Since the S protein of 2019-nCoV shares closest ancestry with SARS GZ02, the sequence coding for spike proteins of these two viruses were compared using MultiAlin software. We found four new insertions in the protein of 2019-nCoV- “GTNGTKR” (IS1), “HKNNKS” (IS2), “GDSSSG” (IS3) and “QTNSPRRA” (IS4) (Figure 2). To our surprise, these sequence insertions were not only absent in S protein of SARS but were also not observed in any other member of the Coronaviridae family (Supplementary figure). This is startling as it is quite unlikely for a virus to have acquired such unique insertions naturally in a short duration of time.
The insertions were observed to be present in all the genomic sequences of 2019-nCoV virus available from the recent clinical isolates. To know the source of these insertions in 2019-nCoV a local alignment was done with BLASTp using these insertions as query with all virus genome. Unexpectedly, all the insertions got aligned with Human immunodeficiency Virus-1 (HIV-1). Further analysis revealed that aligned sequences of HIV-1 with 2019-nCoV were derived from surface glycoprotein gp120 (amino acid sequence positions: 404-409, 462-467, 136-150) and from Gag protein (366-384 amino acid) (Table 1). Gag protein of HIV is involved in host membrane binding, packaging of the virus and for the formation of virus-like particles. Gp120 plays crucial role in recognizing the host cell by binding to the primary receptor CD4.This binding induces structural rearrangements in GP120, creating a high affinity binding site for a chemokine co-receptor like CXCR4 and/or CCR5.
https://www.biorxiv.org/content/10.1101 ... 0.927871v1
A coronavirus that originated in Wuhan, China, has killed 18 people and infected more than 630.
The virus has been reported in at least eight other countries, including the US, where a man in Washington who recently visited China was confirmed to have the illness.
A scientist at Johns Hopkins last year modelled what would happen if a deadly coronavirus reached a pandemic scale. His simulated scenario predicted that 65 million people could die within 18 months.
https://www.businessinsider.com/scienti ... ths-2020-1