scholarly journals Skeletal animation for visualizing dynamic shapes of macromolecules

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Yutaka Ueno ◽  
Shinya Muraoka ◽  
Tetsuo Sato
Keyword(s):  
Computer Graphics ◽  
Normal Mode Analysis ◽  
Normal Modes ◽  
Dynamics Simulation ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Protein Molecules ◽  
Skeletal Animation ◽  
Detailed Computer ◽  
Animation Software

AbstractWe apply a skeletal animation technique developed for general computer graphics animation to display the dynamic shape of protein molecules. Polygon-based models for macromolecules such as atomic representations, surface models, and protein ribbon models are deformed by the motion of skeletal bones that provide coarse-grained descriptions of detailed computer graphics models. Using the animation software Blender, we developed methods to generate the skeletal bones for molecules. Our example of the superposition of normal modes demonstrates the thermal fluctuating motion obtained from normal mode analysis. The method is also applied to display the motions of protein molecules using trajectory coordinates of a molecular dynamics simulation. We found that a standard motion capture file was practical and useful for describing the motion of the molecule using available computer graphics tools.

Cofilin reduces the mechanical properties of actin filaments: approach with coarse-grained methods

2015 ◽  
Vol 17 (12) ◽  
pp. 8148-8158 ◽  
Author(s):  
Jae In Kim ◽  
Junpyo Kwon ◽  
Inchul Baek ◽  
Harold S. Park ◽  
Sungsoo Na
Keyword(s):  
Mechanical Properties ◽  
Normal Mode Analysis ◽  
Beam Theory ◽  
Dynamics Simulation ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Elastic Network Model ◽  
Elastic Network ◽  
Good Agreement ◽  
The Continuum

We applied a coarse-grained molecular dynamics simulation (CGMD) method and constructed elastic network model-based structures, actin and cofilactin filaments. Based on a normal mode analysis, the continuum beam theory was used to calculate the mechanical properties and the results showed good agreement with the established experimental data.

scholarly journals A coarse-grained elastic network atom contact model and its use in the simulation of protein dynamics and the prediction of the effect of mutations

2013 ◽  
Author(s):  
Vincent Frappier ◽  
Rafael Najmanovich
Keyword(s):  
Potential Energy ◽  
Normal Mode Analysis ◽  
Protein Structures ◽  
Normal Modes ◽  
Potential Energy Function ◽  
Contact Model ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Elastic Network Model ◽  
Elastic Network

Normal mode analysis (NMA) methods are widely used to study dynamic aspects of protein structures. Two critical components of NMA methods are coarse-graining in the level of simplification used to represent protein structures and the choice of potential energy functional form. There is a trade-off between speed and accuracy in different choices. In one extreme one finds accurate but slow molecular-dynamics based methods with all-atom representations and detailed atom potentials. On the other extreme, fast elastic network model (ENM) methods with Cαonly representations and simplified potentials that based on geometry alone, thus oblivious to protein sequence. Here we present ENCoM, an Elastic Network Contact Model that employs a potential energy function that includes a pairwise atom-type non-bonded interaction term and thus makes it possible to consider the effect of the specific nature of amino-acids on dynamics within the context of NMA. ENCoM is as fast as existing ENM methods and outperforms such methods in the generation of conformational ensembles. Here we introduce a new application for NMA methods with the use of ENCoM in the prediction of the effect of mutations on protein stability. While existing methods are based on machine learning or enthalpic considerations, the use of ENCoM, based on vibrational normal modes, is based on entropic considerations. This represents a novel area of application for NMA methods and a novel approach for the prediction of the effect of mutations. We compare ENCoM to a large number of methods in terms of accuracy and self-consistency. We show that the accuracy of ENCoM is comparable to that of the best existing methods. We show that existing methods are biased towards the prediction of destabilizing mutations and that ENCoM is less biased at predicting stabilizing mutations.

scholarly journals ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

2019 ◽  
Vol 47 (W1) ◽  
pp. W471-W476 ◽  
Author(s):  
Rasim Murat Aydınkal ◽  
Onur Serçinoğlu ◽  
Pemra Ozbek
Keyword(s):  
Protein Structure ◽  
Normal Mode Analysis ◽  
Shortest Paths ◽  
Protein Structures ◽  
Interaction Strength ◽  
Biological Information ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Residue Interaction ◽  
Natural Variant

AbstractProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

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.

Structure network analysis to gain insights into GPCR function

2016 ◽  
Vol 44 (2) ◽  
pp. 613-618 ◽  
Author(s):  
Francesca Fanelli ◽  
Angelo Felline ◽  
Francesco Raimondi ◽  
Michele Seeber
Keyword(s):  
G Protein ◽  
Normal Mode Analysis ◽  
Network Models ◽  
Md Simulations ◽  
G Protein Coupled Receptors ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Biomolecular Systems ◽  
Retinal Chromophore ◽  
G Protein Coupled

G protein coupled receptors (GPCRs) are allosteric proteins whose functioning fundamentals are the communication between the two poles of the helix bundle. Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system's dynamics can be provided by atomistic molecular dynamics (MD) simulations or coarse grained elastic network models paired with normal mode analysis (ENM–NMA). The present review article describes the application of PSN analysis to uncover the structural communication in G protein coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding, dimerization and activation are described. Focus is put on the ENM–NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signalling active (meta II (MII)) states, highlighting changes in structure network and centrality of the retinal chromophore in differentiating the inactive and active states of the receptor.

The type IA topoisomerase catalytic cycle: A normal mode analysis and molecular dynamics simulation

2008 ◽  
Vol 71 (4) ◽  
pp. 1984-1994 ◽  
Author(s):  
Bing Xiong ◽  
David L. Burk ◽  
Jianhua Shen ◽  
Xiaomin Luo ◽  
Hong Liu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Normal Mode ◽  
Normal Mode Analysis ◽  
Catalytic Cycle ◽  
Dynamics Simulation ◽  
Mode Analysis ◽  
Type Ia

scholarly journals A force field for virtual atom molecular mechanics of proteins

2009 ◽  
Vol 106 (37) ◽  
pp. 15667-15672 ◽  
Author(s):  
Anil Korkut ◽  
Wayne A. Hendrickson
Keyword(s):  
Force Field ◽  
Molecular Mechanics ◽  
Normal Mode ◽  
Conformational Changes ◽  
Normal Mode Analysis ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Biological Macromolecules ◽  
Molecular Mechanics Calculations ◽  
Low Degrees

Activities of many biological macromolecules involve large conformational transitions for which crystallography can specify atomic details of alternative end states, but the course of transitions is often beyond the reach of computations based on full-atomic potential functions. We have developed a coarse-grained force field for molecular mechanics calculations based on the virtual interactions of Cα atoms in protein molecules. This force field is parameterized based on the statistical distribution of the energy terms extracted from crystallographic data, and it is formulated to capture features dependent on secondary structure and on residue-specific contact information. The resulting force field is applied to energy minimization and normal mode analysis of several proteins. We find robust convergence in minimizations to low energies and energy gradients with low degrees of structural distortion, and atomic fluctuations calculated from the normal mode analyses correlate well with the experimental B-factors obtained from high-resolution crystal structures. These findings suggest that the virtual atom force field is a suitable tool for various molecular mechanics applications on large macromolecular systems undergoing large conformational changes.

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