Omicron mutations increase interdomain interactions and reduce epitope exposure in the SARS-CoV-2 spike

Wieczór, Miłosz; Tang, Phu K; Orozco, Modesto; Cossio, Pilar

doi:10.1016/j.isci.2023.105981

Cited by 8 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the dynamics of the Omicron 𝑆 trimers were likely not affected by the 6𝑃 substitution, in line with the preserved immunogenicity of this construct (19). Therefore, our observed difference in 𝑅𝐵𝐷 state occupancy and dynamics between Omicron and Wuhan variants is likely due to the acquired mutations during SARS-CoV-2 evolution in the human population, as recently suggested by molecular dynamics simulations (45). Moreover, the higher population of closed states in Omicron would explain the lower multivalent binding to 𝐴𝐶𝐸2, as recently described (17).…”

Section: Real-time Conformational Dynamics Of 𝑅𝐵𝐷Ssupporting

confidence: 79%

See 1 more Smart Citation

Modulation of SARS-CoV-2 spike binding to ACE2 through conformational selection

Saha,

Fernanadez,

Sumbul

et al. 2024

Preprint

View full text Add to dashboard Cite

The first step of SARS-CoV-2 infection involves the interaction between the trimeric viral spike protein (𝑆) and the host angiotensin-converting enzyme 2 (𝐴𝐶𝐸2). The receptor binding domain (𝑅𝐵𝐷) of 𝑆 adopts two conformations: open and closed, respectively, accessible and inaccessible to 𝐴𝐶𝐸2. Therefore, 𝑅𝐵𝐷 motions are suspected to affect 𝐴𝐶𝐸2 binding; yet a quantitative description of the underlying mechanism has been elusive. Here, using single-molecule approaches, we visualize 𝑅𝐵𝐷 opening and closing and probe the 𝑆/𝐴𝐶𝐸2 interaction. Our results show that RBD dynamics affect 𝐴𝐶𝐸2 binding but not unbinding. The resulting modulation is quantitatively predicted by a conformational selection model in which each protomer behaves independently. Our work reveals a general molecular mechanism affecting binding affinity without altering binding strength, helping to understand coronavirus infection and immune evasion.

show abstract

Section: Real-time Conformational Dynamics Of 𝑅𝐵𝐷Ssupporting

confidence: 79%

“…The advantage of this apparently neutral deal would be that an Omicron spike with lower probability of presenting 𝑅𝐵𝐷 open is less susceptible to be neutralized by antibodies binding to the 𝑅𝐵𝐷. Therefore, our results partly explain the high immune evasion of the Omicron spike (45,62).…”

Section: Conformational Selection Modelmentioning

confidence: 70%

Modulation of SARS-CoV-2 spike binding to ACE2 through conformational selection

Saha,

Fernanadez,

Sumbul

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…A notable difference observed between the Omicron BA.1 S protein compared to previous SARS-CoV-2 variants is its tighter interprotomer packing and more compact architecture. 10 , 32 , 38 , 39 , 41 Omicron BA.1 had 15 RBD mutations compared to the initial SARS-CoV-2 S protein ( Figure 1A ), The S371L, S373P, and S375F substitutions occur in an interfacial RBD loop, contributing, along with Y505H, to interactions between the down-state RBDs. The S373P substitution also shifts a short RBD helix, forcing a rotation of the N-linked glycan at N343 and allowing S371L (in BA.1) and S371F (in BA.2 and all later Omicron variants) to point toward the glycan to stabilize its new conformation.…”

Section: Introductionmentioning

confidence: 99%

Evolution of the SARS-CoV-2 Omicron spike

Parsons,

Acharya

2023

Cell Reports

View full text Add to dashboard Cite

“…Most methods that tackle the problem of computing rates of rare transitions seek to generate a set of unbiased trajectories, either through combining direct sampling of many short trajectories from different starting points 13,14 as done in Markov State Modeling, through Monte Carlo in trajectory space as in Transition Path Sampling, 15 or by generating trajectories that progress in a particular coordinate as in Forward Flux Sampling, 16 Steered Transition Path Sampling, 17 Weighted Ensemble, 18 Transition Interface Sampling, 19 and Milestoning. 20 However, these methods are computationally expensive, and some scale poorly with system size, making them challenging to apply for the complex biophysical problems we are interested in studying, such as finding the time scale for protein−drug unbinding, 21 for the RBD opening of the SARS-CoV-2 Spike protein, 22 or for the unbinding of cytoskeletal adhesion proteins under force. 23−25 As such, we are interested in approaches that build on CV biasing methods, which have been used to probe conformational transitions with sufficient computational efficiency even for relatively complex biological assemblies.…”

Section: Introductionmentioning

confidence: 99%

Good Rates From Bad Coordinates: The Exponential Average Time-dependent Rate Approach

Mazzaferro,

Sasmal,

Cossio

et al. 2024

J. Chem. Theory Comput.

Self Cite

View full text Add to dashboard Cite

Our ability to calculate rate constants of biochemical processes using molecular dynamics simulations is severely limited by the fact that the time scales for reactions, or changes in conformational state, scale exponentially with the relevant free-energy barrier heights. In this work, we improve upon a recently proposed rate estimator that allows us to predict transition times with molecular dynamics simulations biased to rapidly explore one or several collective variables (CVs). This approach relies on the idea that not all bias goes into promoting transitions, and along with the rate, it estimates a concomitant scale factor for the bias termed the "CV biasing efficiency" γ. First, we demonstrate mathematically that our new formulation allows us to derive the commonly used Infrequent Metadynamics (iMetaD) estimator when using a perfect CV, where γ = 1. After testing it on a model potential, we then study the unfolding behavior of a previously well characterized coarse-grained protein, which is sufficiently complex that we can choose many different CVs to bias, but which is sufficiently simple that we are able to compute the unbiased rate directly. For this system, we demonstrate that predictions from our new Exponential Average Time-Dependent Rate (EATR) estimator converge to the true rate constant more rapidly as a function of bias deposition time than does the previous iMetaD approach, even for bias deposition times that are short. We also show that the γ parameter can serve as a good metric for assessing the quality of the biasing coordinate. We demonstrate that these results hold when applying the methods to an atomistic protein folding example. Finally, we demonstrate that our approach works when combining multiple less-than-optimal bias coordinates, and adapt our method to the related "OPES flooding" approach. Overall, our time-dependent rate approach offers a powerful framework for predicting rate constants from biased simulations.

show abstract

Omicron mutations increase interdomain interactions and reduce epitope exposure in the SARS-CoV-2 spike

Cited by 8 publications

References 48 publications

Modulation of SARS-CoV-2 spike binding to ACE2 through conformational selection

Modulation of SARS-CoV-2 spike binding to ACE2 through conformational selection

Evolution of the SARS-CoV-2 Omicron spike

Good Rates From Bad Coordinates: The Exponential Average Time-dependent Rate Approach

Contact Info

Product

Resources

About