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Open AccessJournal ArticleDOI

Binding mode of SARS-CoV-2 fusion peptide to human cellular membrane.

TLDR
In this article, the authors used an array of molecular dynamics simulations that take advantage of the highly mobile membrane mimetic model to investigate the interaction of the SARS-CoV2 FP with a lipid bilayer representing mammalian cellular membranes at an atomic level and to characterize the membrane-bound form of the peptide.
About
This article is published in Biophysical Journal.The article was published on 2021-03-04 and is currently open access. It has received 28 citations till now. The article focuses on the topics: Host cell membrane & Lipid bilayer fusion.

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Effect of the Graphene Nanosheet on Functions of the Spike Protein in Open and Closed States: Comparison between SARS-CoV-2 Wild Type and the Omicron Variant

TL;DR: In this paper , the effect of the SARS-CoV-2 variant on the nanomaterial-spike protein interaction was investigated by molecular simulations, where the authors took the graphene nanosheet (GN) as the model and investigated its interaction with the spike protein in both wild type (WT) and Omicron.
Posted ContentDOI

Large fluctuations of the fusion intermediate help SARS-CoV-2 capture host cell membranes

TL;DR: In this paper, the authors constructed a full-length model of the CoV-2 FI by extrapolating from known pre-and post-fusion structures and showed that the FI is a remarkably flexible mechanical assembly executing large orientational and extensional fluctuations due to three hinges in the C-terminal base.
Posted ContentDOI

Binding of SARS-CoV-2 fusion peptide to host membranes

TL;DR: In this article, the authors use molecular dynamics simulations to study the two core functions of the SARS-CoV-2 peptide: to attach quickly to cellular membranes and to form an anchor strong enough to withstand the mechanical force during membrane fusion.
Journal ArticleDOI

Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation.

TL;DR: In this paper , two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design, are described and discussed. And the possibility to introduce all-atom molecular dynamics simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.
Journal ArticleDOI

Effect of Disulfide Bridge on the Binding of SARS-CoV-2 Fusion Peptide to Cell Membrane: A Coarse-Grained Study

Hujun Shen, +1 more
- 06 Oct 2022 - 
TL;DR: This work indicates that the stable helical structure is crucial for binding the SARS-CoV-2 FP to cell membranes, and the helical stability of FP should have a significant influence on the FP–membrane binding.
References
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Journal Article

Scikit-learn: Machine Learning in Python

TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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VMD: Visual molecular dynamics

TL;DR: VMD is a molecular graphics program designed for the display and analysis of molecular assemblies, in particular biopolymers such as proteins and nucleic acids, which can simultaneously display any number of structures using a wide variety of rendering styles and coloring methods.
Journal ArticleDOI

Comparison of simple potential functions for simulating liquid water

TL;DR: In this article, the authors compared the Bernal Fowler (BF), SPC, ST2, TIPS2, TIP3P, and TIP4P potential functions for liquid water in the NPT ensemble at 25°C and 1 atm.
Journal ArticleDOI

Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems

TL;DR: An N⋅log(N) method for evaluating electrostatic energies and forces of large periodic systems is presented based on interpolation of the reciprocal space Ewald sums and evaluation of the resulting convolutions using fast Fourier transforms.
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