key: cord-355397-y69bk5jc
authors: Caruso, Ícaro P.; Sanches, Karoline; Da Poian, Andrea T.; Pinheiro, Anderson S.; Almeida, Fabio C. L.
title: Dynamics of the N-terminal domain of SARS-CoV-2 nucleocapsid protein drives dsRNA melting in a counterintuitive tweezer-like mechanism
date: 2020-09-06
journal: bioRxiv
DOI: 10.1101/2020.08.24.264465
sha: 
doc_id: 355397
cord_uid: y69bk5jc

The N protein of betacoronaviruses is responsible for nucleocapsid assembly and other essential regulatory functions. Its N-terminal domain (NTD) interacts and melts the double-stranded transcriptional regulatory sequences (dsTRS), regulating the discontinuous subgenome transcription process. Here, we used molecular dynamics (MD) simulations to study the binding of SARS-CoV-2 N-NTD to non-specific (NS) and TRS dsRNAs. We probed dsRNAs’ Watson and Crick (WC) base-pairing over 25 replicas of 100 ns MD simulations, showing that only one N-NTD of dimeric N is enough to destabilize dsRNAs, initiating melting. N-NTD dsRNA destabilizing activity was more efficient for dsTRS than dsNS. N-NTD dynamics, especially a tweezer-like motion of β2-β3 and 2-β5 loops, played a key role in WC base-pairing destabilization. Based on experimental information available in the literature, we constructed kinetics models for N-NTD-mediated dsRNA melting. Our results support a 1:1 stoichiometry (N-NTD:dsRNA), matching MD simulations and raising different possibilities for N-NTD action: (i) two N-NTDs of dimeric N would act independently, increasing efficiency; (ii) two N-NTDs of dimeric N would bind to two different RNA sites, bridging distant regions of the genome; and (iii) monomeric N would be active, opening up the possibility of a regulatory dissociation event. IMPORTANCE Coronaviruses are among the largest positive-sense RNA viruses. They display a unique discontinous transcription mechanism, involving N protein as a major player. The N-NTD promote the dsRNA melting releasing the nascent sense negative strand via a poorly known mechanism of action. It specifically recognizes the body TRS conserved RNA motif located at the 5’ end of each ORF. N protein has the ability to transfer the nascent RNA strand to the leader TRS. The mechanism is essential and one single mutation at the RNA binding site of the N-NTD impairs the viral replication. Here, we describe a counterintuitive mechanism of action of N-NTD based on molecular dynamics simulation and kinetic modelling of the experimental melting activity of N-NTD. This data impacts directly in the understanding of the way N protein acts in the cell and will guide future experiments.

The recent pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of Coronavirus Disease 2019 (COVID- 19) , has become a global health emergency (1, 2) . SARS-CoV-2 is an enveloped virus containing a large nonsegmented positive-sense single-stranded RNA genome, belonging to the Coronaviridae family (3, 4) . The 5' two-thirds of the coronaviruses' genome, corresponding to ORF1a/b, is translated into two polyproteins (pp1a and pp1ab) that are proteolytically processed into sixteen nonstructural proteins (NSPs) (5) . These NSPs assemble in the viral replicasetranscriptase complex (RTC) at the endoplasmic reticulum membrane, being responsible for genome replication and transcription (6) . Conversely, the 3' one-third of the genome is translated into accessory proteins as well as the four structural proteins, spike (S), membrane (M), envelope (E), and nucleocapsid (N), through a unique process of subgenomic mRNA (sgmRNA) transcription (7, 8) .

N is one of the most abundant viral proteins in the infected cell. It is a 46-kDa multifunctional RNA-binding protein that drives the viral RNA packaging into a helical nucleocapsid (9) . In addition, N localizes at the RTC at early stages of infection and plays a central role in the regulation of RNA synthesis (10) (11) (12) . It is composed of two functionally distinct folded domains, which are interspersed by an intrinsically disordered linker region enriched in arginine and serine residues. Both the two domains and the linker region contribute individually to RNA binding (13) . The N-terminal domain (NTD) has been shown to interact with regulatory RNA sequences during subgenome transcription, whereas the C-terminal domain (CTD) is responsible for N protein dimerization, which is crucial for nucleocapsid assembly (14, 15) . The recently reported solution structure of SARS-CoV-2 N-NTD reveals a right hand-like fold, composed of a five-stranded central β-sheet flanked 5 by two short α-helices, arranged in a β4-β2-β3-β1-β5 topology (16) . The β-sheet core is referred to as the hand's palm, while the long β2-β3 hairpin, mostly composed of basic amino acid residues, corresponds to the basic finger. The positively-charged cleft between the basic finger and the palm has been suggested as a putative RNA binding site (16) .

Genome replication is a continuous process in coronaviruses. In contrast, transcription is discontinuous and involves the production of sgmRNAs (17) . Regulation of sgmRNA synthesis is dependent on transcriptional regulatory sequences (TRSs) located either at the 5' end of the positive-strand RNA genome, known as the leader TRS (TRS-L), or at the 5' end of each viral gene coding for structural and accessory proteins, called the body TRS (TRS-B). TRS-L and TRS-B share an identical core sequence, which allows for a template switch during sgmRNA synthesis. Once the TRS-B has been copied, the nascent negative-strand RNA is transferred to TRS-L and transcription is terminated (17, 18) .

Multiple well-orchestrated factors, including TRS secondary structure, RNA-RNA and RNA-protein interactions, influence sgmRNA transcription (17) . Coronaviruses' N-NTD specifically interacts with the TRS and efficiently melts a TRS-cTRS RNA duplex, facilitating template switch and playing a pivotal role in the regulation of discontinuous transcription (10, 16, 17, 19) . Despite its relevance for the viral replication cycle, the molecular basis underlying the specificity of interaction of SARS-CoV-2 N-NTD with the TRS sequence remains elusive. Thus, understanding the mechanism by which SARS-CoV-2 N-NTD specifically recognizes TRS RNA at atomic detail is paramount for the rational development of new antiviral strategies.

Here, we present a hypothesis for the molecular mechanism of dsRNA melting activity of SARS-CoV-2 N-NTD. We showed by molecular dynamics (MD) simulations (25 replicas of 100 ns) that N-NTD destabilizes dsRNA's Watson and Crick base-pairing 6 by dropping down intramolecular hydrogen bonds and perturbing the local rigid-body geometric parameters of dsRNA. The destabilization is more significant for TRS than for a non-specific (NS) dsRNA sequence. Moreover, a tweezer-like motion between β2-β3 and 2-β5 loops of N-NTD seems to be a key dynamic feature for selectivity and, consequently, dsRNA melting activity. We also constructed kinetic models for characterizing the melting activity of the dimeric N protein assuming 1:1 and 2:1 (N-NTD:dsRNA) stoichiometries, revealing that only one N-NTD is enough for dsRNA melting.

We calculated the structural model of the N-NTD:dsTRS complex based on the experimental data for the N-NTD interaction with a non-specific dsRNA (5'-CACUGAC-3') (dsNS) (16) using the HADDOCK 2.2 server (20) . The structural restraints of the N-NTD:dsNS complex were defined from CSPs titration performed by Dinesh et al. (2020) (16) . The lowest-energy structure of the N-NTD:dsNS complex from the cluster with the lowest HADDOCK score (fraction common contacts = 0.8 ± 0.1 Å, interface-RMSD = 1.0 ± 0.6 Å, and ligand-RMSD = 2.2 ± 1.2 Å) was used to mutate the dsNS molecule to obtain the TRS sequence (5'-UCUAAAC-3') and, therefore, to generate the N-NTD:dsTRS complex structure. Figure 1A shows the structural model of the N-NTD:dsTRS complex, in which the TRS RNA is inserted in a cleft located between the large protruding β2-β3 loop, named finger, and the central -sheet of N-NTD, referred to as the palm. Analysis of the electrostatic surface potential of N-NTD revealed that the dsRNA-binding pocket is positively charged, with the finger being the highest charged region ( Figure S1 ). This result is consistent with the charge complementarity of the nucleic acid phosphate groups that exhibit negative charge. It is worth mentioning that the orientation of the TRS sense strand in the complex model is in agreement with experimental results described by Keane et al. (2012) for the Murine Hepatitis Virus structural homolog of N-NTD (21) , in which the 5'end of the sense strand binds close to β4-β2-β3 and the 3'-end binds next to β1-β5 ( Figure   1A ).

We performed 25 calculations of 100 ns molecular dynamics (MD) simulations to investigate the stability of the structural models of N-NTD in complex with either dsNS or dsTRS, as well as each of the biomolecules separately (dsNS, dsTRS, and N-NTD). Figure   1B 

To estimate the stability of the Watson-Crick (WC) base-pairing of dsNS and dsTRS complexed with N-NTD, we evaluated the intramolecular hydrogen bonds formed between sense and anti-sense strands of the dsRNA bound to N-NTD. In addition, the hydrogen bonds of the free RNA molecules were investigated as a control parameter. This confirms the consistency of the force field used to describe the studied molecular system. For the N-NTD-bound dsRNAs, we observed a decrease in the overall average number of intramolecular hydrogen bonds for both RNA ligands, accompanied by an 9 increase in respective standard deviations. This increase in standard deviation is due to a significant reduction in the average number of intramolecular hydrogen bonds of particular replicas, specifically 4 for dsTRS (runs 5, 8, 17, and 25) and 2 for dsNS (runs 15 and 23) ( Figure 3A ). The N-NTD-induced reduction in the number of intramolecular hydrogen bonds between the sense and anti-sense strands of dsRNAs was more pronounced for dsTRS than dsNS, as shown by the analysis of score profile in Figure 2 .

In contrast to the decrease in the number of intramolecular hydrogen bonds between the sense and anti-sense strands of dsRNAs (WC base-pairing) due to N-NTD binding, we observed an increase in the average number of intermolecular hydrogen bonds formed between the nitrogenous bases of dsTRS and N-NTD (protein-RNA interaction) along the 100 ns MD simulation, whereas for dsNS, this average value was constant ( Figure 3B top). It is noteworthy that dsNS has more hydrogen bond-forming sites (acceptor and donor) than dsTRS and, in spite of that, the average number of intermolecular hydrogen bonds for the N-NTD:dsTRS complex is higher after 50 ns simulation. Figure 3B Figure S12 ). The structural model of the N-NTD:dsTRS complex presented in Figure 3C suggests a hypothesis for the mechanism 10 of action of N-NTD in which only one domain is capable of destabilizing the RNA duplex, possibly leading to its dissociation and ultimately release of the RNA single strands. Figure 4 ).

Investigation of the stretch, stagger, and shear distances for dsNS and dsTRS showed that the equilibrium population at ~0 Å decreased for both dsRNAs as a result of N-NTD binding. However, this reduction is more drastic for dsTRS than dsNS, as can be seen in the inset for the respective plots in Figure 4 . In addition to this reduction effect, we also verified that N-NTD-bound dsTRS exhibited clear subpopulations at ~1, ~±1.5, and ~3 Å for the stretch, stagger, and shear distances, respectively. However, this destabilization effect is more evident for the N-NTD:dsTRS complex, since the above analysis of angle and distance parameters suggests an impairment of base-pairing planarity accompanied by an increase in the separation between the nitrogenous bases of the complementary dsRNA strands upon N-NTD binding. This result agrees well with the analysis of the intramolecular hydrogen bonds formed between the sense and anti-sense dsRNA strands (see Figure 3A ). It is worth mentioning that, even though base-pairing destabilization was more pronounced for dsTRS than dsNS, dsNS suffered a greater reduction in the RNA duplex twist, as suggested by the N-NTD-induced perturbation of the propeller angles. 12 We also analyzed the population distributions of the local base-pair parameters for the 25 replicas of dsNS and dsTRS in their free and N-NTD-bound states. Figure S13 shows that the WC base-pairing perturbations observed in runs 5, 8, 17, and 25 for dsTRS due to N-NTD binding was also seen for the fully unbiased distribution generated from the 25 replicas. Nevertheless, an opposite effect occurred for dsNS, for which the angle and distance parameters exhibited characteristics of stability and/or slight fluctuations around the equilibrium population. Figure 5C ). An investigation of the motions filtered from the eigenvectors of PC1 and PC2 revealed that dsTRS-bound N-NTD exhibited the largest conformational dynamics when compared to free and dsNSbound N-NTD, which were similar ( Figure 5D and 5E). We highlight that the most evident motions took place in the N-and C-termini as well as the basic finger (β2-β3 loop) for both free and dsRNA-bound N-NTD. However, the eigenvectors of PC1 and PC2 for the N-NTD:dsTRS complex suggested a wide motion between the basic finger and the 2-β5 loop, located at the palm, similar to a tweezer. Interestingly, this tweezer-like motion was intrinsic to the residues located at the dsRNA-binding cleft in N-NTD ( Figure 1A ).

Our results of conformational flexibility from RMSF and PCA for free and dsRNAbound N-NTD corroborated each other and suggest a significant contribution of the N-and C-termini and the basic finger (β2-β3 loop) to N-NTD dynamics. They also revealed that N-NTD interaction with dsTRS led to a general gain in protein conformational flexibility when compared to its free state. We suggest that this flexibility gain of dsTRS-bound N-NTD over 25 replicas of concatenated simulations may be a key structural factor to 14 promote dsTRS WC base-pairing destabilization upon N-NTD binding, as determined by the break of intramolecular hydrogen bonds ( Figure 2B and 3A) and perturbation of the local base-pair parameters (Figure 4 ). Increasing N-NTD concentration led to the dsRNA melting curve, which is characterized by an exponential decay of FRET efficiency as a function of N-NTD concentration. The melting curves reached either zero, for an N-NTD construct that contains the C-terminal serine/arginine (SR)-rich motif, or a plateau, for N-NTD itself (10) .

Since the FRET efficiency is a measure of the molar fraction of dsRNA, in the simulated kinetic models presented here, we report the molar fraction of dsRNA as a 15 function of N-NTD concentration, simulating the dsRNA melting curve. We used the software Kinetiscope (http://hinsberg.net/kinetiscope/), which is based on a stochastic algorithm developed by Bunker (24) and Gillespie (25) . We used the elementary rate constants for individual chemical steps to produce an absolute time base ( Figure 6A ). The starting condition mimics exactly the experimental condition, varying the concentration of N-NTD over 50 nM dsRNA (dsTRS). The predictions were validated by direct comparison to the experimental data (10) .

To simulate the melting curve, we had to constrain the kinetic space, which is large because each model is composed by 6 reactions and 12 individual rate constants, assuming the following boundaries: (B1) the kinetic model must be complete, complying all possible reactions for a given mechanism; (B2) the presence of N-NTD must lead to catalysis, with the melting of dsRNA being faster than the annealing reaction; (B3) the equilibrium of the annealing is shifted toward the dsRNA; and (B4) the equilibrium for the melting activity must be reached in few seconds or less to be efficient in the cellular environment.

The criterion for choosing the rate constants for the annealing reaction (R1, Figure   6 ) was that it must be significantly slower than the melting activity (catalysis). Since to our knowledge, there is no experimental kinetic rate constant available for the annealing of dsTRS, we fixed for the simulations a k off = 8×10 -4 s -1 , which is the experimental value of the dissociation rate constant observed for the almost inactive Y127A N-NTD+SR mutant (10) . This mutant has a melting activity of hours, while our simulation showed melting activities of few seconds ( Figure 6B ). To yield an equilibrium shifted toward the dsRNA, we used k on = 4×10 -1 M -1 s -1 , which is true below the melting temperature of the dsRNA.

Any values of k off < 1 s -1 , with an association constant K a , gives the same molar fraction of dsRNA.

We constrained the binding reactions R2 and R3 of N-NTD to the sense (TRS) and antisense (cTRS) single-stranded RNA (ssRNA) ( Figure 6A ) based on the published experimental values for these association constants (10, 21) . Since these values were very similar, to simplify the simulation, we used the same K a for both reactions (K a = 4×10 7 M -1 ). Note that k on < 10 6 M -1 s -1 makes the reaction too slow to reach equilibrium, violating boundary B4 (Figures S14A and S16B ).

For dsRNA (dsTRS) binding, there was no experimental data to constrain the simulation. However, simulations unambiguously showed that K a for reaction R4 must be of the same order of that for ssRNAs, leading to the allowed ranges depicted in Figure 6A .

We also determined k on based on the simulations, taking boundaries B2 and B4 into consideration, which were also considered for reactions R1 to R3 (Figure S14B, S15A and S16B).

All the constraints applied to reactions R1 to R4 are valid for both kinetic models (models 1 and 2). Conversely, reactions R5 and R6 are specific for each kinetic model, being essential to comply with boundary B1.

For model 1, there is no experimental data available to constrain reactions R5 and R6, but the simulations showed that they are tightly related to reactions R2 and R3, being both K a and k off of the same order of magnitude for reactions R2 and R3 ( Figure S14C ).

Note that there is an intricate relationship between the formation of ssRNA-bound states (C2 and C3) and the decrease of free or bound dsRNA (dsTRS and C4). To illustrate this relationship, Figure 6B shows the kinetics at three concentrations of N-NTD. The simulated melting curves for model 1 resembled the near exponential decay observed experimentally ( Figure 6C, left) . Interestingly, when k off of reactions R5 and R6 were bigger than k off for reactions R2 and R3, we observed a plateau in the exponential decay of the dsRNA melting curve ( Figure 6C ). Remarkably, melting curves that either decayed to zero or reached a plateau was observed experimentally, as mentioned before (10) . It is worth mentioning that the kinetic model 1 is fully compatible with the experimental data by Grossoehme et al.

(2009) (10), as well as with the mechanism suggested by the MD simulations, in which one N-NTD can initiate dsRNA melting, destabilizing the WC base-pairing.

We also evaluated kinetic model 2. This mechanism for N-NTD melting activity was suggested in the conclusion scheme drawn in the paper by Grossoehme To build a kinetic model that would exclusively produce ssRNA from the sandwiched dsRNA, we had to replace reactions R5 and R6 of kinetic model 1. In the new model, reaction R5 forms the sandwiched dsRNA (C5, Figure 6A ) and reaction R6 is the dissociation of C5 into the ssRNA-bound N-NTDs (C2 and C3, Figure 6A ). To simulate N-NTD melting activity considering model 2, we used the same boundaries described earlier (B1, B2, B3 and B4), with reactions R1 to R4 having almost the same constraints described for model 1.

Reaction R5 and R6 of model 2 has no parallel to any other reaction. We scanned all the kinetic space that led to the catalysis of melting activity and observed two contrasting situations. The first is when reaction R6 equilibrium is between 10 -6 and 10 7 M -1 , always having the dissociated forms C2 and C3 available and making the melting curve very stiff (model 2a). The second is the opposite situation, where equilibrium is skilled toward the sandwich state (C5) with K a > 10 7 M -1 (model 2b). Figure 6C illustrates the melting curves obtained for the two situations.

Model 2a is characterized for the high efficiency in the dissociation of the dsRNA, k on and k off can assume any value. Particularly for model 2a, the kinetic of dsRNA melting is also independent of k on for reactions R2 and R3, at fixed concentrations of N-NTD. All simulated conditions led to the curve in red ( Figure 6C ), in which, the minimal amount of N-NTD (10 nM) led to complete dissociation of the dsRNA (molar fraction of zero). Figure   S15 illustrates all the simulated boundaries. Note that for model 2a, there is never an accumulation of C5 ( Figure S15C ).

Model 2b corresponds to when the equilibrium of reaction R6 is shifted toward C5 (K a > 10 7 M -1 ). Figure S16 illustrates the reaction boundaries. In this situation, we were able to observe a melting curve ( Figure 6C , blue) with a near exponential decay at a low concentration of N-NTD and a near exponential rise at higher concentrations of N-NTD. for model 2b. We determined that k on has to be > 10 7 s -1 to keep up with boundaries B2 and B4. For the melting activity to take place, the equilibrium of reaction R5 was shifted toward C5 (K a > 1, for model 2a or 10 7 M -1 for model 2b) (Figure S15B and S16C). 

In the present work, we used computational simulations to unravel the dsRNA melting activity of the isolated SARS-CoV-2 N-NTD. Our molecular dynamics data suggested that, during interaction with dsRNA, protein dynamics drives the destabilization of hydrogen bonds involved in the WC RNA base-pairing, probably in a 1:1 stoichiometry (N-NTD:dsRNA). We also showed that the capacity of N-NTD to break the WC basepairing was sequence-specific, being more efficient for dsTRS (5'-UCUAAAC-3') than for a non-specific (NS) sequence (5'-CACUGAC-3'). To further explore the N-NTD:dsRNA stoichiometry, we constructed kinectic models based on experimental data by Grossoehme et al (2009) (10) . Remarkably, the model using a 1:1 stoichiometry greatly fits the experimental data, reinforcing the mechanism we hypothesize here.

The strategy of performing 25 100 ns-molecular dynamics simulations with the same starting structure but different seeds of the random number generator provided a large sampling of conformational space of each molecular system (N-NTD, dsRNAs, and N-NTD/dsRNA complexes). This set of theoretical data ensured a statistically significant result showing that N-NTD destabilizes the WC base-pairing, especially for dsTRS, through the replacement of intramolecular hydrogen bonds between the dsRNA strands by intermolecular hydrogen bonds between N-NTD and the nitrogenous bases of each RNA strand. The results also revealed unbiasedly that the rigid-body geometric parameters of the WC base-pairing were significantly changed due to N-NTD binding.

One notable N-NTD structural feature is the presence of a significant number of loops; only 32 out of 140 residues are involved in secondary structure (16, 26) . This is a typical feature of a dynamic protein. In fact, our results revealed that N-NTD is a plastic protein, with the N and C-termini and the β2-β3 loop (finger) as the most prominent dynamic regions. For the N-NTD:dsTRS interaction, a remarkable tweezer-like motion 20 between the finger and the 2-β5 loop could be related to the sequence-specific WC basepairing destabilization. This led us to hypothesize that, following formation of the N-NTD:dsTRS complex, the tweezer-like motion resulted from intrinsic protein dynamics might promote a steric effect causing a "compaction pressure" on the dsRNA strands. This might expose residues from the bottom of palm (finger/ 2-β5 cleft) allowing their interaction with the bases, leading to destabilization of the WC base-pairing (Figure 7 ).

To confirm the mechanism emerged from the MD simulations, where only one molecule of N-NTD was enough to initiate dsRNA melting, we constructed kinetic models considering two possible scenarios: a stoichiometry of 1:1 or 2:1 for N-NTD and dsRNA.

The 2:1 stoichiometry is more intuitive since N protein is dimeric in solution (27) . 

To perform the docking, we took advantage of experimental data previously Next, the structural model of the N-NTD:dsTRS (5'-UCUAAAC-3') complex was generated from the lowest-energy structure of the N-NTD:dsNS complex, derived from the cluster with the lowest HADDOCK score, by mutating the dsRNA sequence using w3DNA (29) . Therefore, both complexes have identical geometries, varying only the dsRNA sequences. Structural conformation of the constructed model for N-NTD:dsTRS complex was displayed using the web application http://skmatic.x3dna.org for easy creation of DSSR (Dissecting the Spatial Structure of RNA)-PyMOL schematics (32) .

Molecular dynamics (MD) calculations for N-NTD, dsRNAs, and N-NTD:dsRNA complexes were performed using GROMACS (version 5.0.7) (33) . The molecular systems were modeled with the corrected AMBER14-OL15 package, including the ff14sb protein (34) and ff99bsc0χ OL3 +OL15 nucleic acid (35, 36) force fields, as well as the TIP3P water model (37) . The structural models of N-NTD (PDB 6YI3), dsRNAs (mutated PDB 4U37), and N-NTD:dsRNA complexes (from molecular docking) were placed in the center of a cubic box solvated by a solution of 50 mM NaCl in water. The protonation state of ionizable residues was set according to the PROPKA server (30) considering pH 7.0.

Periodic boundary conditions were used and all simulations were performed in NPT ensemble, keeping the system at 298 K and 1.0 bar using Nose-Hoover thermostat (τ T = 2 ps) and Parrinello-Rahman barostat (τ P = 2 ps and compressibility = 4.510 -5 ·bar -1 ). A 23 cutoff of 12 Å for both Lennard-Jones and Coulomb potentials was used. The long-range electrostatic interactions were calculated using the particle mesh Ewald (PME) algorithm.

In every MD simulation, a time step of 2.0 fs was used and all covalent bonds involving hydrogen atoms were constrained to their equilibrium distance. A conjugate gradient minimization algorithm was used to relax the superposition of atoms generated in the box construction process. Energy minimizations were carried out with steepest descent integrator and conjugate gradient algorithm, using 1,000 kJ·mol -1 ·nm -1 as maximum force criterion. One hundred thousand steps of molecular dynamics were performed for each directly with the N-NTD. We suggest that this activity is a consequence of intrinsic dynamics of N-NTD, especially because the tweezer-like motion between β2-β3 (finger) 34 and 2-β5 loops. The protein is denoted as cartoon with the helix-and β-strand secondary structures colored in cyan and orange, respectively. The dsRNA is showed as a line model with the complementary strands colored in red and blue. The tweezer-like motion between the finger and 2-β5 loop is indicated by bidirectional arrows colored in magenta.

A novel coronavirus from patients with pneumonia in China

SARS-CoV-2 structure and replication characterized by in situ cryo-electron tomography

Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies: Structural genomics approach

Proteolytic processing of polyproteins 1a and 1ab between non-structural proteins 10 and 11/12 of Coronavirus infectious bronchitis virus is dispensable for viral replication in cultured cells

Ultrastructure and Origin of Membrane Vesicles Associated with the Severe Acute Respiratory Syndrome Coronavirus Replication Complex

Unique and conserved features of genome and proteome of SARS-coronavirus, an early split-off from the coronavirus group 2 lineage

Mechanisms and enzymes involved in SARS coronavirus genome expression

Analyses of Coronavirus Assembly Interactions with Interspecies Membrane and Nucleocapsid Protein Chimeras

Coronavirus N Protein N-Terminal Domain (NTD) Specifically Binds the Transcriptional Regulatory Sequence (TRS) and Melts TRS-cTRS RNA Duplexes

The Coronavirus Nucleocapsid Protein Is Dynamically Associated with the Replication-Transcription Complexes

Coronavirus Nucleocapsid Protein Facilitates Template Switching and Is Required for Efficient Transcription

Multiple Nucleic Acid Binding Sites and Intrinsic Disorder of Severe Acute Respiratory Syndrome Coronavirus Nucleocapsid Protein: Implications for Ribonucleocapsid Protein Packaging

The nucleocapsid protein of the SARS coronavirus is capable of self-association through a C-terminal 209 amino acid interaction domain

The coronavirus nucleocapsid is a multifunctional protein

Structural basis of RNA recognition by the SARS-CoV-2 nucleocapsid phosphoprotein

Continuous and Discontinuous RNA Synthesis in Coronaviruses

Sequence Motifs Involved in the Regulation of Discontinuous Coronavirus Subgenomic RNA Synthesis

The SARS coronavirus nucleocapsid protein -Forms and functions

User-Friendly Integrative Modeling of Biomolecular Complexes

Functional Transcriptional Regulatory Sequence (TRS) RNA binding and helix destabilizing determinants of Murine Hepatitis Virus (MHV) Nucleocapsid (N) protein

Structural bioinformatics do_x3dna: a tool to analyze structural fluctuations of dsDNA or dsRNA from molecular dynamics simulations

3DNA: A versatile, integrated software system for the analysis, rebuilding and visualization of three-dimensional nucleic-acid structures

Discrete simulation methods in combustion kinetics

A general method for numerically simulating the stochastic time evolution of coupled chemical reactions

Crystal structure of SARS-CoV-2 nucleocapsid protein RNA binding domain reveals potential unique drug targeting sites

Biochemical 29 characterization of SARS-CoV-2 nucleocapsid protein

Crystal structure studies of RNA duplexes containing s2U:A and s2U:U base Pairs

Web 3DNA 2.0 for the analysis, visualization, and modeling of 3D nucleic acid structures

PROPKA3: Consistent treatment of internal and surface residues in empirical p K a predictions

Refinement of protein structures in explicit solvent

DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL -PubMed

Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB

Refinement of the Sugar-Phosphate Backbone Torsion Beta for AMBER Force Fields Improves the Description of Z-and B-DNA

Refinement of the Cornell et al. Nucleic acids force field based on reference quantum chemical calculations of glycosidic torsion profiles

Comparison of simple potential functions for simulating liquid water

The PyMOL Molecular Graphics System

model of the N-NTD:dsRNA complex and its validation from molecular dynamics simulations. (A) Structural model of the N-NTD:dsTRS complex determined by molecular docking calculations and mutation of dsNS nucleotide sequence

The color of the rectangles corresponds to the nitrogenous base of the dsRNA sense strand, namely A: red, C: yellow, U: cyan, and G: green. The large protruding β2-β3 loop is referred to as the finger. (B) Average RMSD values for dsNS and dsTRS in their free and N-NTD-bound states (top), average RMSD values for N-NTD in its free and dsRNA-bound state (middle), and average number of contacts between N-NTD and dsRNA atoms

The authors declare that no conflict of interest exists.