Theoretical causes of the Brazilian P.1 and P.2 lineages of the SARS-CoV-2 virus through molecular dynamics

  1. Micael D. L. Oliveira
  2. Kelson M. T. Oliveira
  3. Jonathas N. Silva
  4. Clarice Santos
  5. João Bessa
  6. Rosiane de Freitas

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Abstract

The new β -coronavirus has been causing sad losses around the world and the emergence of new variants has caused great concern. This pandemic is of a proportion not seen since the Spanish Flu in 1918. Thus, throughout this research, the B.1.1.28 lineage of the P.1 clade (K417T, N501Y, E484K) that emerged in Brazil was studied, as well as the latest Delta variant. This is because the molecular mechanisms by which phenotypic changes in transmissibility or mortality remain unknown. Through molecular dynamics simulations with the NAMD 3 algorithm in the 50 ns interval, it was possible to understand the impact on structural stabilization on the interaction of the ACE2-RBD complex, as well as simulations in 30 ns for the neutralizing antibody P2B-2F6, with this antibody was derived from immune cells from patients infected with SARS-CoV-2. Although not all molecular dynamics analyzes support the hypothesis of greater stability in the face of mutations, there was a predominance of low fluctuations. Thus, 3 (three) analyzes corroborate the hypothesis of greater ACE2-RBD stability as a result of P.1, among them: Low mean RMSF values, greater formation of hydrogen bonds and low solvent exposure measured by the SASA value. An inverse behavior occurs in the interaction with neutralizing antibodies, since the mutations induce greater instability and thus hinder the recognition of antibodies in neutralizing the Spike protein, where we noticed a smaller number of hydrogen bonds as a result of P.1. Through MM-PBSA energy decomposition, we found that Van der Waals interactions predominated and were more favorable when the structure has P.1 strain mutations. Therefore, we believe that greater stabilization of the ACE2-RBD complex may be a plausible explanation for why some mutations are converging in different strains, such as E484K and N501Y. The P.1 concern variant still makes the Spike protein recognizable by antibodies, and therefore, even if the vaccines’ efficacy can be diminished, there are no results in the literature that nullify them.

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  1. Version published to 10.1101/2021.04.09.439181v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.04.09.439181: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Wang et al., 2020) and antibody-antigen (PDB ID: 7BWJ) (Ju et al., 2020) with P.2 mutations was performed with Branch-and-Prune implementation algorithm.
    antibody-antigen
    suggested: None
    In parallel, the I-Mutant 3.0 (Capriotti, Fariselli, & Casadio, 2005) tool was used, which implements the SVM machine-learning approach to predict ΔΔG as a result of the emerging variants in the ACE2-Spike complex (PDB ID: 7DF4) and neutralizing antibodies RBD-Ty1 (PDB ID: 6ZXN) where we adopt the physiological condition at pH = 7.4, temperature of 25°C and SVM3 ternary classification algorithm where it denotes stability (ΔΔG ≥ +0.5kcal·mol−1), destabilization (ΔΔG ≤ −0.5kcal·mol−1) and neutrality (−0.5kcal·mol−1 ≤ ΔΔG ≤ +0.5kcal·mol−1).
    Capriotti, Fariselli,
    suggested: None
    Software and Algorithms
    SentencesResources
    The in silico mutation was performed in the PyMOL 2.3 software (Schrödinger, LLC, 2015) with the module “Mutagenesis” (see Figure 1) having the rotamer with lowest steric tension automatically defined by the software.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)
    The analysis of trajectories and construction of RMSD and RMSF graphics were possible with the MDAnalysis (Michaud-Agrawal, Denning, Woolf, & Beckstein, 2011) and Matplotlib libraries implemented in the Python 3 programming language with the Shrake and Rupley algorithm (Shrake & Rupley, 1973).
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)
    We used the Scipy library in Python for all statistical analysis with the command ttest_ind(a, b, equal_var = False), where (a, b) denote two matrices containing the molecular dynamics RMS data.
    Scipy
    suggested: (SciPy, RRID:SCR_008058)
    Python
    suggested: (IPython, RRID:SCR_001658)
    The component of Poisson-Boltzmann was estimated for the last frame by the finite difference method using the DelPhi v8.4.5 algorithm (Li et al., 2012) with a 0.0001 convergence criterion, in a 0.10M solution having a dielectric constant for the solute with ϵ = 2.0 and for the solvent the water dielectric constant was ϵ = 80.0.
    DelPhi
    suggested: (DelPhi, RRID:SCR_008669)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2021.04.09.439181v1 on bioRxiv