scholarly journals Mutations of SARS-CoV-2 RBD May Alter Its Molecular Structure to Improve Its Infection Efficiency

Biomolecules ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1273
Author(s):  
Ahmed L. Alaofi ◽  
Mudassar Shahid
Keyword(s):  
Free Energy ◽  
Angiotensin Converting Enzyme ◽  
Receptor Binding ◽  
Neutralizing Antibodies ◽  
Energy Landscape ◽  
Principal Component ◽  
Md Simulations ◽  
Free Energy Landscape ◽  
Binding Motif ◽  
Converting Enzyme

The receptor-binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mediates the viral–host interaction and is a target for most neutralizing antibodies. Nevertheless, SARS-CoV-2 RBD mutations pose a threat due to their role in host cell entry via the human angiotensin-converting enzyme 2 receptor that might strengthen SARS-CoV-2 infectivity, viral load, or resistance against neutralizing antibodies. To understand the molecular structural link between RBD mutations and infectivity, the top five mutant RBDs (i.e., N501Y, E484K L452R, S477N, and N439K) were selected based on their recorded case numbers. These mutants along with wild-type (WT) RBD were studied through all-atom molecular dynamics (MD) simulations of 100 ns. The principal component analysis and the free energy landscape were used too. Interestingly, N501Y, N439K, and E484K mutations were observed to increase the rigidity in some RBD regions while increasing the flexibility of the receptor-binding motif (RBM) region, suggesting a compensation of the entropy penalty. However, S477N and L452R RBDs were observed to increase the flexibility of the RBM region while maintaining similar flexibility in other RBD regions in comparison to WT RBD. Therefore, both mutations (especially S477N) might destabilize the RBD structure, as loose conformation compactness was observed. The destabilizing effect of S477N RBD was consistent with previous work on S477N mutation. Finally, the free energy landscape results showed that mutations changed WT RBD conformation while local minima were maintained for all mutant RBDs. In conclusion, RBD mutations definitely impact the WT RBD structure and conformation as well as increase the binding affinity to angiotensin-converting enzyme receptor.

Computational optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2

2020 ◽  
Author(s):  
S. Polydorides ◽  
G. Archontis
Keyword(s):  
Angiotensin Converting Enzyme ◽  
Receptor Binding ◽  
Protein Design ◽  
Md Simulations ◽  
Binding Motif ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
Computational Optimization ◽  
Binding Domains ◽  
Viral Sequences

ABSTRACTThe coronavirus SARS-CoV-2, that is responsible for the COVID-19 pandemic, and the closely related SARS-CoV coronavirus enter cells by binding at the human angiotensin converting enzyme 2 (hACE2). The stronger hACE2 affinity of SARS-CoV-2 has been connected with its higher infectivity. In this work, we study hACE2 complexes with the receptor binding domains (RBDs) of the human SARS-CoV-2 and human SARS-CoV viruses, using all-atom molecular dynamics (MD) simulations and Computational Protein Design (CPD) with a physics-based energy function. The MD simulations identify charge-modifying substitutions between the CoV-2 and CoV RBDs, which either increase or decrease the hACE2 affinity of the SARS-CoV-2 RBD. The combined effect of these mutations is small, and the relative affinity is mainly determined by substitutions at residues in contact with hACE2. Many of these findings are in line and interpret recent experiments. Our CPD calculations redesign positions 455, 493, 494 and 501 of the SARS-CoV-2 RBM, which contact hACE2 in the complex and are important for ACE2 recognition. Sampling is enhanced by an adaptive importance sampling Monte Carlo method. Sequences with increased affinity replace CoV-2 glutamine by a negative residue at position 493, and serine by nonpolar, aromatic or a threonine at position 494. Substitutions at positions positions 455 and 501 have a smaller effect on affinity. Substitutions suggested by our design are seen in viral sequences encountered in other species, including bat and pangolin. Our results might be used to identify potential virus strains with higher human infectivity and assist in the design of peptide-based or peptidomimetic compounds with the potential to inhibit SARS-CoV-2 binding at hACE2.SIGNIFICANCEThe coronavirus SARS-CoV-2 is responsible for the current COVID-19 pandemic. SARS-CoV-2 and the earlier, closely related SARS-CoV virus bind at the human angiotensin converting enzyme 2 (hACE2) receptor at the cell surface. The higher human infectivity of SARS-CoV-2 may be linked to its stronger affinity for hACE2. Here, we study by computational methods complexes of hACE2 with the receptor binding domains (RBDs) of viruses SARS-CoV-2 and SARS-CoV. We identify residues affecting the affinities of the two domains for hACE2. We also propose mutations at key SARS-CoV-2 positions, which might enhance hACE2 affinity. Such mutations may appear in viral strains with increased human infectivity and might assist the design of peptide-based compounds that inhibit infection of human cells by SARS-CoV-2.

Construction of the free energy landscape of biomolecules via dihedral angle principal component analysis

2008 ◽  
Vol 128 (24) ◽  
pp. 245102 ◽  
Author(s):  
Alexandros Altis ◽  
Moritz Otten ◽  
Phuong H. Nguyen ◽  
Rainer Hegger ◽  
Gerhard Stock
Keyword(s):  
Principal Component Analysis ◽  
Free Energy ◽  
Dihedral Angle ◽  
Energy Landscape ◽  
Principal Component ◽  
Component Analysis ◽  
Free Energy Landscape

scholarly journals Mutations in two SARS-CoV-2 variants of concern reflect two distinct strategies of antibody escape

2021 ◽  
Author(s):  
Sebastian Fiedler ◽  
Viola Denninger ◽  
Alexey S. Morgunov ◽  
Alison Ilsley ◽  
Roland Worth ◽  
...  
Keyword(s):  
Angiotensin Converting Enzyme ◽  
Receptor Binding ◽  
Neutralizing Antibodies ◽  
Neutralizing Antibody ◽  
Cell Receptor ◽  
Converting Enzyme ◽  
Wild Type ◽  
Angiotensin Converting Enzyme 2 ◽  
Binding Domains ◽  
Host Cell Receptor

Understanding the factors that contribute to antibody escape of SARS-CoV-2 and its variants is key for the development of drugs and vaccines that provide broad protection against a variety of virus variants. Using microfluidic diffusional sizing, we determined the dissociation constant ((KD)) for the interaction between receptor binding domains (RBDs) of SARS-CoV-2 in its original version (WT) as well as alpha and beta variants with the host-cell receptor angiotensin converting enzyme 2 (ACE2). For RBD-alpha, the ACE2-binding affinity was increased by a factor of ten when compared with RBD-WT, while ACE2-binding of RBD-beta was largely unaffected. However, when challenged with a neutralizing antibody that binds to both RBD-WT and RBD-alpha with low nanomolar (KD) values, RBD-beta displayed no binding, suggesting a substantial epitope change. In SARS-CoV-2 convalescent sera, RBD-binding antibodies showed low nanomolar affinities to both wild-type and variant RBD proteins—strikingly, the concentration of antibodies binding to RBD-beta was half that of RBD-WT and RBD-alpha, again indicating considerable epitope changes in the beta variant. Our data therefore suggests that one factor contributing to the higher transmissibility and antibody evasion of SARS-CoV-2 alpha and beta is a larger fraction of viruses that can form a complex with ACE2. However, the two variants employ different mechanisms to achieve this goal. While SARS-CoV-2 alpha RBD binds with greater affinity to ACE2 and is thus more difficult to displace from the receptor by neutralizing antibodies, RBD-beta is less accessible to antibodies due to epitope changes which increases the chances of ACE2-binding and infection.

scholarly journals Revealing Free Energy Landscape from MD Data via Conditional Angle Partition Tree

2021 ◽  
Author(s):  
Hangjin Jiang ◽  
Xuhui Huang ◽  
Han Li ◽  
Wing H Wong ◽  
Xiaodan Fan
Keyword(s):  
Free Energy ◽  
Energy Landscape ◽  
Dynamic Structure ◽  
Md Simulations ◽  
Free Energy Landscape ◽  
Partition Tree ◽  
Protein Protein Interaction ◽  
Geometric Similarity ◽  
The Hierarchical Structure ◽  
Alanine Dipeptide

Deciphering the free energy landscape of biomolecular structure space is crucial for understanding many complex molecular processes, such as protein-protein interaction, RNA folding, and protein folding. A major source of current dynamic structure data is Molecular Dynamics (MD) simulations. Several methods have been proposed to investigate the free energy landscape from MD data, but all of them rely on the assumption that kinetic similarity is associated with global geometric similarity, which may lead to unsatisfactory results. In this paper, we proposed a new method called Conditional Angle Partition Tree to reveal the hierarchical free energy landscape by correlating local geometric similarity with kinetic similarity. Its application on the benchmark alanine dipeptide MD data showed a much better performance than existing methods in exploring and understanding the free energy landscape. We also applied it to the MD data of Villin HP35. Our results are more reasonable on various aspects than those from other methods and very informative on the hierarchical structure of its energy landscape.

scholarly journals Multi-Level DBSCAN: A Hierarchical Density-Based Clustering Method for Analyzing Molecular Dynamics Simulation Trajectories

2021 ◽  
Author(s):  
Song Liu ◽  
Siqin Cao ◽  
Michael Alexander SUAREZ VASQUEZ ◽  
Eshani C Goonetillek ◽  
Xuhui Huang
Keyword(s):  
Free Energy ◽  
Level Density ◽  
Energy Landscape ◽  
Md Simulations ◽  
Metastable States ◽  
Dynamics Simulation ◽  
Free Energy Landscape ◽  
Islet Amyloid ◽  
Structural Details ◽  
Multi Level

Molecular Dynamic (MD) simulations have been extensively used as a powerful tool to investigate dynamics of biological molecules in recent decades. Generally, MD simulations generate high-dimensional data that is very hard to visualize and comprehend. As a result, clustering algorithms have been commonly used to reduce the dimensionality of MD data with the key benefit being their ability to reduce the dimensionality of MD data without prior knowledge of structural details or dynamic mechanisms. In this paper, we propose a new algorithm, the Multi-Level Density-Based Spatial Clustering of Applications with Noise (ML-DBSCAN), which combines the clustering results at different resolution of density levels to obtain the hierarchical structure of the free energy landscape and the metastable state assignment. At relatively low resolutions, the ML-DBSCAN can efficiently detect high population regions that contain all metastable states, while at higher resolutions, the ML-DBSCAN can find all metastable states and structural details of the free energy landscape. We demonstrate the powerfulness of the ML-DBSCAN in generating metastable states with a particle moving in a Mexican hat-like potential, and four peptide and protein examples are used to demonstrate how hierarchical structures of free energy landscapes can be found. Furthermore, we developed a GPU implementation of the ML-DBSCAN, which allows the algorithm to handle larger MD datasets and be up to two orders of magnitude faster than the CPU implementation. We demonstrate the power of the ML-DBSCAN on MD simulation datasets of five systems: a 2D-potential, alanine dipeptide, β-hairpin Tryptophan Zipper 2 (Trpzip2), Human Islet Amyloid Polypeptide (hIAPP), and Maltose Binding Protein (MBP). Our code is available at https://github.com/liusong299/ML-DBSCAN.

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