The mutational landscape of SARS-CoV-2 nucleocapsid protein

National Institute Of Biomedical Imaging And Bioengineering, Bethesda

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Abstract

An unprecedented worldwide collaboration led to the deposition of a large number of SARS-CoV-2 genome sequences in the GISAID database, which now contains 1-2 orders of magnitude more viral genomes than databases of any other virus. This genomic database has been instrumental for enhancing our understanding of viral evolution and geographic spread, such as identifying and predicting variants of concern. Less attention has been devoted to the fact that the database provides unique information on viral sequence variability and on seemingly inconsequential transient fluctuations of viral protein sequences that do not result in fixed mutations. Because the database consists of consensus sequences from viral samples taken from infected patients, all sequences represent alternate viral species, each of which is successfully replicating. Charting the scope of observed amino acid substitutions along the protein sequence we obtain a mutational landscape that we have shown can be interpreted in the context of biophysical protein properties. It constitutes a biophysical tool to elucidate protein structures, dynamics, and functions. Going beyond the analysis of the amino acids that can occupy each position in viral proteins, in the reporting period we have embarked on the analysis of cross-correlations between transient amino acid mutations. The goal is to identify coupling between residues within and among viral proteins, either due to structural contacts, protein-protein interactions, or functional dependencies. A problem in the analysis of the mutation statistics in the GISAID database is that many sequences are phylogenetically related and their mutations are not independent occurrences. To remove such bias we have developed a computational strategy that exploits sequence metadata to remove closely related sequences. We have also expanded the analysis of transient mutations from the previous focus on nucleocapsid (N) protein to all viral proteins. A grand challenge in evolution is the relationship between the sequence space and the biophysical phenotype. Previously we have explored this for the N protein with computational methods and through experiments with select N species carrying defining mutations of Delta and Omicron variants of concern. We observed that an unexpectedly large biophysical parameter space is inscribed by the observed sequence space, and that mutations in the disordered regions, in particular, can have significant non-local impact. This included the observation of a new protein-protein interface generated by the N:P13L mutation in the disordered N-terminal region. Going further in the review period, we have focused on the assembly of ribonucleoprotein particles (RNPs), which is one of the main and eponymous functions of the viral nucleocapsid protein. RNPs are critical for viral fitness as they scaffold and condense the viral genome for packaging into the virion. We have previously developed a coarse-grained model for the architecture of RNPs based on the known protein-protein and protein-nucleic acid interfaces. Using a combination of sedimentation velocity and mass photometry experiments, we have developed an approach to measure the stability of RNPs. This allowed us to ask how defining N mutations of different variants of concern impact RNP stability, combining biophysical tools with a virus-like particle assay, and reverse genetics experiments. We find convergent evolution in independent introduction of amino acid substitutions strengthening existing RNP binding interfaces. In particular, N:P13L of Omicron variants creates a self-association interface de novo that enhances RNP assembly and increases viral fitness. We observe the formation of polydisperse, largely disordered N-RNA clusters with distributed weak binding interfaces, which we hypothesize optimizes reversible RNA condensation while allowing for a large sequence space to be explored to support host adaptation and evolution. Publications: This work is currently under peer review for publication in eLife and is freely available as a preprint in bioRxiv (10.1101/2025.04.26.650775).