scholarly journals The Elastic Network Contact Model applied to RNA: enhanced accuracy for conformational space prediction

2017 ◽  
Author(s):  
Olivier Mailhot ◽  
Vincent Frappier ◽  
François Major ◽  
Rafael Najmanovich
Keyword(s):  
Normal Mode Analysis ◽  
Data Bank ◽  
Contact Model ◽  
Conformational Space ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Elastic Network ◽  
Rna Molecules ◽  
Coarse Grained Model ◽  
Anisotropic Network

ABSTRACTMotivationThe use of Normal Mode Analysis (NMA) methods to study both protein and nucleic acid dynamics is well established. However, the most widely used coarse-grained methods are based on backbone geometry alone and do not take into account the chemical nature of the residues. Elastic Network Contact Model (ENCoM) is a coarse-grained NMA method that includes a pairwise atom-type non-bonded interaction term, which makes it sensitive to the sequence of the studied molecule. We adapted ENCoM to simulate the dynamics of ribonucleic acid (RNA) molecules.ResultsENCoM outperforms the most commonly used coarse-grained model on RNA, Anisotropic Network Model (ANM), in the prediction of b-factors, in the prediction of conformational change as measured by overlap (a measure of effective prediction of structural transitions) and in the prediction of structural variance from NMR ensembles. These benchmarks were derived from the set of all RNA structures available from the Protein Data Bank (PDB) and contain more total cases than previous studies applying NMA to RNA. We thus established ENCoM as an attractive tool for fast and accurate exploration of the conformational space of RNA molecules.AvailabilityENCoM is open source software available at https://github.com/NRGlab/ENCoM

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.

A minimal coarse-grained model for the low-frequency normal mode analysis of icosahedral viral capsids

Soft Matter ◽  
2020 ◽  
Vol 16 (14) ◽  
pp. 3443-3455 ◽  
Author(s):  
M. Martín-Bravo ◽  
J. M. Gomez Llorente ◽  
J. Hernández-Rojas
Keyword(s):  
Structural Properties ◽  
Normal Mode ◽  
Normal Mode Analysis ◽  
Low Frequency ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Viral Capsids ◽  
Mode Spectrum ◽  
Coarse Grained Model

A minimal coarse-grained model unveils relevant structural properties of icosahedral viral capsids when fitted to reproduce their low-frequency normal-mode spectrum.

scholarly journals An Algorithm to Predict the Possible SARS-CoV-2 Mutations

2021 ◽  
Vol 3 (1) ◽  
pp. 1-7
Author(s):  
Raúl Isea
Keyword(s):  
Free Energy ◽  
Protein Data Bank ◽  
Gibbs Free Energy ◽  
Human Population ◽  
Normal Mode Analysis ◽  
Network Models ◽  
Data Bank ◽  
Mode Analysis ◽  
Vibration Modes ◽  
Anisotropic Network

An algorithm to determine the possible mutations that can occur in the S protein responsible of the Covid-19 in humans is designed. To do that, nine tridimensional sequences available in the Protein Data Bank similar to the initial strain sequenced in Wuhan (December 2019) are identified. The conditions driving this potential mutation are: (1) an accumulated number of mutations greater than (or equal to) 5 in each position; (2), a cumulative value of the different variations of Gibbs free energy less than -2.0 Kcal/mol; and (3), a squared fluctuation greater than 1.6 Å obtained according to calculations for normal mode analysis based on anisotropic network models (ANM) after averaging the first 20 vibration modes. The result is that 491 positions can mutate, while 424 positions did not provide any mutation. Finally, the results reveal that there are mutations that cannot be predicted, so more studies are needed to determine why they are present in the human population.

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 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 HOPMA: Boosting protein functional dynamics with colored contact maps

2021 ◽  
Author(s):  
Elodie Laine ◽  
Sergei Grudinin
Keyword(s):  
Normal Mode Analysis ◽  
Protein Structures ◽  
3D Structure ◽  
Normal Modes ◽  
Conformational Space ◽  
Mode Analysis ◽  
Elastic Network ◽  
Link Type ◽  
Input Structure ◽  
Functional States

In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches.We present HOPMA, a novel method to predict protein functional states and transitions using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures.The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.

scholarly journals Sequence-dependent Three Interaction Site (TIS) Model for Single and Double-stranded DNA

2018 ◽  
Author(s):  
Debayan Chakraborty ◽  
Naoto Hori ◽  
D. Thirumalai
Keyword(s):  
Electrostatic Interactions ◽  
Persistence Length ◽  
Data Bank ◽  
Coarse Grained ◽  
Force Extension ◽  
Sequence Dependence ◽  
Coarse Grained Model ◽  
Interaction Sites ◽  
Double Stranded Dna ◽  
Centers Of Mass

AbstractWe develop a robust coarse-grained model for single and double stranded DNA by representing each nucleotide by three interaction sites (TIS) located at the centers of mass of sugar, phosphate, and base. The resulting TIS model includes base-stacking, hydrogen bond, and electrostatic interactions as well as bond-stretching and bond angle potentials that account for the polymeric nature of DNA. The choices of force constants for stretching and the bending potentials were guided by a Boltzmann inversion procedure using a large representative set of DNA structures extracted from the Protein Data Bank. Some of the parameters in the stacking interactions were calculated using a learning procedure, which ensured that the experimentally measured melting temperatures of dimers are faithfully reproduced. Without any further adjustments, the calculations based on the TIS model reproduces the experimentally measured salt and sequence dependence of the size of single stranded DNA (ssDNA), as well as the persistence lengths of poly(dA) and poly(dT) chains. Interestingly, upon application of mechanical force the extension of poly(dA) exhibits a plateau, which we trace to the formation of stacked helical domains. In contrast, the force-extension curve (FEC) of poly(dT) is entropic in origin, and could be described by a standard polymer model. We also show that the persistence length of double stranded DNA, formed from two complementary ssDNAs with one hundred and thirty base pairs, is consistent with the prediction based on the worm-like chain. The persistence length, which decreases with increasing salt concentration, is in accord with the Odijk-Skolnick-Fixman theory intended for stiff polyelectrolyte chains near the rod limit. The range of applications, which did not require adjusting any parameter after the initial construction based solely on PDB structures and melting profiles of dimers, attests to the transferability and robustness of the TIS model for ssDNA and dsDNA.

Sign in / Sign up

Export Citation Format

Share Document