Predicting the mutational drivers of future SARS-CoV-2 variants of concern

Abstract

INTRODUCTIONSARS-CoV-2 evolution presents an ongoing challenge to public health. Tens of thousands of mutations have arisen in the SARS-CoV-2 genome as the pandemic has progressed. Understanding the relative importance of mutations in viral proteins, particularly those of relevance for antiviral immunity, is key to allocating preparedness efforts. Mutations in the viral Spike protein have received particular attention because Spike is the target of antibody-mediated immunity and is the primary antigen in current vaccines (1). As of December 1st, 2021, there are 10,381 distinct amino acid substitutions, insertions, or deletions in Spike sequences from the GISAID database (2). These mutations occur at all but one position in the protein, in different combinations, creating over 160,000 unique Spike protein sequences. A small subset of these mutations are components of "Variants Being Monitored" (VBMs), "Variants of Interest" (VOIs) or "Variants of Concern" (VOCs), as classified by the United States Centers for Disease Control (CDC) (3). The distinction between VOIs and the higher alert VOCs is whether a negative clinical impact is suspected or confirmed. VBMs are variants that would be classified as VOCs if not for low prevalence.Early statistical and algorithmic identification of the key Spike amino acid changes contributing to future putative VBM/VOI/VOCs are of clear benefit to public health strategy. Such predictions could enhance the identification of vulnerabilities for antibody-based therapeutics, vaccines, and diagnostics. Predicting future successful mutations would extend the time available to develop proactive responses at earlier stages of spread. It would also complement existing forecasting efforts which seek to predict overall SARS-CoV-2 incidence, hospitalizations, and death over time (4-6). Focus on the success of individual mutations rather than genomic variants also facilitates longer-term forecasting. The

Stability and expression of SARS-CoV-2 spike-protein mutations

“…[76] In addition to these two, protein fold stability is commonly important during the evolution of new functionality, as it constrains the options available (function-stability tradeoffs)[29, 30, 77, 78] and has been speculated to play a role in SARS-CoV-2 S-protein evolution as well. [28] In order to understand and possibly predict SARS-CoV-2 evolution, an important health challenge,[28, 39] we evaluated such contributions of protein fold stability to the S-protein RBD mutation space.…”

“…[33][34][35][36][37] Understanding the importance of fold stability-function tradeoffs within the S-protein could be important for estimating the tolerability of new mutations and the likelihood and fitness of new emerging variants, which will remain an important topic even in the postpandemic period. [39] To understand this, we can compute the stability effects of the many mutations possible in the RBD. Many programs exist based on machine-learning or energybased force-fields that can compute changes in protein stability upon mutation, [21,[40][41][42][43][44][45][46][47] with variable accuracy and issues relating to systematic errors (biases) and results being dependent on input structures used.…”

Evolutionary velocity with protein language models predicts evolutionary dynamics of diverse proteins

“…We next applied our algorithm to analyze 75,584 sequences of the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across a much shorter historical timescale of around 21 months. The sequence similarity network reconstructs the overall trajectory of spike evolution, and evo-velocity analysis identifies the sequence clusters associated with later sequences, including the B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.1 (Kappa), B.1.617.2 (Delta), and P.1 (Gamma) variants-of-concern (Maher et al, 2022;Walensky et al, 2021), as later in pseudotime (Figures 3E-3G). Despite a shorter evolutionary timescale, evo-velocity pseudotime and sampling date still had a Spearman correlation of 0.55 (twosided t-distribution p < 1 3 10 À308 ).…”

Rocaglates as Antivirals: Comparing the Effects on Viral Resistance, Anti-Coronaviral Activity, RNA-Clamping on eIF4A and Immune Cell Toxicity

“…Despite their increased replication fidelity, coronaviruses retained their capability of readily acquiring mutations under specific selection pressures, often resulting in changes in replication efficiency, disease severity, transmissibility and antigenicity. For example, genetic variants (of concern) with altered phenotypes continue to emerge in the current SARS-CoV-2 pandemic, preferentially with mutations in the spike protein gene that improve replication efficiency, transmissibility or lead to immune escape [ 7 , 8 ].…”