ABSTRACT The nucleocapsid (N) protein of SARS-CoV-2 is known to participate in various host cellular processes, including interferon inhibition, RNA interference, apoptosis, and regulation

of virus life cycles. Additionally, it has potential as a diagnostic antigen and/or immunogen. Our research focuses on examining structural changes caused by mutations in the N protein. We

have modeled the complete tertiary structure of native and mutated forms of the N protein using Alphafold2. Notably, the N protein contains 3 disordered regions. The focus was on

investigating the impact of mutations on the stability of the protein's dimeric structure based on binding free energy calculations (MM-PB/GB-SA) and RMSD fluctuations after MD

cell, as well as for the research of other viruses belonging to the same genus additionally, to anticipate potential strategies for addressing this viral illness․ SIMILAR CONTENT BEING

VIEWED BY OTHERS A CORE NETWORK IN THE SARS-COV-2 NUCLEOCAPSID NTD MEDIATES STRUCTURAL INTEGRITY AND SELECTIVE RNA-BINDING Article Open access 09 December 2024 STRUCTURAL, ENERGETIC AND

LIPOPHILIC ANALYSIS OF SARS-COV-2 NON-STRUCTURAL PROTEIN 9 (NSP9) Article Open access 26 November 2021 EXPOSING STRUCTURAL VARIATIONS IN SARS-COV-2 EVOLUTION Article Open access 11 November

2021 INTRODUCTION The SARS-CoV-2 virus contains a single-stranded RNA genome packaged in a 100-nm-diameter membrane-bound virion. The viral genome is made up of a positive sense,

single-stranded RNA that encodes four structural proteins—spike (S), envelope (E), membrane (M), and nucleocapsid (N)—along with 9 auxiliary proteins and 14 open reading frames (ORFs) that

encode 16 nonstructural proteins, making up a replicase complex. One of the most abundant structural proteins in cells infected with viruses is protein N, which is very conservative in the

CoV genus1,2,3. The primary function of the N protein is to encapsulate the viral RNA into long-stranded ribonucleocapsid (RNP) complexes, as well as to participate in the assembly of the

virus by interacting with the M protein of the virus genome. Additionally, it has been demonstrated that the CoV N protein participates in host cellular processes that control the viral life

cycles, also the N protein is an immunodominant antigen of the host cell immune system1,3,4,5. The N protein is encoded by the 9th ORF of the virus, consists of 419 amino acids, and has a

modular structure that can be divided into intrinsically disordered regions (IDRs) and conservative structural regions2,4. IDRs include 3 modules: N-arm (1–48 aa), central Ser/Arg rich

linker region (LKR) (176–247aa), and C-tail (366–419 aa), while conservative structural regions include two modules: N-terminal domain (NTD) (49–175 aa) and C-terminal domain (CTD) (248–365

aa). In the primary structure, NTD and CTD are connected by LKR and are surrounded by N-arm and C-tail1,3. The presence of disordered regions in viral proteins is commonly linked to viral

infectivity and pathogenicity. Disordered regions participate in liquid–liquid phase separation, which is also typical of N protein, which exhibits concentration-dependent liquid–liquid

phase separation in the presence of RNA6,7. The N protein is known to exhibit functional activity when present in a dimeric structure1,8. In the host cell, the N protein typically undergoes

phosphorylation, mainly occurring in its linker region. Additionally, it interacts with several viral proteins such as Membrane (M ) and non-structural protein (Nsp33), and host proteins

including GTPase-activating protein SH3 domain-binding protein 1/2 (G3BP-1/2), heterogeneous nuclear ribonucleoproteins (hnRNPs), NLR family PYRIN domain containing-3 (NLRP3), 14-3-3, and

others.3,6,9. The data revealed that the regions spanning amino acid positions 164 to 205, exhibited the highest number of mutations compared to the total number of amino acids among the N

amino acid sequences, and the second-highest frequency of mutations was observed in the regions spanning amino acid positions 205 to 24610. Some mutations can have a positive impact on

pathogenesis, such as R203K/G204R mutations11,12,13, but at the same time, these mutations decrease the structural stability and flexibility of the N protein. Also, there are data about the

absence of a significant correlation between N protein mutations and infection rates14. Some variants containing the R203K/G204R mutations in the N protein often exhibit concurrent

mutations, including the spike protein’s N501Y and E484K, among others13. Based on the above-mentioned, it is indeed challenging to establish a clear correlation between specific mutations

and pathogenesis or disease severity. Taking into account that viral proteins do not mutate singularly, and the impact on pathogenesis is likely influenced by the combination of multiple

mutations in different proteins, it becomes a complicated task. Therefore, determining the relationship between mutations and disease outcomes requires a comprehensive analysis of various

mutation combinations rather than focusing on individual mutations in isolation12,15. Due to the presence of multiple disordered regions within the N protein, including the N-arm, LKR, and

C-tail1,2,4, acquiring its comprehensive tertiary structure through experimental techniques like X-ray crystallography has presented formidable challenges16,17,18. As of now, there is no

established protocol for elucidating the structural conformation of disordered proteins. However, it's pertinent to highlight that the complete monomeric structure of the N protein

became accessible in the Protein Data Bank (PDB) on January 2023, utilizing electron microscopy with a resolution of 4.57 Å (PDBID: 8FD5). Herein, we used computational approaches to

understand how mutations impact the stabilization of SARS-CoV-2 N protein dimeric structure. We performed molecular modeling for the complete tertiary structure of wild-type and mutated N

proteins, molecular dynamic simulations, binding free energy and RMSD (root mean square deviation) calculations, and structural rearrangements among wild-type and mutated proteins. The data

obtained from this study are essential to understanding the tertiary structure of the N protein of SARS-CoV-2 and the impact of mutations on protein dimeric structure. Moreover, the findings

shed light on the behavioral dynamics of these mutations within host cellular environments. Such insights are crucial and could expand the scope of research on viruses of a similar genus.

RESULTS We used Alphafold2 (AF) for modeling and as we mentioned, there is no X-ray crystallographic model of the N protein complete tertiary structure, only 2 domains are available, thus we

based our further validation process on 2 known X-ray crystallographic domains: NTD (_PDBID:7N0R_), and CTD (_PDBID:6WZQ_). Figure 1a represents the N protein monomer, dimer, dimer’s chain

A and chain B complete modeled structures. It can be seen from the tertiary structure that the monomer disordered regions (N-arm, LKR, C-tail), besides not having the characteristic elements

of the secondary structure are scattered and not compactly assembled, and in the case of the dimer, the structure is different. We separated the dimer into 2 monomers (chain A and chain B)

that make up the dimer and compared them with the N monomer (Fig. 1b). Superimposition of modeled proteins on these domains and RMSD score comparison was performed using ICM Pro software.

RMSD comparison showed that RMSD [AF_N_monomer vs AF_N_ dimer_chainA] = 24.905253 Å and RMSD [AF_N_monomer vs AF_N _dimer_chainB] = 25.339612 Å. Given the limitation of observing structural

rearrangements within tertiary structures, we opted to visualize these alterations in secondary structural elements as a more effective means of analysis and visualization. We used the

PDBSum server to understand what kind of elements of secondary structures are presented in the modeled protein. The differences in the structure are also shown by the analysis of the

topology and the secondary structure elements (Fig. 1c,d). It is important to note that in both monomer and dimer models, there is an alpha helix between 400 and 412 amino acids (Fig. 1d),

which is the C-tail and one of the disordered regions of N protein. In the case of dimer, there are several short alpha helixes formed, the most noticeable of which are between 178 and 196

amino acids in chain A and B and between 215 and 234 amino acids in chain B and monomer (Fig. 1d). Figure 2a and b represent the pLDDT confidence measure in the B factor field obtained by

ChimeraX. The pLDDT score represents a pivotal instrument within the domain of protein structural prediction. Its primary function is to provide an estimate of the confidence levels

associated with individual amino acid residues within predicted protein configurations․ Employing a continuum ranging from 0 to 100, with scores exceeding 90 signifying a robust degree of

assurance and those falling below 50 indicating a diminished level of confidence, the pLDDT score plays a critical role in visualizing AlphaFold2 models. Specifically, it imparts a color

scheme where regions of heightened confidence are typically rendered in gradients of blue, contrasting with lower confidence regions, which are often portrayed in the hues of yellow, orange,

or red. The pLDDT score evaluates whether the predicted positioning of a given residue aligns with the distances between its C-alpha atom and neighboring C-alpha atoms (within a 15 Å range)

as observed in the true protein structure19. In the computational models presented (Fig. 2a and b), regions characterized by hues of yellow and orange signify pLDDT scores ranging from 50

to 70 which indicate IDRs within the protein structure. The manifestation of such IDRs is a documented attribute and is deemed characteristic within this molecular framework. The N monomer

exhibited an average pLDDT score of 65.54, while the dimer was characterized by an average iptm + ptm score of 0.466. The PAE plot functions similarly to a map, highlighting the differences

between the predicted and actual positions of various segments of a protein. PAE represents the absolute error in the relative positioning of residues, quantified in Ångströms (0–30)․ Colors

are used to represent these disparities, with dark green indicating minimal differences and white representing larger discrepancies. When examining this map, we expect most points along the

diagonal line to be close to dark green because this is where the predicted and actual positions should closely align. If we observe distinct clusters of similar colors elsewhere on the

map, it signifies that the model is highly confident in those regions. An outstanding model would display an entirely dark green map, signifying highly accurate predictions of the

protein's structure19. The plot c displayed in Fig. 2c represents a PAE plot specifically designed to depict the characteristics of the N-dimer model. On January 2023 Casasanta M et al.

released the N protein monomer model using cryo-electron microscopy (Cryo-EM) (_PDBID:8FD5_) with 4.57 Å resolution20. Our investigation entailed a comprehensive examination of the N

monomers' domains within our AF models in superimposition with the analogous domains derived from Cryo-EM and X-ray crystallography methodologies. The results are presented in Fig. 3.

To check the accuracy of the AF models we compared them with X-ray and with CryoEM structures (Fig. 3a–c). Through calculations and comparisons, we have established the significant utility

of AF modeling. Specifically, when we superimposed AF-NTD and AF-CTD models onto X-ray domains, we observed remarkably low RMSD scores (Fig. 3a and c), measuring at 1.94 Å and 1.89 Å,

respectively. However, in the case of IDR, where no X-ray model was available for reference, we conducted a comparison solely with the Cryo-EM model (Fig. 3b), resulting in a considerably

higher RMSD score exceeding 23.4 Å. This elevated RMSD score can be attributed to the absence of structured regions in the AF and Cryo-EM models, with Cryo-EM models demonstrating greater

assembly of disordered regions, a behavior consistent with expectations. The N protein acquires functional activity when it adopts a dimeric structure. Consequently, in subsequent

experiments, we used the AF dimeric configuration. To assess the reliability of the AF dimeric structure, we considered not only the pLDDT and PAE scores but also examined whether its

dimerization domain contributed to the formation of the dimer. Furthermore, we conducted a comparison of structures with the X-ray model (PDBID:6WZQ, PDBID:7N0R), which was originally

associated with the oligomeric structure (Fig. 3d). When comparing the AF-NTD dimeric structure with X-ray dimeric structures, we observed a higher RMSD score for the NTD dimer as

represented in Fig. 3d. This elevated RMSD score can be attributed to the fact that the NTD does not participate in dimerization. However, in a separate comparison where we compared the

NTD-dimer chains with X-ray monomeric structures, we found a significantly lower RMSD score of 1.16 Å. This lower RMSD score suggests a closer structural resemblance between the NTD-dimer

chains and the X-ray monomeric structures. In contrast, for the CTD dimer, the RMSD score was 0.8 Å, which aligns with our expectations. This low RMSD score indicates a close match between

the AF-CTD dimeric structure and the X-ray dimeric structures, suggesting that the CTD is actively involved in dimerization. We evaluated several metrics such as pLDDT, PAE, RMSD scores,

along with tertiary and secondary structure elements, and topologies. These metrics uniformly confirmed the precision and dependability of the AF models. Apart from the precision concerns of

AF modeling, our main objective was to understand how mutations in the N protein impact the stability of its dimeric formation. For our study, we chose 34 single mutations (D3L, Q9L, P13L,

D63G, I157T, Q160R, P168Q, A173S, R185C, S186F, S197A, S197L, S202N, R203E, R203M, T205A, T205I, A208S, G215C, S235F, K256N, T265I, A267V, T296I, F307V, A308S, M322I, P326L, K374N, D377Y,

Q384H, D401Y, S413I, Q418H) and 3 combined mutations, each distinctive to specific strains of concern. These include the Alpha strain (lineage B.1.1.7) with mutations D3L, S235F, R203K,

G204R; the Gamma strain (lineage P.1) and Omicron strain (lineage B1.1.529) both featuring R203K, G204R mutations, and the Delta strain (lineage B1.617.2) identified by mutations D63G,

R203M, D377Y. It is also important to note that the R203K/G204R mutants occur together10,16, and we also observed this in the single-protein model. T205I is associated with the Beta strain

(lineage B.1.351) so we observed T205I single mutation as a Beta strain-related mutation. Thus, we have had 38 models, 1 for native N protein (N wild type) and 37 for mutated proteins. Data

for N protein mutations were obtained from the previous study21 and the CoV-Lineages Report22. Figure 4 represents all selected mutations for further study- according to their location: it

can be seen that the vast majority of mutations are located in the disordered regions N-arm -3 mutations, LKR-14 mutations, C-tail-6 mutations, and in the functional domains—NTD-5 and CTD-8

mutations. For further experiments, the native N protein was chosen as the control model. The supplementary Table S1 represents the RMSD scores for all models compared with the native N

protein structure. The strain-specific protein tertiary structures were looked at, as shown in Fig. 5. The data analysis reveals notable distinctions between the mutant models and the native

N protein structure, as observed through structural superimposition. Figure 5a represents the existence of variations in the secondary structure elements within each strain-specific model

which illustrates the impact of the mutation on the structure. All mentioned rearrangements affect on tertiary structure of strain-specific models (Fig. 5b). An in-depth examination of the

mutated variants revealed that all the studied mutations had an impact on the secondary structure elements of the protein, resulting in structural rearrangements such as loop-to-α-helix,

loop-to-β-sheet, β-sheet-to-loop, and α-helix-to-loop transitions, as shown in Supplementary Fig. S1. We employed the PDBSum website for this structural analysis23. All studies were

performed at the level of tertiary structure, but for visualization and analysis, the tertiary structure was represented as a secondary structure. Since the server performs calculations at

the monomer level, we separated the model dimers by monomer chains and then considered them. The models utilized in our research were selectively phosphorylated at several distinct sites:

S23, S180, S186, S188, S194, S197, S201, S202, S206, T24, T198, T205, T265, and T391. After the phosphorylation process, these modified structures were then subjected to MD simulations. Each

simulation was conducted for a substantial duration of 100 ns with a simulation time step of 2 fs. To determine the protein structure stability, ΔG values, as well as RMSD and ΔG

fluctuation graphs, were considered and presented in Figs. 6, 7 and Supplementary Table S1. The average number of atoms in each of the systems, together with water and ions (Na+ and Cl−), is

about 123,000 atoms, and the protein part of the systems is about 12,800 atoms. From the graphs of RMSD fluctuations (Fig. 6) for native N and all mutated models, it can be seen that the

structure of the native N protein remained stable, while the mutant forms also showed instability of the structure, or stability at the beginning of the dynamics, then instability, and vice

versa. Instability was observed especially in D3L, S186F, S197L, R203E, K256N, F307V models. Using the trajectories obtained from molecular dynamics, we also calculated the binding free

energy of the monomers that make up the dimer in the systems by the MM/PBSA and MM/GBSA methods. ΔG values results are presented in Supplementary Table S1. In Fig. 7, we present a series of

graphs that depict the ΔG values computed using the MM/PBSA methodology across all systems under investigation. For these calculations, every 10th frame was selected, resulting in a total of

500 frames being considered for both the native and all associated mutated models. From the data obtained by the MM/GBSA method (Supplementary Table S1), the strongest interaction of

monomers in the dimer is observed in the case of the P326L mutation (ΔG = − 848.7848 kcal/mol), in the case when the following value is N wild-type (ΔG = − 765.8 ), the worst value is S202N

(ΔG = 1331.8977), and strongest interaction by MM/PBSA data is Q418H (ΔG = − 643.6706), the weakest match with MM/GBSA data and it’s S202N (ΔG = 1477.9502). The more negative the value of

ΔG, the more stable the system is, and vice versa. By data in Supplementary Table S1, according to MM/PBSA N wild-type ΔG = − 587.1434, and for Q418H ΔG = − 643.6706, T205A:ΔG = − 625.9514,

P168Q:ΔG = − 615.9578, Q384H:ΔG = − 600.1673, P326L:ΔG = − 598.1866, D3L:ΔG = − 587.1434, which means that these mutations led to a significant stabilization of the dimer structure. And the

mutations R203K/G204R (ΔG = 74.8293), S197L (ΔG = 490.5904), D63G (ΔG = 665.7818), K256N (ΔG = 751.8934), D377Y (ΔG = 994.0435), S202N (ΔG = 1477.9502) caused destabilization. As

demonstrated by MM/PBSA analyses, it was found that the dimer structure was stable in case of 28 mutations (Q418H, T205A, P168Q, Q384H, P326L, D3L, G215C, S197A, A267V, S235F, S413I, T265I,

Q160R, M322I, F307V, D401Y, A308S, alpha, P13L, delta, R185C, A173S, A208S, I157T, T205I, Q9L, R203E, S186F), and unstable in case of 9 mutations (K374N, T296I, R203M, R203K_G204R, S197L,

D63G, K256N, D377Y, S202N), Fig. 8. DISCUSSION The N protein, widely distributed within the viral genome, assumes a key role in a spectrum of fundamental biological functions, including RNA

packaging, viral assembly, replication, and transcription1,2,4,6. The multifaceted involvement of the N protein in these essential processes underscores its significance at the core of the

virus life cycle. However, studying the structure of N protein is complicated by the presence of disordered regions characterized by inherent conformational flexibility and a lack of

well-defined tertiary structures. The study of such disordered regions is of paramount importance, given their propensity to contribute to the functional versatility of the protein,

particularly in mediating various protein–protein interactions and adaptations during viral processes. The primary objective of our research was to elucidate the impact of mutations in the N

protein on the stability of its dimeric conformation. The N protein attains its functional activity in a dimeric arrangement. Given the pivotal role of the N protein in interactions with

both viral and host cell proteins, comprehending the potential influence of mutations on its dimeric structure assumes critical importance. Before scrutinizing the effects of the mutations,

a prerequisite was the modeling of the complete structure of the N protein, a task rendered challenging by the presence of disordered regions that are inherently flexible. Employing the

Alphafold2 technique, we executed the structural modeling and validated it by comparative analysis with the extant two X-ray structures of 2 domains: NTD and CTD, as well as

cross-verification with models derived from cryo-EM. The modeling encompassed both the monomeric and dimeric forms of the N protein, aiming to discern potential disparities between the two.

Visualization of the tertiary structure and identification of secondary structural elements were pivotal in this comparative analysis. As illustrated in Fig. 1, we observed a pattern in the

monomers that constitute the dimeric structure. Specifically, secondary structure elements were found to manifest in disorder regions within the monomers forming the dimer compared to the

monomer model. An intriguing observation emerged when examining the dimeric structure itself—the disorder regions within the dimer exhibited a greater degree of assembly. We hold the view

that the disorder regions in the protein tend to be more organized and assembled when part of a dimeric structure7. Subsequent experimental investigations focused exclusively on the dimeric

structure. The impact of a total 34 individual mutations, along with an additional 3 combined mutations, was systematically studied utilizing computational methodologies. This analytical

approach afforded a comprehensive understanding of the nuanced effects of these mutations on the stability and conformational dynamics of the N protein's dimeric structure. The

R203M/R203K, D377Y, D63G, G215C, G204R, D3L, S235F, and Q9L mutations included in our study are among the top 10 mutations of the N protein with the highest frequency worldwide10. MD

simulations and free binding energy calculations within the dimers showed that 9 of the 37 mutant models selected led to the destabilization of the dimer structure and 28 to stabilization.

The R203K/G204R mutation caused destabilization, which was also shown by other authors13,14,24. These mutations have been particularly studied because it has been found in a larger number of

lineages. The escalated infectiousness of the R203K/G204R mutant virus was experimentally verified by Wu et al., as evidenced by heightened viral replication observed in the 203K/204R

virus13. This enhanced replication was observed across various cell lines and primary upper airway tissues both humans and hamsters. The heightened efficiency in viral replication suggests a

potential for increased virulence and fitness. Similar associations with heightened fitness and disease severity have been observed in other mutations linked to high infectivity, including

D614G and N510Y in Spike (S) protein. Despite the distinct locations of the S protein and N protein, both being structural components of the virion, notable similarities between D614G and

R203K/G204R exist concerning the consequences of the virus's properties. In article from Mourier et al. has shown that R203K/G204R has higher viral loads in COVID-19 patients in Saudi

Arabia24. R203K/G204R mutations increased along with the mutation of the Spike Y501N protein. R203K/G204R mutation increased oligomerization potential and RNA-binding affinity compared with

wild-type N. Also, they showed that R203K/G204R mutations lead to significant changes in protein structure (destabilize) and potentially enhance the protein's ability to bind RNA and

alter its response to serine phosphorylation events. In any case, the authors did not find an association between the R203K/G204R mutations' high load and mortality, because even though

the virus had a high load, it did not affect mortality, since it is possible that other factors played a role, such as a change in the course of treatment, or a person's susceptibility

to infection. the complications that occurred24. Anyway, making definitive conclusions about the correlation between disease severity and mutation based on a single mutation within one

protein is challenging. The reason is that strains typically possess multiple complex mutations in various proteins, both structural and non-structural. During our study, 63.8% of selected

mutations were located in disorder regions, of which 60.86% were in the LKR part, which is involved in protein–protein interactions. About this, Abavisani et al. also note that the majority

of N protein mutations occur in 164 to 205 AA and the second highest mutations frequency in N 205 to 246 AA, which also include in the basis of the disorder sections10. The study by Oulas et

al. demonstrates that the P13L mutation can influence both transmissibility and death rates. Specifically, this mutation is associated with a decrease in the number of deaths and cases per

million15,25. For the P13L mutation, our experiments showed that it leads to structural stabilization. Making definitive conclusions about the correlation between disease severity and

mutation based on a single mutation within one protein is challenging. The reason is that strains typically possess multiple complex mutations in various proteins, both structural and

non-structural, and more research is needed, because the same R203K/G204R mutation, which has been reported in several articles to affect virulence, pathogenesis, and fitness10,11,13,24, is

found in the base together with the spike protein N510Y mutation, which also enhances viral attachment and infectivity10,24․ In conclusion, our findings hold significance in elucidating the

tertiary structure of the N protein dimer and comprehending the consequences of mutations on its conformation. Our study provides insights into the behavioral dynamics of these mutations

within the host cell. It is known from the literature that intrinsically disordered proteins (IDPs) undergo a disorder-to-order transition state during protein–protein interactions7,26,27.

We advance a hypothesis suggesting that in the dimeric state characterized by protein–protein interaction, secondary structure elements appear in the disordered parts of the protein, and the

protein is assembled in its tertiary structure. As illustrated in Fig. 1d and Supplementary Fig. S1, our findings indicate that, compared with N monomer secondary structure elements, in

dimers, the secondary structure elements appear in disorder regions. And compared to the wild-type dimer N structure, models featuring the D63G, Q160R, S197L, S186F, S202N, S203E, R203M,

K256N, T296I, S413I, Q418H, alpha and delta mutations (35,1% of all models) exhibit a reduced presence of secondary structure elements within the disordered regions (Supplementary Fig. S1).

From which D63G, S197L, S202N, R203M, K256N, and T296I mutations (46,15%) led to the destabilization of the structure. This allows us to assume mutations that caused destabilization, are

less likely to generate secondary structure elements in disordered regions of the protein. Our results contribute not only to an understanding of the SARS-CoV-2 N protein but also offer

valuable hints for the investigation of conservative proteins in other viruses within the same genus. Our research establishes a basis for proactive strategies designed to address the

pathogenesis of this viral illness as the presence of disordered regions in viral proteins is commonly associated with viral infectivity and pathogenicity, as these regions typically play an

essential role in the binding process with targets7. Certainly, in the context of single protein mutations, drawing unequivocal conclusions regarding their impact on virus flexibility,

virulence, and disease progression among patients is challenging. Comprehensive investigations are essential, extending beyond the examination of individual mutations within a single

protein. Instead, a more intricate approach involves conducting studies at the level of multiple mutations within proteins that play crucial roles in the virus's life cycle. This

encompasses an exploration of the collective effects of mutations across various proteins involved in the virus life cycle. METHODS MODELING To carry out the research, we used Alphafold2

version 2.1.2 for protein structure prediction19,28. The amino acid sequence of the SARS-CoV-2 N protein (UniProt ID: P0DTC9) was used for molecular modeling. PHOSPHORYLATION AND MUTATIONS

All mutations were obtained using the ICM-Pro software29, and then the models were phosphorylated at the following sites—S23, S180, S186, S188, S194, S197, S201, S202, S206, T24, T198, T205,

T265, T39130. MD SIMULATIONS, ANALYSIS (MM/PB(GB)SA, RMSD) The resulting systems were subjected to 100 ns molecular dynamics (MD) simulations using AMBER2031,32. All molecular dynamics (MD)

simulations were conducted in water environments using the ff19SB force field for the solute, as it has been demonstrated to synergize optimally with the OPC water model, which offers

enhanced accuracy33,34,35. The solvation box, housing the water molecules, was defined as a cubic periodic boundary to ensure homogeneous spatial distribution and to mitigate potential edge

effects throughout the simulation trajectories. The integration step for the leap-frog algorithm was fixed at 2 fs. Simultaneously, the system was maintained at 309.75 K under standard

atmospheric pressure. The binding free energy was calculated by the MM/PB(GB)SA method, which is based on the determination of the energy of the protein–ligand complex, the individual

energies of the protein and the ligand, and then the determination of the energy difference36,37. Molecular dynamics simulations were executed across all the prepared systems, each spanning

a duration of 100 ns. For every individual system, a consistent sampling rate was maintained, resulting in the acquisition of 5,000 frames. The binding free energy (ΔG) is calculated

according to Eq. (1)36,37. $$\Delta G={\Delta G}_{complex}-{\Delta G}_{receptor}-{\Delta G}_{ligand}$$ (1) Since each system consists of an N protein dimer, the role of the receptor and the

ligand were each of the monomers: receptor (monomer1)—the monomer with amino acid residues 1–419, and ligand (monomer2)—the monomer with amino acid residues 420–838, Eq. (2). $$\Delta

G={\Delta G}_{complex}-{\Delta G}_{monomer1}-{\Delta G}_{monomer2}$$ (2) Typically, the quality of a model is assessed based on its energy or similarity to a reference structure—RMSD

(usually atoms in the protein backbone). RSMD analysis of all obtained trajectories was performed using the CPPTRAJ program. RMSD can be used to identify significant changes in protein

structure relative to the starting point. The equilibrium nature of the RMSD curve indicates that the protein was equilibrated38. For studying structural rearrangements (loop-α-helix,

loop-β-sheet, β-sheet-loop, and α-helix-loop) the PDBSum website was used23. For visualization, we have used ICM-Pro software, and for graph building Gnuplot and ChimeraX have been

used39,40. DATA AVAILABILITY The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. REFERENCES * Bai, Z., Cao, Y., Liu, W.

& Li, J. The SARS-COV-2 nucleocapsid protein and its role in viral structure, biological functions, and a potential target for drug or vaccine mitigation. _Viruses_ 13, 1115 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar  * Kopecky-Bromberg, S. A., Martínez-Sobrido, L., Frieman, M. B., Baric, R. & Palese, P. Severe acute respiratory syndrome

coronavirus open reading frame (ORF) 3B, ORF 6, and nucleocapsid proteins function as interferon antagonists. _J. Virol._ 81, 548–557 (2007). Article  CAS  PubMed  Google Scholar  * Khan, A.

_et al._ Structural insights into the mechanism of RNA recognition by the N-terminal RNA-binding domain of the SARS-CoV-2 nucleocapsid phosphoprotein. _Comput. Struct. Biotechnol. J._ 18,

2174–2184 (2020). Article  CAS  PubMed  PubMed Central  Google Scholar  * Giri, R. _et al._ Understanding COVID-19 via comparative analysis of dark proteomes of SARS-CoV-2, human SARS and

bat SARS-like coronaviruses. _Cell. Mol. Life Sci._ 78, 1655–1688 (2020). Article  PubMed  PubMed Central  Google Scholar  * Mu, J. _et al._ SARS-CoV-2-encoded nucleocapsid protein acts as a

viral suppressor of RNA interference in cells. _Sci. China Life Sci._ 63, 1413–1416 (2020). Article  PubMed  PubMed Central  Google Scholar  * Tugaeva, K. V. _et al._ The mechanism of

SARS-COV-2 nucleocapsid protein recognition by the human 14-3-3 proteins. _J. Mol. Biol._ 433, 166875 (2021). Article  CAS  PubMed  PubMed Central  Google Scholar  * Tenchov, R. & Zhou,

Q. Intrinsically disordered proteins: Perspective on COVID-19 infection and drug discovery. _ACS Infect. Dis._ 8, 422–432 (2022). Article  CAS  PubMed  Google Scholar  * Zhou, R., Zeng, R.,

Von Brunn, A. & Lei, J. Structural characterization of the C-terminal domain of SARS-CoV-2 nucleocapsid protein. _Mol. Biomed._ https://doi.org/10.1186/s43556-020-00001-4 (2020). Article

  PubMed  PubMed Central  Google Scholar  * Ni, X., Han, Y., Zhou, R., Zhou, Y. & Lei, J. Structural insights into ribonucleoprotein dissociation by nucleocapsid protein interacting with

non-structural protein 3 in SARS-CoV-2. _Commun. Biol._ https://doi.org/10.1038/s42003-023-04570-2 (2023). Article  PubMed  PubMed Central  Google Scholar  * Abavisani, M. _et al._

Mutations in SARS-CoV-2 structural proteins: A global analysis. _Virol. J._ https://doi.org/10.1186/s12985-022-01951-7 (2022). Article  PubMed  PubMed Central  Google Scholar  * Johnson, B.

A. _et al._ Nucleocapsid mutations in SARS-CoV-2 augment replication and pathogenesis. _PLoS Pathog._ 18, e1010627 (2022). Article  CAS  PubMed  PubMed Central  Google Scholar  * Khan, M. Z.

_et al._ An overview of viral mutagenesis and the impact on pathogenesis of SARS-CoV-2 variants. _Front. Immunol._ https://doi.org/10.3389/fimmu.2022.1034444 (2022). Article  PubMed  PubMed

Central  Google Scholar  * Wu, H. _et al._ Nucleocapsid mutations R203K/G204R increase the infectivity, fitness, and virulence of SARS-CoV-2. _Cell Host Microbe_ 29, 1788-1801.e6 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar  * Rahman, M. S. _et al._ Evolutionary dynamics of SARS-CoV-2 nucleocapsid protein and its consequences. _J. Med. Virol._ 93, 2177–2195

(2020). Article  PubMed  Google Scholar  * Dang, S., Ren, L. & Wang, J. Functional mutations of SARS-CoV-2: Implications to viral transmission, pathogenicity and immune escape. _Chin.

Med. J._ 135, 1213–1222 (2022). Article  CAS  PubMed  PubMed Central  Google Scholar  * Oldfield, C. J. & Dunker, A. K. Intrinsically disordered proteins and intrinsically disordered

protein regions. _Annu. Rev. Biochem._ 83, 553–584 (2014). Article  CAS  PubMed  Google Scholar  * Uversky, V. N., Gillespie, J. R. & Fink, A. L. Why are “natively unfolded” proteins

unstructured under physiologic conditions?. _Proteins_ 41, 415–427 (2000). Article  CAS  PubMed  Google Scholar  * Wilson, C. J., Choy, W. & Karttunen, M. AlphaFold2: A role for

disordered protein/region prediction?. _Int. J. Mol. Sci._ 23, 4591 (2022). Article  CAS  PubMed  PubMed Central  Google Scholar  * Jumper, J. _et al._ Highly accurate protein structure

prediction with AlphaFold. _Nature_ 596, 583–589 (2021). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Casasanta, M. A. _et al._ Structural insights of the SARS-COV-2

nucleocapsid protein: Implications for the inner-workings of rapid antigen tests. _Microsc. Microanal._ 29, 649–657 (2022). Article  ADS  Google Scholar  * Avetyan, D. _et al._ Molecular

analysis of SARS-COV-2 lineages in Armenia. _Viruses_ 14, 1074 (2022). Article  CAS  PubMed  PubMed Central  Google Scholar  * Cov-Lineages. https://cov-lineages.org/. * Team, E. W.PDBSum

generate. https://www.ebi.ac.uk/thornton-srv/databases/pdbsum. * Mourier, T. _et al._ SARS-CoV-2 genomes from Saudi Arabia implicate nucleocapsid mutations in host response and increased

viral load. _Nat. Commun._ https://doi.org/10.1038/s41467-022-28287-8 (2022). Article  PubMed  PubMed Central  Google Scholar  * Oulas, A. _et al._ Generalized linear models provide a

measure of virulence for specific mutations in SARS-CoV-2 strains. _PLoS ONE_ 16, e0238665 (2021). Article  CAS  PubMed  PubMed Central  Google Scholar  * Lindström, I. & Dogan, J.

Dynamics, conformational entropy, and frustration in protein–protein interactions involving an intrinsically disordered protein domain. _ACS Chem. Biol._ 13, 1218–1227 (2018). Article 

PubMed  Google Scholar  * Saurabh, S., Nadendla, K., Purohit, S. S., Sivakumar, P. M. & Çetinel, S. Fuzzy drug targets: Disordered proteins in the drug-discovery realm. _ACS Omega_ 8,

9729–9747 (2023). Article  CAS  PubMed  PubMed Central  Google Scholar  * Evans, R. _et al._ Protein complex prediction with AlphaFold-Multimer. _BioRxiv Cold Spring Harb. Lab._

https://doi.org/10.1101/2021.10.04.463034 (2021). Article  Google Scholar  * Abagyan, R., Totrov, M. & Kuznetsov, D. ICM - A new method for protein modeling and design: Applications to

docking and structure prediction from the distorted native conformation. _J. Comput. Chem._ 15, 488–506 (1994). Article  CAS  Google Scholar  * Tung, H. Y. L. & Limtung, P. Mutations in

the phosphorylation sites of SARS-CoV-2 encoded nucleocapsid protein and structure model of sequestration by protein 14-3-3. _Biochem. Biophys. Res. Commun._ 532, 134–138 (2020). Article 

CAS  PubMed  PubMed Central  Google Scholar  * Case, D. I. _et al._ AMBER (2020). * Karplus, M. Molecular Dynamics simulations of biomolecules. _Acc. Chem. Res._ 35, 321–323 (2002). Article

  CAS  PubMed  Google Scholar  * Izadi, S., Anandakrishnan, R. & Onufriev, A. V. Building water models: A different approach. _J. Phys. Chem. Lett._ 5, 3863–3871 (2014). Article  CAS 

PubMed  PubMed Central  Google Scholar  * Raguette L., Cuomo, A., Belfon, K., Tian, C., Wu, Q. & Simmerling, C. Updated Amber force field parameters for phosphorylated amino acids for

ff14SB and ff19SB. (2020) (IN PREP). * Tian, C. _et al._ ff19SB: Amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. _J. Chem.

Theory Comput._ 16, 528–552 (2019). Article  PubMed  Google Scholar  * Kumari, R., Kumar, R. & Lynn, A. M. g_mmpbsa—A GROMACS tool for high-throughput MM-PBSA calculations. _J. Chem.

Inf. Model._ 54, 1951–1962 (2014). Article  CAS  PubMed  Google Scholar  * Massova, I. & Kollman, P. A. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to

predict ligand binding. _Perspect. Drug Discov. Des._ 18, 113–135 (2000). Article  CAS  Google Scholar  * Kufareva, I. & Abagyan, R. Methods of protein structure comparison. In _Methods

in Molecular Biology_ 231–257 (2011) https://doi.org/10.1007/978-1-61779-588-6_10. * Williams, T., et algnuplot 5.2. An interactive plotting program. (2017). http://www.gnuplot.info/. *

Pettersen, E. F. _et al._ UCSF ChimeraX: Structure visualization for researchers, educators, and developers. _Protein Sci._ 30, 70–82 (2020). Article  PubMed  PubMed Central  Google Scholar

  Download references ACKNOWLEDGEMENTS We are grateful to Dr. Arsen Arakelyan and the Institute of Molecular Biology’s Human Genomics Laboratory and Bioinformatics group for providing

mutation data related to the N protein. The work was supported by the Higher Education and Science Committee RA, in the frames of the research projects №21AG-1F057, 22AA-1F026, and

Foundation Armenia (Switzerland). This work used the Cirrus UK National Tier2 HPC Service at EPCC (http://www.cirrus.ac.uk) funded by the University of Edinburgh and EPSRC (EP/P020267/1). We

also thank the Joint Institute for Nuclear Research (Dubna, Russia) for providing HPC resources. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Laboratory of Computational Modeling of

Biological Processes, Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia (NAS RA), 0014, Yerevan, Armenia Nelli Muradyan, Vahram Arakelov, Arsen

Sargsyan, Adrine Paronyan, Grigor Arakelov & Karen Nazaryan * Russian-Armenian University, 0051, Yerevan, Armenia Arsen Sargsyan, Adrine Paronyan, Grigor Arakelov & Karen Nazaryan

Authors * Nelli Muradyan View author publications You can also search for this author inPubMed Google Scholar * Vahram Arakelov View author publications You can also search for this author

inPubMed Google Scholar * Arsen Sargsyan View author publications You can also search for this author inPubMed Google Scholar * Adrine Paronyan View author publications You can also search

for this author inPubMed Google Scholar * Grigor Arakelov View author publications You can also search for this author inPubMed Google Scholar * Karen Nazaryan View author publications You

can also search for this author inPubMed Google Scholar CONTRIBUTIONS G.A. conceived the experiments, N.M. conducted the experiments, N.M. and G.A. analysed the experiments, N.M. and V.A.

prepared figures. N.M. wrote the draft. All authors: K.N., V.A., G.A., A.P., A.S., and N.M. reviewed the manuscript. K.N. supervision. CORRESPONDING AUTHOR Correspondence to Grigor Arakelov.

ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER'S NOTE Springer Nature remains neutral with regard to

jurisdictional claims in published maps and institutional affiliations. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION. RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under

a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate

credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article

are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons

licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of

this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Muradyan, N., Arakelov, V., Sargsyan, A. _et al._ Impact of

mutations on the stability of SARS-CoV-2 nucleocapsid protein structure. _Sci Rep_ 14, 5870 (2024). https://doi.org/10.1038/s41598-024-55157-8 Download citation * Received: 30 October 2023 *

Accepted: 21 February 2024 * Published: 11 March 2024 * DOI: https://doi.org/10.1038/s41598-024-55157-8 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this

content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative