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Record W3196904073 · doi:10.1101/2021.09.03.458946

Network analysis outlines strengths and weaknesses of emerging SARS-CoV-2 Spike variants

2021· preprint· en· W3196904073 on OpenAlexaff
Pedro D. Manrique, Sandipan Chakraborty, Khanh Nguyen, Rachael A. Mansbach, Bette Korber, S. Gnanakaran

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsConcordia University
FundersLos Alamos National Laboratory
KeywordsComputational biologyAllosteric regulationBiologyGeneticsReceptor

Abstract

fetched live from OpenAlex

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has triggered myriad efforts to dissect and understand the structure and dynamics of this complex pathogen. The Spike glycoprotein of SARS-CoV-2 has received special attention as it is the means by which the virus enters the human host cells. The N-terminal domain (NTD) is one of the targeted regions of the Spike protein for therapeutics and neutralizing antibodies against COVID-19. Though its function is not well-understood, the NTD is reported to acquire mutations and deletions that can accelerate the evolutionary adaptation of the virus driving antibody escape. Cellular processes are known to be regulated by complex interactions at the molecular level, which can be characterized by means of a graph representation facilitating the identification of key residues and critical communication pathways within the molecular complex. From extensive all-atom molecular dynamics simulations of the entire Spike for the wild-type and the dominant variant, we derive a weighted graph representation of the protein in two dominant conformations of the receptor-binding-domain; all-down and one-up. We implement graph theory techniques to characterize the relevance of specific residues at facilitating roles of communication and control, while uncovering key implications for fitness and adaptation. We find that many of the reported high-frequency mutations tend to occur away from the critical residues highlighted by our graph theory analysis, implying that these mutations tend to avoid targeting residues that are most critical for protein allosteric communication. We propose that these critical residues could be candidate targets for novel antibody therapeutics. In addition, our analysis provides quantitative insights of the critical role of the NTD and furin cleavage site and their wide-reaching influence over the protein at large. Many of our conclusions are supported by empirical evidence while others point the way towards crucial simulation-guided experiments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.308
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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