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

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TLDR
In this paper , the predictive value of features comprising epidemiology, evolution, immunology, and neural network-based protein sequence modeling was used to identify primary biological drivers of SARS-CoV-2 intrapandemic evolution.
Abstract
SARS-CoV-2 evolution threatens vaccine- and natural infection–derived immunity and the efficacy of therapeutic antibodies. To improve public health preparedness, we sought to predict which existing amino acid mutations in SARS-CoV-2 might contribute to future variants of concern. We tested the predictive value of features comprising epidemiology, evolution, immunology, and neural network–based protein sequence modeling and identified primary biological drivers of SARS-CoV-2 intrapandemic evolution. We found evidence that ACE2-mediated transmissibility and resistance to population-level host immunity has waxed and waned as a primary driver of SARS-CoV-2 evolution over time. We retroactively identified with high accuracy (area under the receiver operator characteristic curve = 0.92 to 0.97) mutations that will spread, at up to 4 months in advance, across different phases of the pandemic. The behavior of the model was consistent with a plausible causal structure where epidemiological covariates combine the effects of diverse and shifting drivers of viral fitness. We applied our model to forecast mutations that will spread in the future and characterize how these mutations affect the binding of therapeutic antibodies. These findings demonstrate that it is possible to forecast the driver mutations that could appear in emerging SARS-CoV-2 variants of concern. We validated this result against Omicron, showing elevated predictive scores for its component mutations before emergence and rapid score increase across daily forecasts during emergence. This modeling approach may be applied to any rapidly evolving pathogens with sufficiently dense genomic surveillance data, such as influenza, and unknown future pandemic viruses.

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Journal ArticleDOI

The Evolution and Biology of SARS-CoV-2 Variants

TL;DR: The most important adaptation of the bat coronavirus progenitor of both SARS-CoV-1 and SARS CoV-2 for human infection (and other mammals) is the use of the angiotensin-converting enzyme 2 (ACE2) receptor as mentioned in this paper .
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Deep mutational scans for ACE2 binding, RBD expression, and antibody escape in the SARS-CoV-2 Omicron BA.1 and BA.2 receptor-binding domains

TL;DR: SARS-CoV-2 continues to acquire mutations in the spike receptor-binding domain (RBD) that impact ACE2 receptor binding, folding stability, and antibody recognition, and these mutations shape the future evolutionary potential of the virus through the phenomenon of epistasis.
Journal ArticleDOI

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

TL;DR: The authors used a protein language model to predict the local evolution within protein families and recover a dynamic "vector field" of protein evolution that they call evolutionary velocity (evo-velocity).
Journal ArticleDOI

The evolution of SARS-CoV-2

TL;DR: In this paper , the authors explored the mechanisms that generate genetic variation in SARS-CoV-2, underlying the within-host and population-level processes that underpin these events, and examined the selective forces that likely drove the evolution of higher transmissibility and, in some cases, higher severity during the first year of the pandemic and the role of antigenic evolution during the second and third years.
References
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Journal ArticleDOI

HyPhy: hypothesis testing using phylogenies

TL;DR: The HyPhypackage is designed to provide a flexible and unified platform for carrying out likelihood-based analyses on multiple alignments of molecular sequence data, with the emphasis on studies of rates and patterns of sequence evolution.
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Not So Different After All: A Comparison of Methods for Detecting Amino Acid Sites Under Selection

TL;DR: Three approaches for estimating the rates of nonsynonymous and synonymous changes at each site in a sequence alignment in order to identify sites under positive or negative selection are considered, suggesting that previously reported differences between results obtained by counting methods and random effects models arise due to a combination of the conservative nature of counting-based methods, the failure of current random effect models to allow for variation in synonymous substitution rates, and the naive application ofrandom effects models to extremely sparse data sets.
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Data, disease and diplomacy: GISAID's innovative contribution to global health

TL;DR: The article finds that the Global Initiative on Sharing All Influenza Data contributes to global health in at least five ways: collating the most complete repository of high‐quality influenza data in the world; facilitating the rapid sharing of potentially pandemic virus information during recent outbreaks; supporting the World Health Organization's biannual seasonal flu vaccine strain selection process; developing informal mechanisms for conflict resolution around the sharing of virus data.
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