What is the difference between a nucleocapsid and a capsid

Irrespective of its physiological role, our results suggest that phase separation provides a macroscopic readout visible droplets of a nanoscopic process protein:RNA and protein:protein interaction. In the context of SARS-CoV-2, those interactions are expected to be key for viral packaging, such that assays that monitor phase separation of N protein with RNA may offer a convenient route to identify compounds that will also attenuate viral assembly.

Coronavirus nucleocapsid proteins are multi-domain RNA-binding proteins that play a critical role in many aspects of the viral life cycle 12 , Work on N protein from a range of model coronaviruses has shown that N protein undergoes both self-association, interaction with other proteins, and interaction with RNA, all in a highly multivalent manner. Notably, all five domains are predicted to bind RNA 29 , 30 , 31 , 32 , 33 , 34 , 35 , and while the dimerization domain facilitates the formation of well-defined stoichiometric dimers, RNA-independent higher-order oligomerization is also expected to occur 34 , 36 , 37 , Importantly, protein—protein and protein—RNA interaction sites have been mapped to all three disordered regions.

Dye positions used in this study are annotated across the top, disorder prediction calculated across the bottom.

The specific positions were selected such that fluorophores are sufficiently close to be in the dynamic range of FRET measurements. Labeling was achieved using cysteine mutations and thiol-maleimide chemistry. Center and left: colored based on surface potential calculated with the Adaptive Poisson Boltzmann Method , revealing the highly basic surface of the RBD.

Right: ribbon structure with N- and C-termini highlighted. Center and left: colored based on surface potential, revealing the highly basic surface. Despite recent structures of the RBD Fig. Understanding N protein function necessitates a mechanistic understanding of the flexible predicted disordered regions and their interplay with the folded domains. A recent small-angle X-ray study shows good agreement with previous work on SARS, suggesting the LINK is relatively extended, but neither the structural basis for this extension nor the underlying dynamics are known 29 , We also investigated the stability of the RBD and truncated variants of the protein to test the role of long range interactions on the disordered regions see SI and Table S2.

In parallel to the experiments, we performed all-atom Monte Carlo simulations of each of the three IDRs in isolation and in context with their adjacent folded domains.

We started our analysis by investigating the NTD conformations. Under native conditions, single-molecule FRET measurements revealed the occurrence of a single population with a mean transfer efficiency of 0.

To assess whether this transfer efficiency reports on a rigid distance e. Under native conditions, the donor and acceptor lifetimes for the NTD construct lie on the line that represents fast conformational dynamics Fig. To properly quantify the timescale associated with these fast structural rearrangements, we leveraged nanosecond FCS. As expected for a dynamic population 43 , 44 , the cross-correlation of acceptor—donor photons for the NTD is anticorrelated Figs. This is longer than reconfiguration times observed for other proteins with a similar persistence length and charge content 44 , 45 , 46 , 47 , hinting at a large contribution from internal friction due to rapid intramolecular contacts formed either within the NTD or with the RBD or transient formation of short structural motifs A conversion from transfer efficiency to chain dimensions can be obtained by assuming the distribution of distances computed from polymer models.

This corresponds to values of persistence length see SI equal to 4. Overall, these results confirm the NTD is disordered, as predicted by sequence analysis. The observed anticorrelated rise is the characteristic signature of FRET dynamics and the timescale associated is directly related to the reconfiguration time of the probed segment.

C Root-mean-square interdye distance as extracted from single-molecule FRET experiments across different denaturant concentrations using a Gaussian chain distribution, examining residues 1—68 in the context of the full-length protein. The full line represents a fit to the model in Eq. The dashed line represents the estimated fraction of folded RBD across different denaturant concentrations based on Eq.

The peaks on the left shoulder of the histogram are due to persistent NTD—RBD interactions in a small subset of simulations. E Normalized distance maps scaling maps quantify heterogeneous interaction between every pair of residues in terms of average inter-residue distance normalized by distance expected for the same system if the IDR had no attractive interactions the excluded volume limit Error bars are standard error of the mean calculated from forty independent simulations.

G Projection of normalized distances onto the folded domain reveals repulsion is through electrostatic interaction positively charged NTD is repelled by the positive face of the RBD, which is proposed to engage in RNA binding while attractive interactions are between positive, aromatic, and polar residues in the NTD and a slightly negative and hydrophobic surface on the RBD see Fig.

H The C-terminal half of the transient helix H2 encodes an arginine-rich surface. We next examined the interaction of the NTD with other domains in the protein.

S8 and Table S2. We then assessed the role of the folded RBD and its influence on the conformations of the NTD by studying the effect of a chemical denaturant on the protein.

S6 and S8. We interpret these two populations as the contribution of the folding and unfolding fraction of the RBD domain on the distances probed by the NTD-FL construct, which includes a labeling position within the folded RBD. A fit of the interdye root-mean-square distances to this model and the inferred stability of the RBD domain midpoint: 1.

A comparative fit of the histograms assuming two overlapping populations yields a consistent result in terms of RBD stability and protein conformations Fig. Though the denaturation of the RBD reveals coexistence of up to three populations, which we identify as an unfolded, an intermediate, and a folded state, the range of the folding transition is compatible with the estimates made using the NTD constructs midpoint: 1.

S9 and Table S6. We also performed simulations of the NTD in isolation. We observed good agreement between simulation and experiment for the equivalent inter-residue distance Fig.

The peaks on the left side of the histogram reflect specific simulations where the NTD engages more extensively with the RBD through a fuzzy interaction, leading to local kinetic traps We also identified several regions in the NTD where transient helices form, and using normalized distance maps found regions of transient attractive and repulsive interaction between the NTD and the RBD Fig.

In particular, the basic beta-strand extension from the RBD Fig. Finally, we noticed the arginine-rich C-terminal residues residues 31—38 form a transient alpha helix projecting three of the four arginines in the same direction Figs. We next turned to the linker LINK FL construct to investigate how the disordered region modulates the interaction and dynamics between the two folded domains.

Under aqueous buffer conditions, single-molecule FRET reveals the coexistence of two overlapping populations with mean transfer efficiencies of 0. A small change in ionic strength of the solution is sufficient to alter the equilibrium between these two populations and favor the low transfer efficiency state see inset in Fig.

Comparison of the fluorescence lifetimes and transfer efficiencies indicates that, like the NTD, the transfer efficiencies represent dynamic conformational ensembles sampled by the LINK Fig. This reconfiguration time is compatible with high internal friction effects, as observed for other unstructured proteins 44 , 45 , but may also account for the drag of the surrounding domains.

A Histogram of the transfer efficiency distribution measured across the labeling positions and in the context of the full-length protein, under aqueous buffer conditions.

C Interdye distance as extracted from single-molecule FRET experiments across different denaturant concentrations. S6 , which accounts for denaturant binding. The inset provides an estimate of the fraction of each population in the low GdmCl concentration regime. D Inter-residue distance distributions calculated from simulations histogram show good agreement with distances inferred from single-molecule FRET measurements green bar.

F Two transient helices are observed in the linker residues — and — The N-terminal helix H3 overlaps with part of the SR region and orientates three arginine residues in the same direction, analogous to behavior observed for H2 in the NTD. Error bars are standard errors of the mean calculated from 30 independent simulations. Next, we addressed whether the LINK segment populates elements of persistent secondary structure or forms stable interaction with the RBD or dimerization domains.

The addition of denaturant leads to the rapid loss of the high transfer efficiency population and a continuous shift of the remaining population toward lower transfer efficiencies Figs. These results correspond to an almost linear expansion of the chain in response to denaturant see Fig. Interestingly the transfer efficiency measured in aqueous buffer is equivalent to the one reported by the high transfer efficiency population of the LINK FL construct.

The electrostatic nature of this compaction is clearly revealed by titrating a polar non-ionic denaturant urea and observing that the chain remains largely compact and recovers the same dimensions measured in GdmCl only when adding salt to the solution Fig. As with the NTD, all-atom Monte Carlo simulations provide atomistic insight that can be compared with our spectroscopic results. In addition, we also performed simulations of the LINK in isolation. We again found good agreement between simulations and experiment Fig.

The root-mean-square inter-residue distance for the low transfer efficiency population between simulated positions and is We tentatively suggest this may reflect sequence properties chosen to prevent aberrant interactions between the LINK and the two folded domains. These two helices encompass a serine—arginine SR rich region known to mediate both protein—protein and protein—RNA interaction. Helix H3 formation leads to the alignment of three arginine residues along one face of the helix.

Single-molecule FRET experiments again reveal a single population with a mean transfer efficiency of 0. Interestingly, when studying the denaturant dependence of the protein, we noticed that the width of the distribution increases while moving toward aqueous buffer conditions. This suggests that the protein may form transient contacts or adopt local structure.

Comparison with a truncated variant that contains only the CTD Fig. S8 reveals a very similar distribution, with almost identical mean transfer efficiency but a narrower width Fig. S6 , suggesting that part of the broadening is due to interactions with the neighboring domains. The flat correlation indicates a lack of dynamics in the studied timescale or the coexistence of two populations in equilibrium whose correlations one correlated and the other anticorrelated compensate each other.

D Inter-residue distance distributions calculated from simulations histogram show good agreement with distances inferred from single-molecule FRET measurements purple bar. E Scaling maps describe the average inter-residue distance between each pair of residues, normalized by the distance expected if the CTD behaved as a self-avoiding random coil. H6 engages in extensive intra-CTD interactions and also interacts with the dimerization domain.

We observe repulsion between the dimerization domain and the N-terminal region of the CTD. Both show a reduction in population in the presence of the dimerization domain at least in part because the same sets of residues engage in transient interactions with the dimerization domain. G The normalized distances are projected onto the surface to map CTD-dimerization interaction. The helical region drives intramolecular interaction, predominantly with the N-terminal side of the dimerization domain.

To further investigate putative interaction between the CTD and neighboring domains, we turned to the investigation of protein dynamics. Though the comparison of the fluorophore lifetimes against transfer efficiency Fig. However, inspection of the donor—donor and acceptor—acceptor autocorrelations reveal a correlated decay. This is different from that expected for a completely static system such as polyprolines 60 , where the donor—donor and acceptor—acceptor autocorrelation are also flat.

An increase in the autocorrelations can be observed for static quenching of the dyes with aromatic residues. Interestingly, donor dye quenching can also contribute to a positive amplitude in the donor—acceptor correlation 61 , Therefore, a plausible interpretation of the flat cross-correlation data is that we are observing two populations in equilibrium whose correlations one anticorrelated, reflecting conformational dynamics, and one correlated, reflecting quenching due contact formation compensate each other.

To further investigate the possibility of two coexisting populations, we performed ns-FCS at increasing GdmCl concentrations. These experiments revealed a progressive increase of the anticorrelated amplitude in the cross-correlation, consistent with an increase of the dynamic population. Moreover, we also observed a simultaneous decrease in the overall donor—donor autocorrelation amplitude, consistent with a decrease in the quenched population Fig. Taken together, these results support our hypothesis that there are at least two distinct species existing in equilibrium.

By analyzing the dynamic species between 0. However, some caution should be used when interpreting these numbers since we know there is some contribution from fluorophore static quenching, which may in turn contribute to an underestimate of the effective transfer efficiency We again obtained good agreement between all-atom Monte Carlo simulations and experiments Fig.

Scaling maps reveal extensive intramolecular interaction by the residues that make up H6, both in terms of local intra-IDR interactions and interaction with the dimerization domain Fig. The difference reflects the fact that several of the helix-forming residues interact with the dimerization domain, leading to a competition between helix formation and intramolecular interaction.

Mapping normalized distances onto the folded structure reveals that interactions occur primarily with the N-terminal portion of the dimerization domain Fig. The cluster of hydrophobic residues in H6 engage in intramolecular contacts and offer a likely physical explanation for the complex ns-FCS data in aqueous buffer.

Over the last decade, biomolecular condensates formed through phase separation have emerged as a new mode of cellular organization 64 , 65 , 66 , Many of the proteins that have been shown to drive phase separation in vitro are RNA-binding proteins with intrinsically disordered regions 64 , Moreover, multivalency is the key molecular feature that determines if a biomolecule can undergo higher-order assembly Having characterized N protein to reveal three IDRs with distinct binding sites for both protein—protein and protein—RNA interactions it became clear that N protein possesses all of the features consistent with a protein that may undergo phase separation.

With these results in hand, we anticipated that N protein would undergo phase separation with RNA 70 , 71 , In line with this expectation, we observed robust droplet formation with homopolymeric RNA Fig. Turbidity assays at different concentrations of protein and poly rU — nucleotides demonstrate the classical re-entrant phase behavior expected for a system undergoing heterotypic interaction Fig.

It is to be noted that turbidity experiments do not exhaustively cover all the conditions for phase separation and are only indicative of the low-boundary concentration regime explored in the current experiments. In particular, turbidity experiments do not provide a measurement of tie-lines, though they are inherently a reflection of the free energy and chemical potential of the solution mixture Though increasing salt concentration results in an upshift of the phase boundaries, one has to consider that in a cellular environment this effect might be counteracted by cellular crowding.

A , B Appearance of solution turbidity upon mixing was monitored to determine the concentration regime in which N protein and poly rU undergo phase separation. Solid lines are simulations of an empirical equation fitted individually to each titration curve see SI.

An inset is provided for the titration at 3. C , D Projection of phase boundaries for poly rU and N protein mixtures highlights a re-entrant behavior, as expected for phase separations induced by heterotypic interactions. Turbidity contour lines are computed from a global fit of all titration curves see SI.

Insets: confocal fluorescence images of droplets doped with fluorescently labeled N protein. At a higher salt concentration, a lower concentration of protein in the droplet is detected. One peculiar characteristic of our measured phase diagram is the narrow regime of conditions in which we observe phase separation of nonspecific RNA at a fixed concentration of protein.

This leads us to hypothesize that the protein may have evolved to maintain tight control of concentrations at which phase separation can or cannot occur. These ratios are in line with the charge neutralization criterion proposed by Banerjee et al. Finally, given we observed phase separation with poly rU , the behavior we are observing is likely driven by relatively nonspecific protein:RNA interactions. In agreement, work from a number of other groups has also established this phenomenon across a range of solution conditions and RNA types 20 , 21 , 22 , 23 , 24 , 25 , 26 , Having established phase separation through a number of assays, we wondered what—if any—physiological relevance this may have for the normal biology of SARS-CoV One possible model is that large, micron-sized cytoplasmic condensates of N protein and RNA form through phase separation and facilitate genome packaging.

These condensates may act as molecular factories that help concentrate the components for pre-capsid assembly where we define a pre-capsid here simply as a species that contains a single copy of the genome with multiple copies of the associated N protein , a model that has been proposed in other viruses However, given that phase separation is unavoidable when high concentrations of multivalent species are combined, we propose that an alternative interpretation of our data is that in this context, phase separation is simply an inevitable epiphenomenon that reflects the inherent multivalency of the N protein for itself and for RNA.

This poses questions about the origin of specificity for viral genomic RNA gRNA , and, of focus in our study, how phase separation might relate to a single-genome packaging through RNA compaction.

One possible way to limit phase separation between two components e. While possible, such a regulatory mechanism is at the mercy of extrinsic factors that may substantially modulate the saturation concentration 76 , 77 , Furthermore, not only must phase separation be prevented, but gRNA compaction should also be promoted through the binding of N protein. In this scenario, the affinity between gRNA and N protein plays a central role in determining the required concentration for condensation of the macromolecule gRNA by the ligand N protein.

Given a system composed of components with defined valencies, phase boundaries are encoded by the strength of interaction between the interacting domains in the components. Considering a long polymer e. With this in mind, we hypothesized that phase separation is reporting on the physical interactions that underlie genome compaction. To explore this hypothesis, we developed a simple computational model where the interplay between compaction and phase separation could be explored. Our setup consists of two types of species: long multivalent polymers and short multivalent binders Fig.

All interactions are isotropic, and each bead is inherently multivalent as a result. In the simplest instantiation of this model, favorable polymer:binder and binder:binder interactions are encoded, mimicking the scenario in which a binder e.

As expected for simulations of binders with homopolymer polymers we observed phase separation in a concentration-dependent manner Fig. Phase separation gives rise to a single large spherical cluster with multiple polymers and binders Fig. A Summary of our model setup, which involves long polymers 61 beads per molecules or short binders 2 beads per molecules. Each bead is multivalent and can interact with every adjacent lattice site.

The interaction matrix to the right defines the pairwise interaction energies associated with each of the bead types. B Concentration-dependent assembly behavior for polymers lacking a high-affinity binding site.

Schematic showing polymer architecture brown with binder blue. C Phase diagram showing the concentration-dependent phase regime—dashed line represents the binodal phase boundary and is provided to guide the eye.

D Analysis in the same 2D space as panel C , assessing the number of droplets at a given concentration. When phase separation occurs, a single droplet appears in almost all cases. E Concentration-dependent assembly behavior for polymers with a high-affinity binding site red bead. F No large droplets are formed in any of the systems, although multiple polymer:binder complexes form.

G The number of clusters observed matches the number of polymers in the system—i. H Simulation snapshots from equivalent simulations for polymers with top or without bottom a single high-affinity binding site. I Polymer dimensions in the dense and dilute phase for the parameters in our model for polymers with no high-affinity binding site.

Note that compaction in the dense phase reflects finite-size effects, as addressed in panel K , and is an artifact of the relatively small droplets formed in our systems relative to the size of the polymer. The droplets act as a bounding cage for the polymer, driving their compaction indirectly. J Polymer dimensions across the same concentration space for polymers with a single high-affinity binding site.

Across all concentrations, each individual polymer is highly compact. K Compaction in the dense phase panel I is due to small droplets. When droplets are sufficiently large, we observe chain expansion, as expected from standard theoretical descriptions.

Under these conditions phase separation is suppressed. Equivalent simulations for polymers with a high-affinity site reveal these chains are no longer compact.

As such, phase separation offers a readout that—in our model—maps to single-polymer compaction. Given our homopolymers undergo robust phase separation, we wondered if a break in the symmetry between intra- and intermolecular interactions would be enough to promote single-polymer condensation in the same concentration regime over which we had previously observed phase separation.

Symmetry breaking in our model is achieved through a single high-affinity-binding site Fig. We choose this particular mode of symmetry breaking to mimic the presence of a packaging signal—a region of the genome that is essential for efficient viral packaging—an established feature in many viruses including coronaviruses although we emphasize this is a general model, as opposed to trying to directly model gRNA with a packaging signal 83 , 84 , We performed identical simulations to those in Fig.

Under these conditions we did not observe large phase separated droplets Fig. Instead, each individual polymer undergoes collapse to form a single-polymer condensate Fig. Collapse is driven by the recruitment of binders to the high-affinity site, where they coat the chain, forming a local cluster of binders on the polymer. This cluster is then able to interact with the remaining regions of the polymer through weak nonspecific interactions, the same interactions that drove phase separation in Fig.

Symmetry breaking is achieved because the local concentration of binder around the site is high, such that intramolecular interactions are favored over intermolecular interaction. This high local concentration also drives compaction at low binder concentrations.

As a result, instead of a single multi-polymer condensate, we observe multiple single-polymers condensates, where the absolute number matches the number of polymers in the system Fig. The high-affinity-binding site polarizes the single-polymer condensate, such that they are organized, recalcitrant to fusion, and kinetically metastable.

To illustrate this metastable nature, extended simulations using an approximate kinetic Monte Carlo scheme demonstrated that a high-affinity-binding site dramatically slows assembly of multichain assemblies, but that ultimately these are the thermodynamically optimal configuration Fig. A convenient physical analogy is that of a micelle, which are non-stoichiometric stable assemblies.

Even for micelles that are far from their optimal size, fusion is slow because it requires substantial molecular reorganization and the breaking of stable interactions 86 , Finally, we ran simulations under conditions in which binder:polymer interactions were reduced, mimicking the scenario in which nonspecific protein:RNA interactions are inhibited Fig. Under these conditions no phase separation occurs for polymers that lack a high-affinity-binding site, while for polymers with a high-affinity-binding site no chain compaction occurs in contrast to when binder:polymer interactions are present, see Fig.

This result illustrates how phase separation offers a convenient readout for molecular interactions that might otherwise be challenging to measure. We emphasize that our conclusions from these coarse-grained simulations are subject to the parameters in our model. We present these results to demonstrate an example of how this single-genome packaging could be achieved, offering a class of mechanism that may be in play.

This is in contrast to the much stronger statement that this is how it is achieved, a statement that would require much more evidence to make. Recent elegant work by Ranganathan and Shakhnovich 88 identified kinetically arrested microclusters, where slow kinetics result from the saturation of stickers within those clusters. This is completely analogous to our results albeit with homotypic interactions, rather than heterotypic interactions , giving us confidence that the physical principles uncovered are robust and, we tentatively suggest, quite general.

Future simulations are required to systematically explore the details of the relevant parameter space in our system. However, regardless of those parameters, our model does establish that if weak multivalent interactions underlie the formation of large multi-polymer droplets, those same interactions cannot also drive polymer compaction inside the droplet. To better understand how the various folded and disordered domains interact with one another, we applied single-molecule spectroscopy and all-atom simulations to perform a detailed biophysical dissection of the protein, uncovering several putative interaction motifs.

Furthermore, based on both sequence analysis and our single-molecule experiments, we anticipated that N protein would undergo phase separation with RNA.

In agreement with this prediction, and in line with work from the Gladfelter and Yildiz groups working independently from us, we find that N protein robustly undergoes phase separation in vitro with model RNA under a range of different salt conditions. Using simple polymer models, we propose that the same interactions that drive phase separation may also drive genome packaging into a dynamic, single-genome condensate.

The formation of single-genome condensates as opposed to multi-genome droplets is influenced by the presence of one or more symmetry-breaking interaction sites, which we tentatively suggest could reflect packaging signals in viral genomes.

Our single-molecule experiments and all-atom simulations are in good agreement with one another and reveal that all three IDRs are extended and, depending on solution condition, highly dynamic. Finally, we see a pronounced interaction between the CTD and the dimerization domain, although these interactions are still highly transient.

Single-molecule experiments and all-atom simulations were performed on monomeric versions of the protein, yet N protein has previously been shown to undergo dimerization and form higher-order oligomers in the absence of RNA S14 and SI.

These experiments and the comparison between full-length and truncated variants suggest that in the concentration regime used for single-molecule experiments the protein exists as a monomer. We identified a number of transient helical motifs that provide structural insight into previously characterized molecular interactions. Transient helices are ubiquitous in viral disordered regions and have been shown to underlie molecular interactions in a range of systems 75 , 89 , 90 , While the application of molecular simulations to identify transient helices in disordered regions can suffer from forcefield inaccuracies, it is worth noting that in prior work we have found good agreement between experimental and simulated secondary structure analysis across a range of systems explored in an analogous manner 70 , 92 , 93 , The serine—arginine SR region which includes H3 has been previously identified as engaging in interaction with a structured acidic helix in Nsp3 in the model coronavirus MHV, consistent with an electrostatic helical interaction 97 , Recent NMR data also show excellent agreement with our results, identifying a transient helix that shows overlap with H3 The SR region is necessary for recruitment to replication-transcription centers in MHV, and also undergoes phosphorylation, setting the stage for a complex regulatory system awaiting exploration 99 , S19 Jack et al.

While transient helix H5 is weakly populated, the positive charge associated with this region may make it critical for protein:RNA interaction, a result strongly supported by the observation that deletion of this region ablates protein:RNA phase separation Transient helix H6 is an amphipathic helix with a highly hydrophobic face Fig. Recent hydrogen—deuterium exchange mass spectrometry also identified H6 Residues in this region have previously been identified as mediating M protein binding in other coronaviruses, such that we propose H6 underlies that interaction 21 , , , Recent work has also identified amphipathic transient helices in disordered proteins as interacting directly with membranes, such that an additional albeit entirely speculative role could involve direct membrane interaction, as has been observed in other viral phosphoproteins , It is for this reason that an icosahedron is known to have 2—3—5 symmetry, because it has twofold, threefold, and fivefold axes of symmetry.

This terminology is useful when dealing with an icosahedral virus because it can be used to indicate specific locations on the virus or where the virion has interactions with the cell surface.

For instance, if a virus interacts with a cell surface receptor at the threefold axis, then you know this interaction occurs at one of the faces of the icosahedron. A protein protruding from the capsid at the fivefold axis will be found at one of the vertices tips of the icosahedron. All of the illustrations of viruses in Fig. How many twofold axes of symmetry are found in one icosahedron? How about the number of threefold or fivefold axes?

How many faces, edges, and vertices are found in an icosahedron? A Icosahedron faces fuchsia triangles , edges red rectangles , and vertices violet pentagons are indicated on the white icosahedron. B The twofold axis of symmetry occurs when the axis is placed through the center of an edge. The threefold axis occurs when the axis is placed in the center of a face C , and the fivefold axis passes through a vertex of the icosahedron D.

Viral proteins form each face small triangle of the icosahedral capsid. Viral proteins are not triangular, however, and so one protein subunit alone is not sufficient to form the entire face. Therefore, a face is formed from at least three viral protein subunits fitted together Fig. These can all be the same protein, or they can be three different proteins. The subunits together form what is called the structural unit. The structural unit repeats to form the capsid of the virion. A Virus capsids are composed of viral protein subunits that form structural units.

The triangulation number T indicates the number of structural units per face of the icosahedron. The red lines outline a triangular face of the icosahedron, while the purple pentagons indicate the vertices fivefold axes of the icosahedron. But how can some viruses form very large icosahedral capsids? The answer is repetition. The structural unit can be repeated over and over again to form a larger icosahedron side.

The number of structural units that creates each side is called the triangulation number T , because the structural units form the triangle face of the icosahedron. The geometry and math involved with icosahedral capsid structure can be complex, and only the very basics are described here. In any case, by increasing the number of identical structural units on each face, the icosahedron can become progressively larger without requiring additional novel proteins to be produced.

Some viruses have triangulation numbers over 25, even! The proteins that compose the structural unit may form three dimensional structures known as capsomeres that are visible in an electron micrograph.

In icosahedral viruses, capsomeres generally take the form of pentons containing five units or hexons containing six units that form a visible pattern on the surface of the icosahedron See Fig. Capsomeres are morphological units that arise from the interaction of the proteins within the repeated structural units. Why does the icosahedral virus structure appear so often? Research has shown that proteins forming icosahedral symmetry require lesser amounts of energy, compared to other structures, and so this structure is evolutionarily favored.

Many viruses that infect animals are icosahedral, including human papillomavirus, rhinovirus, hepatitis B virus, and herpesviruses Fig. Like their helical counterparts, icosahedral viruses can be naked or enveloped, as well.

Poliovirus A , rotavirus B , varicella—zoster virus C , the virus that causes chickenpox and shingles, and reovirus D. Note that C is enveloped. The majority of viruses can be categorized as having helical or icosahedral structure. A few viruses, however, have a complex architecture that does not strictly conform to a simple helical or icosahedral shape. Poxviruses, geminiviruses, and many bacteriophages are examples of viruses with complex structure Fig.

Poxviruses, including the viruses that cause smallpox or cowpox, are large oval or brick-shaped particles — nm long. The geminiviruses also exhibit complex structure. As their name suggests, these plant-infecting viruses are composed of two icosahedral heads joined together. Bacteriophages , also known as bacterial viruses or prokaryotic viruses , are viruses that infect and replicate within bacteria.

Many bacteriophages also have complex structure, such as bacteriophage P2, which has an icosahedral head, containing the nucleic acid, attached to a cylindrical tail sheath that facilitates binding of the bacteriophage to the bacterial cell. Vaccinia virus A , a virus belonging to the poxvirus family, has a complex capsid architecture with a dumbbell-shaped core. Geminiviruses B have a double-icosahedron capsid.

Bacteriophages, such as P2 C , often have complex capsid structure. The classification of viruses is useful for many reasons. It allows scientists to contrast viruses and to reveal information on newly discovered viruses by comparing them to similar viruses.

It also allows scientists to study the origin of viruses and how they have evolved over time. The classification of viruses is not simple, however—there are currently over different viral species with very different properties!

One classification scheme was developed in the s by Nobel laureate David Baltimore. The Baltimore classification system categorizes viruses based on the type of nucleic acid genome and replication strategy of the virus.

As will be further discussed in the next chapter, positive-strand also positive-sense or plus-strand RNA is able to be immediately translated into proteins; as such, messenger RNA mRNA in the cell is positive strand.

Negative-strand also negative-sense or minus-strand RNA is not translatable into proteins; it first has to be transcribed into positive-strand RNA. Baltimore also took into account viruses that are able to reverse transcribe , or create DNA from an RNA template, which is something that cells are not capable of doing.

Together, the seven classes are. There are a variety of ways by which viruses could be classified, however, including virion size, capsid structure, type of nucleic acid, physical properties, host species, or disease caused. Because of this formidable challenge, the International Committee on Taxonomy of Viruses ICTV was formed and has been the sole body charged with classifying viruses since Taxonomy is the science of categorizing and assigning names nomenclature to organisms based on similar characteristics, and the ICTV utilizes the same taxonomical hierarchy that is used to classify living things.

It is important to note that viruses, since they are not alive, belong to a completely separate system that does not fall under the tree of life.

Whereas a living organism is classified using domain, kingdom, phylum, class, order, family, genus, and species taxa singular: taxon , or categories, viruses are only classified using order, family, genus, and species Table 2. The ICTV classifies viruses based upon a variety of different characteristics with the intention of categorizing the most similar viruses with each other. The chemical and physical properties of the virus are considered, such as the type of nucleic acid or number of different proteins encoded by the virus.

DNA technologies now allow us to sequence viral genomes relatively quickly and easily, allowing scientists to compare the nucleic acid sequences of two viruses to determine how closely related they are. Other virion properties are also taken into account, including virion size, capsid shape, and whether or not an envelope is present.

The taxa of viruses that infect vertebrates are shown in Fig. Also note the size difference between viruses of different families. Viruses are categorized based upon their type of nucleic acid DNA viruses in yellow boxes and RNA viruses in blue boxes and further classified based upon distinguishing characteristics. Note the nucleic acid, size, and architectural differences between viruses of different families.

Viruses in color will be discussed in later chapters. Seventy-seven virus families, however, have yet to be assigned to an order, including notable viruses such as the retroviruses, papillomaviruses, and poxviruses.

New orders have been proposed, and it is likely that more will be created as the taxonomical process continues. The ICTV has established guidelines for naming newly discovered viruses. The Latin binomial names that are used for living organisms, where the genus and species are listed together such as Homo sapiens or Yersinia pestis , are not used for naming viruses.

When directly referring to a viral order, family, genus, or species the virus name should be written in italics with the first letter capitalized.

When not referring specifically to viral classification, however, capitalization and italics are not required unless a proper name is encountered. Section 2. What is the function of the capsid? Why must viruses repeat the same capsid protein subunits over and over again, rather than having hundreds of different capsid proteins?

What is a structural unit? What taxa are used to classify viruses? How does this differ from the classification of a living organism? National Center for Biotechnology Information , U.

Essential Human Virology. Published online May 6. Jennifer Louten. Author information Copyright and License information Disclaimer. Elsevier hereby grants permission to make all its COVIDrelated research that is available on the COVID resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source.

Abstract Viruses have several common characteristics: they are small, have DNA or RNA genomes, and are obligate intracellular parasites. Taken together, we have learned that although they can be quite diverse, viruses share several common characteristics: 1. Open in a separate window.

All authors: No reported conflicts of interest. National Center for Biotechnology Information , U. Version 1. Peter D. Burbelo , 1 Francis X. Strich , 6 Daniel S.

Chertow , 6 Richard T. Davey, Jr. Cohen 8. Francis X. Jeffrey R. Daniel S. Richard T. Jeffrey I. Author information Copyright and License information Disclaimer. Correspondence to: Jeffrey I. Cohen, Bldg. Copyright notice.

This article is a US Government work. It is not subject to copyright under 17 USC and is also made available for use under a CC0 license. Methodology Quantitative measurements of plasma or serum antibodies by luciferase immunoprecipitation assay systems LIPS to the nucleocapsid and spike proteins were analyzed in cross-sectional or longitudinal samples from SARS-CoVinfected patients. Conclusions Antibody to the nucleocapsid protein of SARS-CoV-2 is more sensitive than spike protein antibody for detecting early infection.

Table 1. Open in a separate window. Figure 1. Figure 2. Figure 3. Heat inactivation of plasma or serum samples has no significant impact on detection of nucleocapsid antibodies. Acknowledgements We thank Kizzmekia S. Footnotes Potential conflicts of interest. References 1.

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