Computational Insights into the Structural Dynamics of MDA5 Variants Associated with Aicardi–Goutières Syndrome and Singleton–Merten Syndrome

Melanoma differentiation-associated protein 5 (MDA5) is a crucial RIG-I-like receptor RNA helicase enzyme encoded by IFIH1 in humans. Single nucleotide polymorphisms in the IFIH1 results in fatal genetic disorders such as Aicardi–Goutières syndrome and Singleton–Merten syndrome, and in increased risk of type I diabetes in humans. In this study, we chose four different amino acid substitutions of the MDA5 protein responsible for genetic disorders: MDA5L372F, MDA5A452T, MDA5R779H, and MDA5R822Q and analyzed their structural and functional relationships using molecular dynamic simulations. Our results suggest that the mutated complexes are relatively more stable than the wild-type MDA5. The radius of gyration, interaction energies, and intra-hydrogen bond analysis indicated the stability of mutated complexes over the wild type, especially MDA5L372F and MDA5R822Q. The dominant motions exhibited by the wild-type and mutant complexes varied significantly. Moreover, the betweenness centrality of the wild-type and mutant complexes showed shared residues for intra-signal propagation. The observed results indicate that the mutations lead to a gain of function, as reported in previous studies, due to increased interaction energies and stability between RNA and MDA5 in mutated complexes. These findings are expected to deepen our understanding of MDA5 variants and may assist in the development of relevant therapeutics against the disorders.


Introduction
Pattern recognition receptors (PRR) are essential for the initiation of innate immune responses, which initially discriminate self-and non-self-components within the organism, thereby regulating responses [1,2]. An example is the retinoic acid-inducible gene 1 (RIG-1)-like receptor, which plays a crucial role in viral infection by sensing viral RNA and initiating/regulating antiviral immune responses [3]. RIG-1-like receptor family comprises of three members: RIG-I, melanoma differentiation-associated protein 5 (MDA5), and laboratory of genetics and physiology 2 [4]. Among these, RIG-1 and MDA5 exhibit similar domain organization and transduce downstream signaling through a common adaptor called mitochondrial antiviral signaling protein. Upon binding to viral RNA, further signaling cascades occur, leading to the production of IFN1 through several signaling intermediates, such as IκB kinase epsilon (IKKε) and TANK Binding Kinase 1 (TBK1) [5][6][7].
RIG-I and MDA5 share similar structural homology, including two repeats of the N-terminal caspase activation recruitment domains, a central DEAD-like or DEAH-like (DExD/H) helicase domain (where x can be any amino acid), and a cysteine-rich C-terminal domain (CTD) [6,8]. The caspase activation recruitment domain interacts with mitochondrial antiviral signaling protein and RNA via the helicase domain. These receptors differ I.
To determine the stability of the protein after single amino acid mutations, I-Mutant 2.0 [29] was used (https://folding.biofold.org/i-mutant//pages/dbMut.html, accessed on 1 June 2021). I-Mutant 2.0 is based on the support vector machine algorithm and can predict the stability of a protein correctly in 80% of cases [29]. The server provides free energy change (DDG) values as a regression estimator and signs of stability change wherein mutated structures with DDG values > 0 are considered to have increased stability and structures with DDG <0 have decreased stability. The functional impact of a mutation was analyzed using the Protein Variation Effect Analyzer, which predicts the impact of a mutation on the biological function of a protein [30]. The server filters sequence variants to identify non-synonymous variants that are functionally important. According to the server, the default threshold score is fixed at −2.5 and variants with scores equal to or below the threshold value are considered deleterious. Likewise, variants with scores above −2.5 are considered neutral.

Preparation of dsRNA-Bound MDA5 Wild Type and Mutant Complexes
MDA5-dsRNA complex structures have been previously solved through crystallography [8]. However, the crystal structures exist with several missing residues, particularly in the loop regions. Hence, to build the missing residues and prepare the MDA5-dsRNA bound complex, we used a dsRNA-bound MDA5 crystal structure (4GL2). The missing residues were built using a Swiss modeler (https://swissmodel.expasy.org/, accessed on 28 April 2020), and then the loops were further refined (only built loops) using mod loop refinement [31,32]. The length of the MDA5 structure considered was 707 residues ranging from 307-1013. As our objective was to assess the structural stability and binding capability of dsRNA upon mutation, we removed the ADPNP molecule from the structure and considered the complex as wild-type MDA5 (MDA5 WT ). To construct mutants, we replaced leucine at position 372 with phenylalanine (MDA5 L372F ), alanine at position 452 with threonine (MDA5 A452T ), arginine at position 779 with histidine (MDA5 R779H ), and arginine at position 822 with glutamine (MDA5 R822Q ) using PyMOL with probable rotamers [33]. Finally, stereochemical properties were checked using the ProQ webserver (https://proq.bioinfo.se/ProQ/ProQ.html, accessed on 14 May 2020) [34], and structural geometries were cleaned for any steric clashes using the Discovery Studio visualizer (Dassault Systems, BIOVIA, San Diego, CA, USA) [35].

Molecular Dynamic Simulations
To assess the structural dynamics of all the five systems (MDA5 WT , MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q ), we employed all-atom molecular dynamic simulations using Gromacs 5.1.4 [36,37], as reported in our previous study [38]. The wild-type and mutated complexes were first placed in a dodecahedron box with water molecules, and the distance between the protein surface and box wall was set at 1.2 nm. The Amber-ff99SB-ILDN force field was used for both the protein and dsRNA. Energy minimization was performed using the steepest gradient method with a maximum tolerance of 1000 kJ/mol. Subsequently, equilibration with the NVT and NPT ensemble was performed for 100 ps and 500 ps, respectively, with restraints. Temperature (modified Brendenson thermostat) and pressure (Parrinello-Rahman barostat) couplings were used at 300 K and 1.0 bar for the equilibrations. Finally, production simulations were performed for 200 ns without restraints. Electrostatic interactions such as short-and long-range interactions were applied with 1.0 nm. The Particle Mesh Ewald algorithm was used to maintain the long-range interactions and grid spacing of 0.16 nm for FFT with fourth-order cubic interpolation. A 2 fs time step was used for the simulations, and the 2 ps data were saved for the entire trajectory. Finally, trajectory analysis was performed using the GROMACS analysis tools, and plots were generated using Excel.

PCA and Free Energy Landscape (FEL) Analyses
The concerted motions of structures are essential for their biological functions, and to study the motions of the structures in this study, PCA was conducted for backbone atoms using 50 ps coordinates from the last 100 ns of each complex trajectory. Rotational and translational motions were removed accordingly, and the covariance matrix was computed. From the covariance matrix, the eigenvalues and eigenvectors were analyzed using the gmx anaeig tool. The principal components (PC) with the largest motions were then selected and plotted for comparison. The comparison using the PCs provides the main information about the spread of the datapoints in the phase space, which indicates the global motion of the protein during simulation. To study FEL, the gmx sham tool was used to combine the data points of the reaction coordinates of the PCs with the largest motions. FEL plots were drawn using Mathematica (version 12).

Residue Network Analysis
The impact of mutations on the structure of a protein can be understood in detail by using residue interaction networks. Residue networks were constructed using the NAPS webserver (http://bioinf.iiit.ac.in/NAPS/, accessed on 15 March 2021) [39] for the representative structures of both the wild-type and mutant MDA5 complexes with a non-weighted edge and a Cα-Cα pair maximum threshold of 7 Å. From this analysis, the betweenness centrality (C B ) values, which may be functionally important for signal transduction within the protein, were determined. C B values play a vital role in suggesting residues that are crucial for protein function. The value indicates the centrality of the node in a residue network. It is calculated by the number of shortest paths from between vertices that pass through a node. Basically, it quantifies the number of times a node can act as a bridge along the paths of any two other nodes. Hence, the high C B value of a particular node (residue) may have an impact on the structural-functional relationship. This allows us to understand the importance of residues involved in long range communications in a protein.

Conservation, Mutant Stability and Functional Analyses
The Consurf server analysis suggested that L372 and R822 are largely conserved with a scale of 9, R779 is moderately conserved with a scale of 6, and A452 is variably conserved with a scale of 3 ( Figure 1A). These results indicate that L372, R779, and R822 may influence MDA5 function when substituted. Hence, the substitution of L372, A452, R779, and R822 with F, T, H, and Q are associated with AGS and Singleton-Merten syndrome [22][23][24]. We further explored the mutant stability and functional analyses using the I-Mutant 2.0 server. The modelled structures of MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q were submitted to the server and analyzed for DDG values; the values for these variants were −1.13, −0.83, −0.71, and −1.76, respectively. Structures with DDG values < 0 are predicted to be less stable when compared to the wild type, while structures with DDG values > 0 are predicted to be stable. All the structures had decreased stability when compared to the wild type; however, MDA5 A452T and MDA5 R779H were relatively stable when compared to the other two mutant complexes. Protein Variation Effect analysis of the mutated sequences yielded interesting results, where MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q scored 0.62, −0.825, −2.591 and −3.785, respectively. According to the server, values less than −2.5 are considered deleterious, whereas values greater than or equal to −2.5 are considered non-deleterious. Based on the data, both MDA5 L372F and MDA5 A452T mutants were considered functionally neutral, while mutant complexes MDA5 R779H and MDA5 R822Q were predicted to be deleterious.
> 0 are predicted to be stable. All the structures had decreased stability when compared to the wild type; however, MDA5 A452T and MDA5 R779H were relatively stable when compared to the other two mutant complexes. Protein Variation Effect analysis of the mutated sequences yielded interesting results, where MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q scored 0.62, −0.825, −2.591 and −3.785, respectively. According to the server, values less than −2.5 are considered deleterious, whereas values greater than or equal to −2.5 are considered non-deleterious. Based on the data, both MDA5 L372F and MDA5 A452T mutants were considered functionally neutral, while mutant complexes MDA5 R779H and MDA5 R822Q were predicted to be deleterious.

Structural Dynamics of Wild-Type and Mutant MDA5-dsRNA Bound Complexes
At first, we prepared the MDA5 wild-type and mutant complexes ( Figure 1B) and subsequently subjected to MD simulations. From the MD trajectory of all the complexes, the root mean square deviation (RMSD) of the protein backbone suggested that the wild-

Structural Dynamics of Wild-Type and Mutant MDA5-dsRNA Bound Complexes
At first, we prepared the MDA5 wild-type and mutant complexes ( Figure 1B) and subsequently subjected to MD simulations. From the MD trajectory of all the complexes, the root mean square deviation (RMSD) of the protein backbone suggested that the wild-type complex (MDA5 WT ) was stable after 50 ns simulations, in contrast to the mutant complexes. In particular, the RMSD of mutant MDA5 A452T ranged from 0.5 to 0.8 nm throughout the simulation period, while other complexes showed less variation ( Figure 2). The average RMSD values of MDA5 WT , MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q were 0.4206 nm, 0.5222 nm, 0.6089 nm, 0.5186 nm, and 0.5255 nm, respectively. Moreover, we assessed the RMSD of dsRNA and found it to be highly stable during the simulation. The average RMSD values of the dsRNA complexed with MDA5 WT , MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q were 0.2527 nm, 0.216 nm, 0.2357 nm, 0.1938 nm, and 0.2078 nm, respectively. The RMSD results indicated that both native and mutant complexes were stable during the simulations (Figure 2).

Hydrogen Bonds and Interaction Energies
Hydrogen bonds play a crucial role in understanding the binding affinity of RNA towards MDA5. Hence, we used the gmx hbond GROMACS tool to assess the hydrogen bond interactions between dsRNA and MDA5 for all complexes during the simulations. We observed an average of 25 hydrogen bonds in the wild-type complex, whereas in mutant complexes, hydrogen bonds slightly increased during the simulations. In particular, MDA5 L372F and MDA5 R822Q showed an average of 35 hydrogen bonds between dsRNA and protein ( Figure 3). Intra-hydrogen bonds for the proteins were also evaluated to determine whether there were any structural interferences in terms of hydrogen bonds. The wild type had 525 intra H-bonds, whereas the mutated complexes MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q had 529, 524, 536, and 541 hydrogen bonds, respectively. MDA5 R779H and MDA5 R822Q had more intra-hydrogen bonds than the other mutated complexes. This might be the reason for their stable RMSD, Rg, and SASA values, where the increased intra-hydrogen bonds stabilized the mutated complexes. Further, we performed interaction energy analysis using the re-run option in Gromacs and determined that the wild type exhibited less interaction energy than that exhibited by the mutants (Figure 3). The analyses indicated higher binding affinities between dsRNA and MDA5 variants than that between dsRNA and MDA5 wild-type. Interaction energy was calculated as the sum of the Coloumb-SR and Lennard Jones-SR. For the MDA5 complexes, interaction energies were −1997 kJ/mol, −2612 kJ/mol, −2003 kJ/mol, −2102 kJ/mol, and −2803 kJ/mol for MDA5 WT , MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q , respectively. The overall summary of the trajectories analysis of MDA5 WT , MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q is given in Table 1. These results suggest that MDA5 variants may induce structural alterations, thereby leading to a gain of function as reported previously. higher Rg values than that of the wild type, and the remaining two mutant complexes had relatively less compacted folding. These results are consistent with the predictions by I-Mutant 2.0, where MDA5 A452T and MDA5 R779H were predicted to be stable. Previous reports have suggested that point mutations induce structural alterations that eventually affect the functional properties of a protein [40,41]. To further study the fluctuations with respect to residues in the MDA5 protein, we assessed the root mean square fluctuations (RMSF) for all complexes during the simulations. The largest fluctuations were observed at the C-terminal region of Hel2i in all the complexes. Apart from Hel2i, the region between the wild type and mutants in the central region of Hel1 also exhibited large variations. The other regions, such as the Pincer and C-terminal domains, were stable throughout the simulation. The solvent-accessible surface area (SASA) of the wild-type and mutated complexes was calculated using the gmx sasa GROMACS tool to determine whether there was any reduction in solvent accessibility to different domains ( Figure S1). The SASA values for MDA5 WT , MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q were 385 nm 2 , 377 nm 2 , 384 nm 2 , 383 nm 2 , and 383 nm 2 , respectively. Although other complexes had similar SASA values, MDA5 L372F showed both reduced SASA and Rg values. This might be due to the substitution of leucine with phenylalanine, a bulkier group, thereby leading to either a pi-alkyl bond between F372 and L368 or a pi-pi interaction between F372 and F377. In addition, L372 is located adjacent to the ATP binding pocket of MDA5, and substitution at this position might inhibit the hydrolysis of ATP. The analyses also further indicated that there were structural variations among the mutant complexes, in contrast to the wild type.

Hydrogen Bonds and Interaction Energies
Hydrogen bonds play a crucial role in understanding the binding affinity of RNA towards MDA5. Hence, we used the gmx hbond GROMACS tool to assess the hydrogen bond interactions between dsRNA and MDA5 for all complexes during the simulations. We observed an average of 25 hydrogen bonds in the wild-type complex, whereas in mutant complexes, hydrogen bonds slightly increased during the simulations. In particular, MDA5 L372F and MDA5 R822Q showed an average of 35 hydrogen bonds between dsRNA and protein (Figure 3). Intra-hydrogen bonds for the proteins were also evaluated to determine whether there were any structural interferences in terms of hydrogen bonds. The wild type had 525 intra H-bonds, whereas the mutated complexes MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q had 529, 524, 536, and 541 hydrogen bonds, respectively. MDA5 R779H and MDA5 R822Q had more intra-hydrogen bonds than the other mutated complexes. This might be the reason for their stable RMSD, Rg, and SASA values, where the increased intra-hydrogen bonds stabilized the mutated complexes. Further, we performed interaction energy analysis using the re-run option in Gromacs and determined that the wild type exhibited less interaction energy than that exhibited by the mutants (Figure 3). The analyses indicated higher binding affinities between dsRNA and MDA5 variants than that between dsRNA and MDA5 wild-type. Interaction energy was calculated as the sum of the Coloumb-SR and Lennard Jones-SR. For the MDA5 complexes, interaction energies were −1997 kJ/mol, −2612 kJ/mol, −2003 kJ/mol, −2102 kJ/mol, and −2803 kJ/mol for MDA5 WT , MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q , respectively. The overall summary of the trajectories analysis of MDA5 WT , MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q is given in Table 1. These results suggest that MDA5 variants may induce structural alterations, thereby leading to a gain of function as reported previously.

Principal Component Analysis
To understand the global motions during simulations and the intrinsic dynamics of the complex, we performed PCA on the MDA5 wild type and mutants. The simulation trajectories suggested that the first 30 PCs greatly contributed to the collective motions of the proteins. Moreover, the largest motions were observed in the first three PCs, with cumulative percentages of 60%, 50%, 65%, 45%, and 65% for MDA5 WT , MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q , respectively (Figure 4). The projections for PC1 and PC2, PC2 and PC3, and PC1 and PC3 are shown in Figure S2. The projections for PC1 and PC2 showed that all the complexes were largely spread in the phase with small energy barriers, except for MDA5 R822Q and MDA5 A452T . However, for the PC2 and PC3 projections, a similar spread in the phase space was observed, which strongly indicated less global motion. Again, the projections for PC1 and PC3 revealed a similar spread with a small energy barrier ( Figure S2). To understand the local motion in the complexes, we constructed porcupine plots for PC1 using a filter option for all trajectories with 50 frames. We observed that the variations were restricted mainly to the Hel1, Hel2i, and Pincer regions of the mutants, in contrast to the wild type ( Figure 4). Furthermore, FEL analysis with PC1 against PC2 strongly suggested the presence of a transition occurring during the simulations with small energy barriers. In particular, MDA5 WT showed three clusters in a basin, whereas the mutants did not exhibit large transitions, except for MDA5 R779H and MDA5 R822Q ( Figure 5). These two mutants (MDA5 R779H and MDA5 R822Q ) exhibited large transitions, thereby indicating the possibility of large structural alterations in these complexes. These findings suggest that global motions were compromised upon the substitution of the conserved residues.

Residue Network Analysis
We performed residue network analysis, and the results showed crucial structural rearrangements in mutants in contrast to the wild-type protein. We used the NAPS webserver and constructed a residue-to-residue network with a cut-off distance of 0.7 nm. The CB (betweenness centrality) was also computed, as it is important to understand intramo-

Residue Network Analysis
We performed residue network analysis, and the results showed crucial structural rearrangements in mutants in contrast to the wild-type protein. We used the NAPS webserver and constructed a residue-to-residue network with a cut-off distance of 0.7 nm. The C B (betweenness centrality) was also computed, as it is important to understand intramolecular signaling flow. The study used these conditions, which showed the variation between the wild type and mutant, thereby presenting insights into the variations in mutants. A high C B value node is typically associated with the function of the complex. Considering this, we calculated the C B for all the complexes (Figure 6), further, we analyzed the C B difference (C Bd ) between MDA5 wild-type and mutants, using the condition |C B (MDA5 WT ) − C B (MDA5 mutants )| ≥ 0.2 and mapped the respective residues on the structure to understand the difference between the wild type and mutated complexes. We observed the similar distribution of crucial residues among the mutant complexes and they shared approximately 50% of the residues. This result suggests that the intra residue signaling varies in the MDA5 mutant complexes compared to the MDA5 wild-type, thereby influencing the functional mechanism. (Figure 6 and Table 3). Notably, the residues around the central region of the MDA5 accommodating the dsRNA may play a crucial role in conformational changes in the mutants. However, these residues require further investigation. The variations in C B indicate how point mutations can induce allosteric changes in the protein.  Figure 1B. Table 3. The table represents the residue numbers from MDA5 mutants for which the CBd between MDA5 wild-type and mutants is 0.2 or more. Bold type distinguishes the residues common for two or more mutants. 418, 447, 449, 451, 453, 492, 616, 617, 618, 621, 622, 625, 725, 730, 733, 790, 791, 792, 793 368, 451, 615, 621, 622, 625, 626, 634, 636, 733, 791, 792, 793, 794, 795, 806, 812 449, 451, 455, 459, 492, 544, 546, 612, 616, 618, 621, 625, 628, 632, 636, 639,  674, 677, 730, 731, 733, 734, 735, 737, 790, 791, 792, 794, 795, 796, 803, 812, 814, 815

Discussion
AGS and Singleton-Merten syndrome are two genetic disorders characterized by several physical defects. These disorders are caused by a single nucleotide polymorphism in the IFIH1 , which codes for MDA5. MDA5 is a crucial PRR that is involved in innate immunity by recognizing viral RNA. The variants of MDA5 are associated with several diseases in human. In particular, L372F, A452T, R779H and R822Q have been associated with AGS and Singleton-Merten syndrome and recent studies have suggested that these  Figure 1B. Table 3. The table represents the residue numbers from MDA5 mutants for which the C Bd between MDA5 wild-type and mutants is 0.2 or more. Bold type distinguishes the residues common for two or more mutants. 447, 449, 451, 453, 492, 616, 617, 618, 621, 622, 625, 725, 730, 733, 790, 791, 792, 793, 794, 795, 808, 810, 812, 814, 884, 886, 887, 888

Discussion
AGS and Singleton-Merten syndrome are two genetic disorders characterized by several physical defects. These disorders are caused by a single nucleotide polymorphism in the IFIH1 , which codes for MDA5. MDA5 is a crucial PRR that is involved in innate immunity by recognizing viral RNA. The variants of MDA5 are associated with several diseases in human. In particular, L372F, A452T, R779H and R822Q have been associated with AGS and Singleton-Merten syndrome and recent studies have suggested that these variants result in gain of function, thereby leading to the disease [22][23][24]. A recent report on cryo-EM structures solved for MDA5 filaments showed that mutations of the filament-forming residues can alter the function [25]. In addition, the chicken MDA5 dimer structure (2MDA5 and one dsRNA) suggested Head-Head configuration upon binding to short/long dsRNA, thereby forming a filament [26]. Several computational studies have previously reported the use of molecular dynamics' simulations to understand the dynamic nature as well as the structure-function relationship of the proteins and ligand bound complexes [38,[40][41][42][43][44]. Hence, in order to understand the reason behind the gain of function associated with MDA5 variants, we performed long-range simulations to identify the structural changes that may influence the functional properties of MDA5 in this study. Five different systems were selected for the study: MDA5 WT , MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q .
We mainly used traditional analyses such as RMSD, Rg, RMSF, and hydrogen bond analysis, which altogether suggest that structural variations occur upon mutation in MDA5. From stability and functional analyses using web servers, it was predicted that MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q were relatively less stable than the wild type, and MDA5 R779H and MDA5 R822Q were predicted to be deleterious. Although the RMSD values of the backbone of the complexes were higher than that of the wild type, the RMSDs of dsRNA chains were stable throughout the simulation. Rg analysis showed the structural compactness of the complexes, especially MDA5 L372F . The MDA5 L372F SASA values were also reduced, corroborating the Rg analysis, which might be due to the pi-alkyl or pi-pi interaction of the substituted phenylalanine residue. In addition, MDA5 R822Q displayed increased stability in terms of RMSD, Rg, SASA, and intra-hydrogen bonds compared to the other complexes. Therefore, we hypothesize that residue substitution might provide adequate stability for the MDA5 protein to gain function. The MDA5 A452T complex had the highest relative instability with respect to the RMSD of the backbone, increased Rg, and increased SASA values. Interestingly, when the interaction energies were analyzed, the mutated complexes had higher interaction energies than that of MDA5 WT in the following order: MDA5 R822Q > MDA5 L372F > MDA5 R779H > MDA5 A452T . These results corroborated another trajectory analysis showing that the MDA5 mutated complexes were more stable than the wild type. Further, the variation in the dominant motions of MDA5 wild-type and mutant complexes suggest that large structural modifications occur to alter the intrinsic dynamics within the complexes (Figure 4) In addition, the betweenness centrality (C B ) indicates the distribution of crucial residues in the complexes; in particular, C B differences between MDA5 wild-type and mutants suggest the similar distribution of functionally important residues within the complexes ( Figure 6 and Table 3). Observed shared residues (~50%) among the mutant complexes in crucial domains of MDA5 indicate that these domains may undergo conformational changes, and also suggest the impact on the stability between dsRNA and MDA5. As a result, these mutations may lead to the syndromes through gain of function. MDA5 forms a long filament complex upon binding to dsRNA and subsequently undergoes RNA-dependent ATP hydrolyzation; hence, it is necessary to perform microsecond simulations along with the ATP-bound state. Although our study is limited to 200 ns simulations, our findings may help deepen the understanding of the mechanistic properties of wild-type and mutant MDA5 upon binding to dsRNA, as well as provide information for basic research on the impact of MDA5 variants that are involved in several diseases This study also provides insights into the general thought that point mutations induce conformational changes that might regulate the function of a protein.

Conclusions
MDA5 is an important PRR coded by IFIH1, and the variants of MDA5 studied in this work has been previously reported to result in gain of function. The gain of function from the MDA5 variants leads to diseases such as Singleton-Merten syndrome and Aicardi-Goutières syndrome in humans. Therapeutics for these syndromes may be defined and designed if the molecular properties and dynamic nature of dsRNA-bound MDA5 mutant complexes are well understood. Therefore, to decipher the conformational changes that occur upon mutations of MDA5, we simulated and analyzed the trajectories of wild-type and four mutated dsRNA-bound MDA5 complexes. The all-atom MD simulation of MDA5 wild-type and mutated complexes (MDA5 L372F , MDA5 A452T , MDA5 R779H , and MDA5 R822Q ) revealed increased stability of the mutated complexes when compared to MDA5 wildtype. The hydrogen bond interactions between MDA5 and the dsRNA were also higher in the mutant complexes than the MDA5 WT . The varied global motions in wild-type and mutant complexes suggest that point mutations induced certain conformational changes. In addition, the betweenness centrality of the residue networks suggested the crucial residues that might be responsible for the functional variation in the MDA5 mutant complexes compared to wild-type. From these results, we assume that MDA5 variants induce specific structural changes in order to increase the dsRNA-MDA5 binding affinity, which may lead to further signaling and associated diseases.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/biom11081251/s1, Figure S1: Solvent accessible surface area, Figure S2: Projection of principal components on to the phase space.

Data Availability Statement:
The raw data supporting the conclusions of this study are available on request from the corresponding author. The data are not publicly available due to larger in size.