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 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 Coarse-Grained Protein Dynamics Studies Using Elastic Network Models

2018 ◽  
Vol 19 (12) ◽  
pp. 3899 ◽  
Author(s):  
Yuichi Togashi ◽  
Holger Flechsig
Keyword(s):  
Normal Mode Analysis ◽  
Protein Structures ◽  
Computational Cost ◽  
Network Models ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Nonlinear Phenomena ◽  
Reference Structure ◽  
Elastic Network ◽  
Elastic Network Models

Elastic networks have been used as simple models of proteins to study their slow structural dynamics. They consist of point-like particles connected by linear Hookean springs and hence are convenient for linear normal mode analysis around a given reference structure. Furthermore, dynamic simulations using these models can provide new insights. As the computational cost associated with these models is considerably lower compared to that of all-atom models, they are also convenient for comparative studies between multiple protein structures. In this review, we introduce examples of coarse-grained molecular dynamics studies using elastic network models and their derivatives, focusing on the nonlinear phenomena, and discuss their applicability to large-scale macromolecular assemblies.

scholarly journals A Comparative Study of All-atom Molecular Dynamics Simulation and Coarse-grained Normal Mode Analysis in Identifying Pre-existing Residue Interaction Networks that Promote Coupled-Domain Dynamics in Escherichia coli Methionyl-tRNA Synthetase

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Samuel Fehling ◽  
Alexander Strom ◽  
Brent Lehman ◽  
Ryan Andrews ◽  
Sudeep Bhattacharyya ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Normal Mode ◽  
Normal Mode Analysis ◽  
Trna Synthetase ◽  
Interaction Networks ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Residue Interaction ◽  
Domain Dynamics ◽  
Methionyl Trna Synthetase

Inter-domain communication plays a key role in the function of modular proteins. Earlier studies have demonstrated that the coupling of domain motions is important in mediating site-to-site communications in modular proteins. In the present study, bioinformatics and molecular simulations were usedto trace “pre-existing” residue-residue interaction networks that mediate coupled-domain dynamics in multi-domain Escherichia coli methionyl-tRNA synthetase (Ec MetRS). In particular, a comparative study was carried out to evaluate the effectiveness of coarse-grained normal mode analysis and all-atom molecular dynamic simulation in predicting pre-existing pathways of inter-domain communications in this enzyme. Integration of dynamic information of residues with their evolutionary features (conserved and coevolved) demonstrated that multiple residue-residue interaction networks exist in Ec MetRS that promote dynamic coupling between the anticodon binding domain and the connective polypeptide I domain, which are > 50Å apart, through correlated motions. Mutation of residues on these pathways have distinct impact on the dynamics and function of this enzyme. Moreover, the present study revealed that the dynamic information obtained from the coarse-grained normal mode analysis is comparable to the atomistic molecular dynamics simulations in predicting the interaction networks that are essential for promoting coupled-domain dynamics in Ec MetRS. KEYWORDS: Domain-domain Communication; Molecular Dynamics; Methionyl-tRNA Synthetase; Normal Mode Analysis; Coupled-domain Dynamics; Course-grained Normal Mode Analysis; Aminoacyl tRNA Synthetases; Statistical Coupling Analysis

Normal Mode Analysis of Trp RNA Binding Attenuation Protein: Structure and Collective Motions

2011 ◽  
Vol 51 (9) ◽  
pp. 2361-2371 ◽  
Author(s):  
Guang Hu ◽  
Servaas Michielssens ◽  
Samuel L. C. Moors ◽  
Arnout Ceulemans
Keyword(s):  
Protein Structure ◽  
Normal Mode ◽  
Rna Binding ◽  
Normal Mode Analysis ◽  
Mode Analysis ◽  
Collective Motions

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.

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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