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.

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

Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  
Keyword(s):  
Free Energy ◽  
Surface Area ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Binding Mode ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Mode Analysis ◽  
Energy Calculation ◽  
Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

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 MODE-TASK: Large-scale protein motion tools

2017 ◽  
Author(s):  
Caroline Ross ◽  
Bilal Nizami ◽  
Michael Glenister ◽  
Olivier Sheik Amamuddy ◽  
Ali Rana Atilgan ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Complexes ◽  
Normal Mode Analysis ◽  
Md Simulations ◽  
Supplementary Information ◽  
Mode Analysis ◽  
Analysis Tool ◽  
Link Type ◽  
Supplementary Material ◽  
Anisotropic Network

AbstractSummaryMODE-TASK, a novel software suite, comprises Principle Component Analysis, Multidimensional Scaling, and t-Distributed Stochastic Neighbor Embedding techniques using molecular dynamics trajectories. MODE-TASK also includes a Normal Mode Analysis tool based on Anisotropic Network Model so as to provide a variety of ways to analyse and compare large-scale motions of protein complexes for which long MD simulations are prohibitive.Availability and ImplementationMODE-TASK has been open-sourced, and is available for download from https://github.com/RUBi-ZA/MODE-TASK, implemented in Python and C++.Supplementary informationDocumentation available at http://mode-task.readthedocs.io.

scholarly journals Dynamics-function relationship of N-terminal acetyltransferases: the β6β7 loop modulates substrate accessibility to the catalytic site

2018 ◽  
Author(s):  
Angèle Abboud ◽  
Pierre Bédoucha ◽  
Jan Byška ◽  
Thomas Arnesen ◽  
Nathalie Reuter
Keyword(s):  
Normal Mode Analysis ◽  
Network Models ◽  
Catalytic Site ◽  
Mode Analysis ◽  
Ligand Specificity ◽  
Hairpin Loop ◽  
Domains Of Life ◽  
Function Relationship ◽  
Relationship Of

N-terminal acetyltransferases (NATs) are enzymes catalysing the transfer of the acetyl from Ac-CoA to the N-terminus of proteins, one of the most common protein modifications. Unlike NATs, lysine acetyltransferases (KATs) transfer an acetyl onto the amine group of internal lysines. To date, not much is known on the exclusive substrate specificity of NATs towards protein N-termini. All the NATs and some KATs share a common fold called GNAT. The main difference between NATs and KATs is an extra hairpin loop found only in NATs called β6β7 loop. It covers the active site as a lid. The hypothesized role of the loop is that of a barrier restricting the access to the catalytic site and preventing acetylation of internal lysines. We investigated the dynamics-function relationships of all available structures of NATs covering the three domains of life. Using elastic network models and normal mode analysis, we found a common dynamics pattern conserved through the GNAT fold; a rigid V-shaped groove, formed by the β4 and β5 strands and three relatively more dynamic loops α1α2, β3β4 and β6β7. We identified two independent dynamical domains in the GNAT fold, which is split at the β5 strand. We characterized the β6β7 hairpin loop slow dynamics and show that its movements are able to significantly widen the mouth of the ligand binding site thereby influencing its size and shape. Taken together our results show that NATs may have access to a broader ligand specificity range than anticipated.

Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  
Keyword(s):  
Free Energy ◽  
Surface Area ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Binding Mode ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Mode Analysis ◽  
Energy Calculation ◽  
Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

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