. However, high computational cost is a primary drawback of MD simulations. This paper presents a computational framework based on the integration of the Finite Element Method (FEM) with MD simulations to calculate the mechanical properties of polyalanine α-helix proteins. In this method, proteins are treated as continuum elastic solids with molecular volume defined exclusively by their atomic surface. Therefore, all solid mechanics theories would be applicable for the presumed elastic media. All-atom normal mode analysis is used to calculate protein’s elastic stiffness as input to the FEM. In addition, constant force molecular dynamics (CFMD) simulations can be used to predict other effective mechanical properties, such as the Poisson’s Ratio. Force versus strain data help elucidate the mechanical behavior of α-helices upon application of constant load. The proposed method may be useful in identifying the mechanical properties of any protein or protein assembly with known atomic structure.

THEORETICAL INVESTIGATIONS ON ELUCIDATING FUNDAMENTAL MECHANISMS OF CATALYSIS AND DYNAMICS INVOLVED IN TRANSCRIPTION BY RNA POLYMERASE

2013 ◽  
Vol 12 (08) ◽  
pp. 1341005 ◽  
Author(s):  
FÁTIMA PARDO-AVILA ◽  
LIN-TAI DA ◽  
YING WANG ◽  
XUHUI HUANG
Keyword(s):  
Rna Polymerase ◽  
Conformational Changes ◽  
Normal Mode Analysis ◽  
Md Simulations ◽  
Mode Analysis ◽  
Transcription Process ◽  
Nucleotide Addition ◽  
Theoretical Investigations ◽  
State Models ◽  
Elongation Process

RNA polymerase is the enzyme that synthesizes RNA during the transcription process. To understand its mechanism, structural studies have provided us pictures of the series of steps necessary to add a new nucleotide to the nascent RNA chain, the steps altogether known as the nucleotide addition cycle (NAC). However, these static snapshots do not provide dynamic information of these processes involved in NAC, such as the conformational changes of the protein and the atomistic details of the catalysis. Computational studies have made efforts to fill these knowledge gaps. In this review, we provide examples of different computational approaches that have improved our understanding of the transcription elongation process for RNA polymerase, such as normal mode analysis, molecular dynamic (MD) simulations, Markov state models (MSMs). We also point out some unsolved questions that could be addressed using computational tools in the future.

scholarly journals Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis

2015 ◽  
Vol 7 (17) ◽  
pp. 2317-2331 ◽  
Author(s):  
Gautier Moroy ◽  
Olivier Sperandio ◽  
Shakti Rielland ◽  
Saurabh Khemka ◽  
Karen Druart ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Screening ◽  
Molecular Dynamics Simulations ◽  
Normal Mode ◽  
Normal Mode Analysis ◽  
Mode Analysis ◽  
Conformational Ensemble ◽  
Dynamics Simulations

scholarly journals A hierarchical Bayesian framework for force field selection in molecular dynamics simulations

2016 ◽  
Vol 374 (2060) ◽  
pp. 20150032 ◽  
Author(s):  
S. Wu ◽  
P. Angelikopoulos ◽  
C. Papadimitriou ◽  
R. Moser ◽  
P. Koumoutsakos
Keyword(s):  
Experimental Data ◽  
Molecular Dynamics ◽  
Force Field ◽  
Computational Cost ◽  
Md Simulations ◽  
Bayesian Framework ◽  
Underlying Structure ◽  
Hierarchical Bayesian ◽  
Wide Range ◽  
Selection Of

We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different environmental conditions with the corresponding variability in experimental data. The high computational cost associated with the hierarchical Bayesian framework is reduced by orders of magnitude through a parallelized Transitional Markov Chain Monte Carlo method combined with the Laplace Asymptotic Approximation. The suitability of the hierarchical approach is demonstrated by performing MD simulations with prescribed parameters to obtain data for transport coefficients under different conditions, which are then used to infer and evaluate the parameters of the MD model. We demonstrate the selection of MD models based on experimental data and verify that the hierarchical model can accurately quantify the uncertainty across experiments; improve the posterior probability density function estimation of the parameters, thus, improve predictions on future experiments; identify the most plausible force field to describe the underlying structure of a given dataset. The framework and associated software are applicable to a wide range of nanoscale simulations associated with experimental data with a hierarchical structure.

scholarly journals Normal Mode Analysis as a Routine Part of a Structural Investigation

Molecules ◽  
2019 ◽  
Vol 24 (18) ◽  
pp. 3293 ◽  
Author(s):  
Jacob A. Bauer ◽  
Jelena Pavlović ◽  
Vladena Bauerová-Hlinková
Keyword(s):  
Normal Mode ◽  
Structural Study ◽  
Computer Technology ◽  
Structural Investigation ◽  
Normal Mode Analysis ◽  
Computational Cost ◽  
Normal Modes ◽  
Mode Analysis ◽  
Computationally Expensive ◽  
The Web

Normal mode analysis (NMA) is a technique that can be used to describe the flexible states accessible to a protein about an equilibrium position. These states have been shown repeatedly to have functional significance. NMA is probably the least computationally expensive method for studying the dynamics of macromolecules, and advances in computer technology and algorithms for calculating normal modes over the last 20 years have made it nearly trivial for all but the largest systems. Despite this, it is still uncommon for NMA to be used as a component of the analysis of a structural study. In this review, we will describe NMA, outline its advantages and limitations, explain what can and cannot be learned from it, and address some criticisms and concerns that have been voiced about it. We will then review the most commonly used techniques for reducing the computational cost of this method and identify the web services making use of these methods. We will illustrate several of their possible uses with recent examples from the literature. We conclude by recommending that NMA become one of the standard tools employed in any structural study.

NO in Kr and Xe solids: Molecular dynamics and normal mode analysis

2005 ◽  
Vol 730 (1-3) ◽  
pp. 255-261 ◽  
Author(s):  
J.C. Castro Palacio ◽  
L. Velazquez Abad ◽  
G. Rojas-Lorenzo ◽  
J. Rubayo-Soneira
Keyword(s):  
Molecular Dynamics ◽  
Normal Mode ◽  
Normal Mode Analysis ◽  
Mode Analysis

Unusually high flexibility of graphene–Cu nanolayered composites under bending

2019 ◽  
Vol 21 (31) ◽  
pp. 17393-17399 ◽  
Author(s):  
Yuxin Zhao ◽  
Xiaoyi Liu ◽  
Jun Zhu ◽  
Sheng-Nian Luo
Keyword(s):  
Mechanical Properties ◽  
Molecular Dynamics ◽  
Analytical Model ◽  
Md Simulations ◽  
High Flexibility

The mechanical properties of graphene–Cu nanolayered (GCuNL) composites under bend loading are investigated via an energy-based analytical model and molecular dynamics (MD) simulations.

Normal-mode analysis without the Hessian: A driven molecular-dynamics approach

2003 ◽  
Vol 119 (2) ◽  
pp. 646-650 ◽  
Author(s):  
Joel M. Bowman ◽  
Xiubin Zhang ◽  
Alex Brown
Keyword(s):  
Molecular Dynamics ◽  
Normal Mode ◽  
Normal Mode Analysis ◽  
Mode Analysis
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