Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 – 2021/03) Using a Statistical Learning Strategy

Zhao, Lue Ping; Lybrand, Terry P.; Gilbert, Peter B.; Hawn, Thomas R.; Schiffer, Joshua T.; Stamatatos, Leonidas; Payne, Thomas H.; Carpp, Lindsay N.; Geraghty, Daniel E.; Jerome, Keith R.

doi:10.1101/2021.06.15.448495

Cited by 6 publications

(1 citation statement)

References 53 publications

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“…The pairwise heat map between variants and mutations (Table 2) indicates that variants do not necessarily evolve with mutations, and the genomes may spontaneously acquire or revert to wildtype, as demonstrated in other statistical analysis for monitoring this pandemic. 57 The Algorithm…”

Section: Quantitation and Analysis Of Variants Of Concernmentioning

confidence: 99%

Algorithm for the Quantitation of Variants of Concern for Rationally Designed Vaccines Based on the Isolation of SARS-CoV-2 Hawaiʻi Lineage B.1.243

Maison

Ching

Cleveland

et al. 2021

Preprint

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SARS-CoV-2 worldwide emergence and evolution has resulted in variants containing mutations resulting in immune evasive epitopes that decrease vaccine efficacy. We acquired clinical samples, analyzed SARS-CoV-2 genomes, used the most worldwide emerged spike mutations from Variants of Concern/Interest, and developed an algorithm for monitoring the SARS-CoV-2 vaccine platform. The algorithm partitions logarithmic-transformed prevalence data monthly and Pearson’s correlation determines exponential emergence. The SARS-CoV-2 genome evaluation indicated 49 mutations. Nine of the ten most worldwide prevalent (>70%) spike protein changes have r-values >0.9. The tenth, D614G, has a prevalence >99% and r-value of 0.67. The resulting algorithm is based on the patterns these ten substitutions elucidated. The strong positive correlation of the emerged spike protein changes and algorithmic predictive value can be harnessed in designing vaccines with relevant immunogenic epitopes. SARS-CoV-2 is predicted to remain endemic and continues to evolve, so must SARS-CoV-2 monitoring and next-generation vaccine design.

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Section: Quantitation and Analysis Of Variants Of Concernmentioning

confidence: 99%

Algorithm for the Quantitation of Variants of Concern for Rationally Designed Vaccines Based on the Isolation of SARS-CoV-2 Hawaiʻi Lineage B.1.243

Maison

Ching

Cleveland

et al. 2021

Preprint

View full text Add to dashboard Cite

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Rapidly identifying new coronavirus mutations of potential concern in the Omicron variant using an unsupervised learning strategy

Zhao

Lybrand

Gilbert

et al. 2022

Sci Rep

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Extensive mutations in the Omicron spike protein appear to accelerate the transmission of SARS-CoV-2, and rapid infections increase the odds that additional mutants will emerge. To build an investigative framework, we have applied an unsupervised machine learning approach to 4296 Omicron viral genomes collected and deposited to GISAID as of December 14, 2021, and have identified a core haplotype of 28 polymutants (A67V, T95I, G339D, R346K, S371L, S373P, S375F, K417N, N440K, G446S, S477N, T478K, E484A, Q493R, G496S, Q498R, N501Y, Y505H, T547K, D614G, H655Y, N679K, P681H, N764K, K796Y, N856K, Q954H, N69K, L981F) in the spike protein and a separate core haplotype of 17 polymutants in non-spike genes: (K38, A1892) in nsp3, T492 in nsp4, (P132, V247, T280, S284) in 3C-like proteinase, I189 in nsp6, P323 in RNA-dependent RNA polymerase, I42 in Exonuclease, T9 in envelope protein, (D3, Q19, A63) in membrane glycoprotein, and (P13, R203, G204) in nucleocapsid phosphoprotein. Using these core haplotypes as reference, we have identified four newly emerging polymutants (R346, A701, I1081, N1192) in the spike protein (p value = 9.37*10−4, 1.0*10−15, 4.76*10−7 and 1.56*10−4, respectively), and five additional polymutants in non-spike genes (D343G in nucleocapsid phosphoprotein, V1069I in nsp3, V94A in nsp4, F694Y in the RNA-dependent RNA polymerase and L106L/F of ORF3a) that exhibit significant increasing trajectories (all p values < 1.0*10−15). In the absence of relevant clinical data for these newly emerging mutations, it is important to monitor them closely. Two emerging mutations may be of particular concern: the N1192S mutation in spike protein locates in an extremely highly conserved region of all human coronaviruses that is integral to the viral fusion process, and the F694Y mutation in the RNA polymerase may induce conformational changes that could impact remdesivir binding.

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Dynamic SARS-CoV-2 emergence algorithm for rationally-designed logical next-generation vaccines

et al. 2022

View full text Add to dashboard Cite

SARS-CoV-2 worldwide spread and evolution has resulted in variants containing mutations resulting in immune evasive epitopes that decrease vaccine efficacy. We acquired SARS-CoV-2 positive clinical samples and compared the worldwide emerged spike mutations from Variants of Concern/Interest, and developed an algorithm for monitoring the evolution of SARS-CoV-2 in the context of vaccines and monoclonal antibodies. The algorithm partitions logarithmic-transformed prevalence data monthly and Pearson’s correlation determines exponential emergence of amino acid substitutions (AAS) and lineages. The SARS-CoV-2 genome evaluation indicated 49 mutations, with 44 resulting in AAS. Nine of the ten most worldwide prevalent (>70%) spike protein changes have Pearson’s coefficient r > 0.9. The tenth, D614G, has a prevalence >99% and r-value of 0.67. The resulting algorithm is based on the patterns these ten substitutions elucidated. The strong positive correlation of the emerged spike protein changes and algorithmic predictive value can be harnessed in designing vaccines with relevant immunogenic epitopes. Monitoring, next-generation vaccine design, and mAb clinical efficacy must keep up with SARS-CoV-2 evolution, as the virus is predicted to remain endemic.

show abstract

Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 – 2021/03) Using a Statistical Learning Strategy

Cited by 6 publications

References 53 publications

Algorithm for the Quantitation of Variants of Concern for Rationally Designed Vaccines Based on the Isolation of SARS-CoV-2 Hawaiʻi Lineage B.1.243

Algorithm for the Quantitation of Variants of Concern for Rationally Designed Vaccines Based on the Isolation of SARS-CoV-2 Hawaiʻi Lineage B.1.243

Rapidly identifying new coronavirus mutations of potential concern in the Omicron variant using an unsupervised learning strategy

Dynamic SARS-CoV-2 emergence algorithm for rationally-designed logical next-generation vaccines

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