Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Semi-Covariance Coefficient Analysis of Spike Proteins from SARS-CoV-2 and other Coronaviruses for Viral Evolution and Characteristics Associated with Fatality

Version 1 : Received: 28 February 2021 / Approved: 2 March 2021 / Online: 2 March 2021 (09:30:12 CET)

How to cite: Huang, J.; Spencer, R.; Zhang, W. Semi-Covariance Coefficient Analysis of Spike Proteins from SARS-CoV-2 and other Coronaviruses for Viral Evolution and Characteristics Associated with Fatality. Preprints 2021, 2021030055 (doi: 10.20944/preprints202103.0055.v1). Huang, J.; Spencer, R.; Zhang, W. Semi-Covariance Coefficient Analysis of Spike Proteins from SARS-CoV-2 and other Coronaviruses for Viral Evolution and Characteristics Associated with Fatality. Preprints 2021, 2021030055 (doi: 10.20944/preprints202103.0055.v1).

Abstract

Complex modeling has received significant attention in recent years and is increasingly used to explain the statistical phenomenon with increasing and decreasing fluctuations such as the similarity or difference of spike protein charge patterns of coronaviruses. Different from the existing covariance or correlation coefficient methods in traditional integer dimension construction, this study proposes a simplified novel fractional dimension derivation with the exact Excel tool algorithm. It involves the fractional center moment extension to covariance, which ends up a complex covariance coefficient that is better than the Pearson correlation coefficient, in the sense that the nonlinearity relationship can be further depicted. The spike protein sequences of coronaviruses were obtained from the GenBank and GISAID database, including the coronaviruses from pangolin, bat, canine, swine (three variants), feline, tiger, SARS-CoV-1, MERS, and SARS-CoV-2 (including the strains of Wuhan, Beijing, New York, German, and UK variant B.1.1.7) were used as the representative examples in this study. By examining the values above and below the average/mean based on the positive and negative charge patterns of the amino acid residues of the spike proteins from coronaviruses, the proposed algorithm provides deep insights into the nonlinear evolving trends of spike proteins for understanding the viral evolution and identifying the protein characteristics associated with viral fatality. The calculation results demonstrate that the complex covariance coefficient analyzed by this algorithm is capable of distinguishing the subtle nonlinear differences in the spike protein charge patterns with reference to Wuhan strain SARS-CoV-2 for which the Pearson correlation coefficient may overlook. Our analysis reveals the unique convergent (positive correlative) to divergent (negative correlative) domain center positions of each virus. The convergent or conserved region may be critical to the viral stability or viability; while the divergent region is highly variable between coronaviruses suggesting high frequency of mutations in this region. The analyses show that the conserved center region of SARS-CoV-1 spike protein is located at amino acid residues 900, but shifted to the amino acid residues 700 in MERS spike protein, and then to amino acid residues 600 in SARS-COV-2 spike protein, indicating the evolvement of the coronaviruses. Interestingly, the conserved center region of the spike protein in SARS-COV-2 variant B.1.1.7 shifted back to amino acid residues 700, suggesting this variant is more virulent than the original SARS-COV-2 strain. Another important characteristic our study reveals is that the distance between the divergent mean and the maximal divergent point in each of the viruses (MERS>SARS-CoV-1>SARS-CoV-2) is proportional to viral fatality rate. This algorithm may help to understand and analyze the evolving trends and critical characteristics of SARS-COV-2 variants, other coronaviral proteins and viruses.

Subject Areas

Fractional complex moment; SARS-CoV-2; coronaviruses; spike protein sequence; Pearson correlation coefficient; semi-covariance coefficient; positive-correlative and negative-correlative domains

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