Computational study of the furin cleavage domain of SARS-CoV-2: delta binds strongest of extant variants

Abstract

We demonstrate that AlphaFold and AlphaFold Multimer, implemented within the ColabFold suite, can accurately predict the structures of the furin enzyme with known six residue inhibitory peptides. Noting the similarity of the peptide inhibitors to polybasic furin cleavage domain insertion region of the SARS-CoV-2, which begins at P681, we implement this approach to study the wild type furin cleavage domain for the virus and several mutants. We introduce mutations in silico for alpha, omicron, and delta variants, for several sequences which have been rarely observed, for sequences which have not yet been observed, for other coronaviruses (NL63, OC43, HUK1a, HUK1b, MERS, and 229E), and for the H5N1 flu. We show that interfacial hydrogen bonds between the furin cleavage domain and furin are a good measure of binding strength that correlate well with endpoint binding free energy estimates, and conclude that among all candidate viral sequences studied, delta is near the very top binding strength within statistical accuracy. However, the binding strength of several rare sequences match delta within statistical accuracy. We find that the furin S1 pocket is optimized for binding arginine as opposed to lysine. This residue, typically at sequence position five, contains the most hydrogen bonds to the furin, and hydrogen bond count for just this residue shows a strong positive correlation with the overall hydrogen bond count . We demonstrate that the root mean square backbone C-alpha fluctuation of the first residue in the furin cleavage domain has a strong negative correlation with the interfacial hydrogen bond count. We show by considering the variation with the number of basic residues that the maximum mean number of interfacial hydrogen bonds expected is 15.7 at 4 basic residues.

In silico analysis of mutations near S1/S2 cleavage site in SARS‐CoV‐2 spike protein reveals increased propensity of glycosylation in Omicron strain

“…In looking at the average number of interactions for the P681 mutations, the P681R mutation was found to significantly increase the number of interacting residues than the Wild‐type, Alpha, or Omicron S1/S2 sites ( p = 6.89 × 10 −7 ) (Figure 3F,I). These results reflect computational and experimental reports that state that the P681H and P681R mutations increase affinity to and cleavage capacity by furin 34–37 …”

“…These results reflect computational and experimental reports that state that the P681H and P681R mutations increase affinity to and cleavage capacity by furin. 34 , 35 , 36 , 37 …”

“…These results reflect computational and experimental reports that state that the P681H and P681R mutations increase affinity to and cleavage capacity by furin. [34][35][36][37] No significant differences were found between the predicted scores and number of residue 681-interacting residues for ensemble docking of the Wild-type, Alpha, Delta, and Omicron S1/S2 sites to TMPRSS2 (Supporting Information: Table 2). Although the affinities did not significantly differ when comparing furin and TMPRSS2 dockings to one another, less poses were reported for docking of the all spike variant S1/S2 site conformers to TMPRSS2, which was interpreted as having less structural complementary and, thus, affinity to the S1/S2 cleavage sites.…”

Using machine learning to detect coronaviruses potentially infectious to humans

“…In previous studies, we have shown that the average number of H-Bonds correlates well with binding energy 67 . Therefore, for each MD simulation, we recorded the number of H-bonds formed between protein-protein interactions (including ionic bonds) at each sampled conformation (snapshots).…”

SARS-CoV-2 omicron spike simulations: broad antibody escape, weakened ACE2 binding, and modest furin cleavage

“…The delta and omicron structures were then obtained by mutation from the predicted WT FCD-Furin structure. In a separate work, we present a complete description of the use of ColabFold/AlphaFold for modeling the FCD-Furin binding as well as simulation results of over 60 observed FCD sequences for SARS-CoV-2 and other commonly observed coronaviruses ( 32 ). In this study, we limit our FCD-Furin binding focus to sequences from WT, delta, and omicron variants.…”