Predicted 3D model of the M protein of Porcine Epidemic Diarrhea Virus and analysis of its immunogenic potential.

The membrane protein M of the Porcine Epidemic Diarrhea Virus (PEDV) is the most abundant component of the viral envelope. The M protein plays a central role in the morphogenesis and assembly of the virus through protein interactions of the M-M, M-Spike (S) and M-nucleocapsid (N) type. The M protein is known to induce protective antibodies in pigs and to participate in the antagonistic response of the cellular antiviral system coordinated by the type I and type III interferon pathways. The 3D structure of the PEDV M protein is still unknown. The present work exposes a predicted 3D model of the M protein generated using the Robetta protocol. The M protein model is organized into a transmembrane and a globular region. The obtained 3D model of the PEDV M protein was compared with 3D models of the SARS-CoV-2 M protein created using neural networks and with initial machine learning-based models created using trRosetta. The 3D model of the present study predicted four linear B-cell epitopes (RSVNASSGTG and KHGDYSAVSNPSALT peptides are noteworthy), six discontinuous B-cell epitopes, forty weak binding and fourteen strong binding T-cell epitopes in the CV777 M protein. A high degree of conservation of the epitopes predicted in the PEDV M protein was observed among different PEDV strains isolated in different countries. The data suggest that the M protein could be a potential candidate for the development of new treatments or strategies that activate protective cellular mechanisms against viral diseases.

Introduction

The Porcine Epidemic Diarrhea Virus (PEDV) is the causative agent of porcine epidemic diarrhea, a highly contagious disease that affects farm animals. The disease is diagnosed by clinical symptomatology such as vomiting, diarrhea, dehydration, and anorexia. The infection affects pigs of all ages, with piglets less than one week old being the most affected (100% morbidity and mortality). Although the virus does not affect human health, it causes significant economic losses to the pig breeding industry [1]. Current treatment is primarily aimed at diarrhea controlling. Control strategies have been oriented to the establishment in farms of rigorous biosecurity programs, adequate zootechnical management and vaccination [2]. Although these strategies have been effective in controlling disease, specific treatments are needed, which would require molecular knowledge of each stage of the viral replication cycle, as well as information on the biochemical characteristics of all the viral proteins. PEDV belongs to the order Nidovirales, family Coronaviridae, subfamily Coronavirinae, genus Alphacoronavirus. Its genome is a single-stranded, positive-sense RNA molecule that encodes 16 non-structural proteins (nsp 1–16) and 5 structural proteins, S, M, N, E and ORF3 [2]. Coronavirus M proteins share the same structural characteristics: three TM domains with N-exo-C-endo orientation flanked by a short glycosylated NH2-terminal domain on the virion surface and a long carboxy-terminal globular domain within the virion [3, 4]. The 3D structure of the PEDV M protein is still unknown. The functions described for the PEDV M protein include assembly and budding of virions, inhibition of innate immune response by blocking type I and III interferon pathways in infected cells and induction of protective antibodies in pigs [5-9]. The M protein performs its functions by interacting with other viral proteins. It is known that M proteins interact with each other and with spike and nucleocapsid proteins during virus assembly. Interestingly, M-M interactions form the overall scaffold for the viral envelope [3]. The indispensable role played by the M protein in the viral replication cycle can be explained in part by the lower genetic variability of the M gene compared to the S gene [10]. Therefore, the PEDV M protein could be a good candidate for developing specific antiviral drugs and as a target for vaccine design. The three-dimensional structure of the PEDV M protein has not been reported yet. The structure of some crystallized proteins of PEDV can be found in the PDB database, including the S protein [11], 3CLpro protease [12], NSP9 [13], papain-like protease (PDB: 6NOZ) and NSP1 (PDB 5XBC). Due to the importance of the M protein in the viral infection cycle, the present work proposes a 3D model of the M protein generated using the Robetta protocol. An immunoinformatics analysis was also carried out to determine the potential antigenic regions of the M protein.

Material and methods

Sequences

For the generation of the 3D model of the M protein, the sequence of the M protein of the PEDV CV777 virulent strain (Uniprot ID: P59771) (226 residues) and the M protein from strain PEDV/MEX/MICH/01/2013 (GenBank number: MH006957.1) isolated in Michoacán, Mexico, called 2013MMV were used. Different sequences of the M protein were retrieved from the NCBI Taxonomy Browser page (https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi). The sequence of the SARS-CoV-2 M protein of the AlphaFold and Feig lab models corresponds to Uniprot ID: P0DTC5.

Cloning sequence of the M gen and phylogenetic analyses

The nucleotide sequence of the 2013MMV was subcloned in the vector pPICZαB from the vector pETSumo and it was called 2013MMV. The recombinant DNA was sequenced, 225 amino acids were obtained by in silico translation, Met residue number 1 belongs to the cloning vector. Phylogenetic analyses were conducted in MEGA7 using the Neighbor-joining method [14, 15] using 87 sequences including the two amino acid sequences aforementioned and others from different strains isolated in the United States, Europe and Asia. Evolutionary distances were calculated using the Poisson correction method [16] and are shown as the number of amino acid substitutions per site.

Programs used to generate 3D protein models and evaluation

The 3D model of the PEDV M protein was generated using Robetta (comparative modeling). CAMEO project (https://www.cameo3d.org/) [17, 18]. The 3D models generated in Robetta were constructed from several templates by Dali server (http://ekhidna2.biocenter.helsinki.fi/dali/) [19]. The 3D M protein models were placed in the membrane using the OPM (https://opm.phar.umich.edu/) by PPM server (http://opm.phar.umich.edu/server.php) to infer its spatial position in the membrane [20]. Images were visualized with FirstGlance in Jmol by PPM server [21]. The quality of the generated models was evaluated using the online servers of QMEAN and QMEANDisCo (https://swissmodel.expasy.org/qmean) [22, 23], PROCHECK (https://saves.mbi.ucla.edu/) [24], MolProbity (http://molprobity.manchester.ac.uk/) [25] and ProSA-web (https://prosa.services.came.sbg.ac.at/prosa.php) [26].

SARS-CoV-2 M protein models

The 3D models of the SARS-CoV-2 M protein made by Feig lab (https://feig.bch.msu.edu/) and AlphaFold (https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19) were used to compare the characteristics of these models with the PEDV M protein model generated by Robetta.

Superimposition of 3D protein models

The superimposition of 3D models and estimation of RMSD values was carried out using the UCSF Chimera program (https://www.cgl.ucsf.edu/chimera/) [27].

Continuous and discontinuous B-cell epitope prediction

Continuous epitopes in the PEDV M protein were identified using the tools of Immune Data Base (https://www.iedb.org/), Bepipred Linear Epitope Prediction 1.0 and 2.0 http://tools.iedb.org/bcell/). Bepipred 2.0 [28] was used with a threshold of 0.5 (sensitivity = 0.58564, specificity = 0.57158). The Bepipred 1.0 Linear Epitope Prediction tool [29] was used with a threshold of 0.35 (sensitivity = 0.49, specificity = 0.75). The predicted epitopes were evaluated using Chou & Fasman Beta-Turn Prediction, Emini Surface Accessibility Prediction, Kolaskar and Tongaonkar Antigenicity, and Parker Hydrophilicity Prediction with a threshold of 1.0. The conservation analysis was carried out using the IEDB Epitope Conservancy Analysis tool (http://tools.iedb.org/conservancy/) [30]. Discontinuous B-cell epitopes were predicted using the online servers DiscoTope (http://tools.iedb.org/discotope/) [31] and ElliPro (http://tools.iedb.org/ellipro/) [32]. ElliPro with a minimum score of 0.5 and a maximum distance of 6 Ǻ. Regarding DiscoTope (version 1.1), a threshold of -7.7 (sensitivity = 0.47, specificity = 0.75) was used.

Prediction of cytotoxic T-cell epitopes

Cytotoxic T-cell epitopes were identified using the NetMHCpan 4.1 server [33]. The swine leucocyte alleles (SLA-1:0101; SLA-1:0401; SLA-1:0801) were used, those alleles are widely distributed in swine populations [34, 35]. The search for peptides covered all nonamers, using 0.5% as a threshold for strong binding and 2% for weak binding.

Results

Conservation of the amino acid sequence of PEDV M proteins

To generate the 3D model of PEDV M protein, the amino acid sequence was selected based on a comparison of amino acid sequences from different strains using the M protein sequence of the CV777 prototype virulent strain as a reference. A high degree of conservation was observed among 87 PEDV strains from Mexico, United States of America, Europe, and Asia. The identity between the different sequences analyzed was recorded in the range of 97.35 to 99.56% corresponding to the minimum and maximum identity values related to the CV777 M protein. The amino acid sequences of M protein were clustered into three groups related to G2a, G2b, G1 strains of PEDV (Fig 1). Thus, the high identity value between the compared sequences allowed the selection of the M protein of the PEDV CV777 strain (called M protein CV777 in this work, UniProt ID P59771) to build the 3D model.

Fig 1

Phylogenetic tree built with 87 sequences of the M protein of the PEDV from Mexico, United States of America, Europe and Asia.

The amino acid sequences of M protein were clustered into three groups related to G2a, G2b, G1 strains of PEDV. The arrows signal the two sequences chosen to generate the 3D model of the M protein. The evolutionary history was inferred using the Neighbor-Joining method. Details and references are provided in Material and Methods section.

3D modelling of the PEDV M protein

The predicted 3D model of the PEDV M protein was done using Robetta server on the CAMEO project (Continuous Automated Model EvaluatiOn) [18] using the RosettaCM comparative modeling [36]. The model 1 of the CV777 M protein was selected based on its QMEAN value of -0.86 and its predicted protein architecture, including the transmembrane and globular domains. Also, a 3D model of the M protein from PEDV/MEX/MICH/01/2013 (GenBank number: MH006957.1) called 2013MMV was predicted in this work. Like the CV777 strain, the model 1 was selected for the further analysis. 2013MMV is a Mexican strain isolated in 2013 in Michoacán, México; it has 6 amino acid differences compared to the CV777 M protein, is one of the strains isolated in cell culture that we keep under guard and that was genetically characterized in its entirety by our research group. Fig 2A and 2B show the predicted 3D models of the CV777 M protein and the 2013MMV M protein, respectively. Both models have a short N-terminal ectodomain, three α-helices corresponding to the three successive transmembrane domains, and a long globular C-terminal domain mainly formed by antiparallel β-strands on the inside of the virion. The 3D models generated in Robetta were constructed from various 3D structures deposited in the PDB and were selected by the Dali server, with the SARS-CoV-2 protein ORF3 (6xdc) showing the greatest structural similarity. Then, the 3D models of the CV777 and 2013MMV M proteins were superimposed using the UCSF Chimera program. The main differences were observed in the last 26 amino acids of the 2013MMV M protein on the globular C-terminal domain, which were folded in different spatial orientations. The RMSD value was 12.195 Å (Fig 2C). The theoretical positioning of the CV777 M protein (Fig 2D) and 2013MMV M protein (Fig 2E) in a lipid bilayer was analyzed using the PPM server in OPM. Both 3D M protein models were included in the membrane by three alpha helical transmembrane segments. The general positioning and orientation of both proteins in the lipid bilayer is maintained, but the spatial orientation of the 2013MMV M protein changes in the extracellular region, in the arrangement of the alpha helices in the transmembrane as well as in the globular region. To the 3D model of CV777 M protein, PPM server calculated a value of depth/hydrophobic thickness of 18.3 ± 2.0 Å with a ΔG to the membrane of -15.1 kcal/mol and a tilt angle of 13±7°. Similarly, a value of depth/hydrophobic thickness of 20.8 ±1.9 Å, with a ΔG of -18.8 kcal/mol and tilt angle de 25±0° was calculated for the 3D model of 2013MMV M protein.

Fig 2

Predicted 3D models of the CV777 M and 2013MMV M proteins, generated with Robetta.

A) CV777 M protein model. B) 2013MMV M protein model. The transmembrane region is shown in red. The regions outside the viral membrane are shown in yellow. The globular region is shown in green. The blue arrows point to the N-terminal end and the white arrows point to the C-terminal end. C) Overlapping of the 3D models of CV777 M and 2013MMV M proteins. The red areas correspond to the highest RMSD value, while the green areas correspond to the lowest RMSD value. The white arrows indicate the sites with the highest RMSD, corresponding to the loops and to the N- and C-terminal ends. D) 3D model of CV777 M protein on a theoretical lipid layer obtained from the OPM server. The program added red pseudoatoms to mark the extracellular hydrophobic boundary of the lipid bilayer. E) 3D model of 2013MMV M protein on a theoretical lipid bilayer obtained from the OPM server. In this case, the server added red and blue pseudoatoms to mark the extracellular and intracellular hydrophobic boundaries of the lipid bilayer, respectively.

Validation of the predicted 3D model of the M protein

The predicted 3D models of the CV777 M protein and the 2013MMV M protein were evaluated with different programs. QMEANDisCo (a composite scoring function to derive the absolute quality of the model) values were 0.59 and 0.57, respectively. Global score must be in a range must be in a range between 0 and 1 with one being good. The PROCHECK program determined that the models of CV777 M protein and 2013MM M protein had values of 89.2% and 88.1% of phi and psi angles in the most favored regions of the Ramachandran plot, respectively. According to the PROCHECK server, a good model must contain more than 90% of the phi and psi angles in the most favored regions. But both values are close to 90%. The evaluation of the 3D models of CV777 M protein and 2013MMV M protein in MolProbity yielded scores of 1.34 and 1.37 respectively, indicating an acceptable stereochemistry. Finally, the 3D models were evaluated in ProSA web, CV777 M protein model (Fig 3A and 3B) and the 2013MMV M protein model (Fig 3C and 3D) had Z (overall quality score) values of -5.26 and -5.88, respectively (Fig 3A and 3C). Z-score of the 3D models of M protein is within the range of scores typically found for native proteins of 225 and 226 amino acid residues in length deposited in the PDB. In the plot of residue scores (Knowledge-based energy) of the 3D models of M protein, the 100 residues of the transmembrane region at the N-terminus have the highest energy level. But none of the amino acids had high positive values and were not penalized by the program (Fig 3B and 3D).

Fig 3

Evaluation of the overall model quality of 3D models of the CV777 M protein and 2013MMV M protein using ProSA web.

A) Z-score plot of the 3D model of CV777 M protein. B) Plot of residue scores (Knowledge-based energy) of the 3D model of CV777 M protein. C) Z-score plot of the 3D model of 2013MMV M protein. D) Plot of residue scores of the 2013MMV M protein. The black dot in A and C represents the 3D model of the M protein.

Comparison of predicted M protein 3D models from PEDV and SARS-CoV-2 M protein models

As a result of the SARS-CoV-2 pandemic, DeepMind (AlphaFold) [37, 38] and Michael Feig’s laboratory (Feig lab) [39] generated 3D models of the M protein of the SARS-CoV-2 virus. AlphaFold uses a neural network to predict the distances between pairs of residues in a protein [37]. Feig lab generated initial machine learning-based models using trRosetta. The models were then subjected to molecular-dynamics simulation-based refinement protocol to maximize model accuracy [39]. Although, SARS-CoV-2 M protein has 33% amino acid sequence identity with CV777 M protein and 94% coverage percentage. 3D models of SARS-CoV-2 and PEDV M proteins displayed a similar global folding (S1 Fig). The SARS-CoV-2 M protein models were evaluated with the programs used with the PEDV M proteins. Then, the results were compared with those obtained from 3D models of the CV777 and 2013MMV M proteins generated by Robetta (S1 Table). The quality parameters of the PEDV M protein models was comparable with those of the validated models from SARS-CoV-2 M protein. To analyze the differences in the theoretical 3D structure of the M proteins, the models of the M proteins from PEDV and SARS-CoV-2 were superimposed. The RMSD values between the CV777 M protein and SARS-CoV-2 M protein from AlphaFold (S1A Fig) and the CV777 M protein and SARS-CoV-2 M protein from Feig lab models were 6.0 and 14.1 Å, respectively (S1B Fig). The RMSD values between 3D model of 2013MMV M protein with the SARS-CoV-2 M protein model from AlphaFold (S1C Fig) and with SARS-CoV-2 M protein model from Feig lab (S1D Fig) were 8.120 and 15.716 Å, respectively. While the superimposition of the SARS-CoV-2 M protein models from AlphaFold and Feig lab showed an RMSD of 12.6 Å (S1E Fig). The main difference between all the models was observed in the α-helix on the transmembrane region and the spatial arrangement of the globular region. The data obtained demonstrate that the 3D models of CV777 and 2013MMV M proteins generated by Robetta have good overall quality similar to the quality of the models of the SARS-CoV-2 M protein obtained by AlphaFold and Feig lab.

Identification of linear B cell epitopes in the PEDV M protein model

The epitopes in the predicted 3D models of the PEDV M protein were analyzed. The B cell epitopes of the CV777 M and 2013MMV M proteins were predicted using servers BepiPred 1.0 (linear epitope prediction) and Bepipred 2.0 (sequential B cell epitope predictor). Both programs predicted four peptides in the CV777 M protein model; the B cell peptides RSVNASSGTG and KHGDYSAVSNPSAVLT were predicted by both servers. The two peptides were above the threshold level of 1.0 (Table 1). In the 2013MMV M protein model, 3 and 4 peptides were predicted by both servers, respectively. The KHGDYSAVSNPSSVLTD epitope was above the threshold level of 1.0 and was predicted by both servers. The peptides identified in the 3D models of the PEDV M proteins were used to perform a conservation analysis (%) with 31 M protein sequences from different PEDV strains isolated in Mexico and different strains isolated in the United States and other countries. A high level of conservation of the epitopes identified in the M proteins of 2013MMV and CV777 was observed among the analyzed sequences. However, the sequences of some epitopes from the 2013MMV M protein were more highly conserved than those from the CV777 M protein (Table 1).

Table 1

Continuous B-cell epitopes of CV777 M protein.

Continuous B-cell epitopes	Position	Length	Chou	Emini	Karplus	Kolaskar	Parker	Conservation %	Identity % Minimum-maximum
CV777 M protein model
Bepipred 1.0
MSNGSIPV	1–8	8	1.217	1.000	1.065	0.999	2.264	90.80	37.50–100.00
SFNPETDAL	111–119	9	1.108	1.000	1.051	0.951	3.781	100.00	100.00–100.00
RSVNASSGTG	185–194	10	1.207	1.000	1.038	0.993	4.561	94.25	90.00–100.00
KHGDYSAVSNPSAVLT	202–217	16	1.133	1.000	1.004	1.057	2.986	16.09	93.75–100.00
Bepipred 2.0
SIPVDEVIEHLRNWNF	5–20	16	0.863	1.00	0.973	1.046	0.497	12.64	56.25–100.00
YKVATGVQVSQL	154–165	12	0.910	1.000	0.989	1.106	1.960	100.00	100.00–100.00
GRVGRSVNASSGTG	181–194	14	1.142	1.000	1.026	1.011	3.807	93.10	92.86–100.00
KHGDYSAVSNPSAVLTDSEKV	202–222	21	1.078	1.000	1.006	1.055	2.883	14.94	90.48–100.00
2013MMV M protein model
Bepipred 1.0
SFNPETDAL	110–118	9	1.108	1.000	1.051	0.951	3.781	100.00	100.00–100.00
RSVNASSGTG	184–193	10	1.207	1.000	1.038	0.993	4.561	94.25	90.00–100.00
KHGDYSAVSNPSSVLTD	201–217	17	1.172	1.000	1.019	1.056	3.169	83.91	94.12–100.00
Bepipred 2.0
VDEVIQHLRNWNF	7–19	13	0.891	1.00	0.952	1.042	0.086	78.16	61.54–100.00
YKVATGVQVSQL	153–164	12	0.910	1.000	0.989	1.106	1.960	100.00	100.00–100.00
RVGRSVNASSGTG	181–193	13	1.153	1.000	1.026	1.007	3.965	94.25	92.31–100.00
KHGDYSAVSNPSSVLTDSEKV	201–221	21	1.129	1.000	1.023	1.052	3.176	82.76	90.48–100.00

Gray shading indicates epitopes with values above 1 in all parameters.

Prediction of discontinuous B-cell epitopes from the PEDV M protein model

The prediction of discontinuous epitopes from M protein 3D models from PEDV and SARS-CoV-2 was performed using the ElliPro and DiscoTope servers. ElliPro predicted 6 epitopes in the CV777 M protein model, 4 in the 2013MMV M protein model, 4 in the SARS-CoV2 M protein model from Alpha fold and 5 in the SARS-CoV2 M protein model from Feig lab (Fig 4) (S2 Table). The DiscoTope program predicted 15 residues in the CV777 model and 14 residues in the 2013MMV M protein model with potential to form an epitope, while 14 and 24 residues were predicted in the SARS-CoV-2 M protein model from AlphaFold and the SARS- CoV-2 M protein model from Feig lab, respectively (Fig 5) (S3 Table). In the Figs 4 and 5, the predicted epitopes were located in protein regions with similar folding. In both figures the colored regions correspond to discontinuous epitopes, ordered from the highest (red) to the lowest score (orange).

Fig 4

Discontinuous epitopes predicted in ElliPro from CV777, 2013MMV and SARS-CoV-2 M protein models.

A) CV777 M protein. B) 2013MMV M protein. C) SARS-CoV-2 M protein from AlphaFold. D) SARS-CoV-2 M protein from Feig lab. Predicted discontinuous epitopes are highlighted in the 3D model with different colors to represent the score. The epitope with the highest score is represented in red up to the lowest value in orange. The score decreases from the epitopes marked in green, blue, yellow, and pink. Arrows with the same color indicate discontinuous epitopes located in equivalent regions in the models. Colored residues in each epitope are indicated in S2 Table.

Fig 5

Discontinuous epitopes predicted in DiscoTope from CV777 and 2013MMV M protein and SARS-CoV-2 protein M models.

A) CV777 M protein. B) 2013MMV M protein. C) SARS-CoV-2 M protein from AlphaFold. D) SARS-CoV-2 M protein from Feig lab. Predicted discontinuous epitopes are highlighted in the 3D model with different colors to represent the score. The epitope with the highest score is represented in red up to the lowest value in orange. The score decreases from the epitopes marked in green, blue, yellow and pink. Arrows with the same color indicate discontinuous epitopes located in equivalent regions in the models. An additional color (light blue) show residues that were not predicted in ElliPro. Colored residues in each epitope are indicated in S3 Table.

Prediction of T-cell epitopes for the PEDV M protein

Prediction of T-cell epitopes was performed using the NetMHCpan 4.1 server (DTU Bioinformatics). Three alleles were used for the prediction, SLA-1: 0101, SLA-1: 0401 and SLA-1:0801. In the CV777 M protein, 40 weak-binding and 14 strong-binding peptides were predicted (Table 2). In the 2013MMV M protein, 36 weak-binding and 16 strong-binding peptides were predicted. The immunogenicity of the strong binding peptides of both M proteins was predicted using the IEDB class 1 immunogenicity server, which yielded 10 epitopes, 2 of them repeated in different alleles. The study provides theoretical data on the potential of the M protein to be considered as an immunogen in the development of a new vaccine for the control of PEDV.

Table 2

Continuous T cell epitopes of CV777 and 2013MMV M protein.

Allele SLA-1:0101
Weak binding peptides	%Rank EL	%Rank BA	Affinity Nm	Strong binding peptides	%Rank EL	%Rank BA	Affinity Nm	Class 1 immunogenicity
GSIPVDEVI	1.379	1.825	7527.49	PVDEVIEHL	0.135	1.608	7076.59	0.37683
ILWPLVLAL**	1.278	1.294	6391.21	LGAPTGVTL**	0.165	0.384	3530.72	0.12876
SLFDAWASF**	0.795	0.464	3876.20	APTGVTLTL**	0.360	1.183	6126.86	0.13676
SFNPETDAL**	0.938	2.037	7930.56	SQLPNFVTV**	0.055	0.048	1310.84	0.14666
TDALLTTSV**	1.285	1.210	6194.39	PVDEVIQHL*	0.105	1.758	7386.78	0.19457
QVSQLPNFV**	1.499	2.900	9428.25	AVSNPSAVL	0.218	0.411	3653.72	-0.1696
TVAKATTTI**	0.505	0.761	4916.88	SAVSNPSSV*	0.476	0.682	4683.27	-0.40609
ASSGTGWAF**	0.787	0.490	3991.62	AVSNPSSVL*	0.245	0.746	4875.56	-0.34224
YSAVSNPSA	1.404	0.869	5261.96
SAVSNPSAV	0.621	0.580	4323.47
AVLTDSEKV	1.561	2.739	9167.12
VLTDSEKVL	1.988	8.192	15472.46
TDSEKVLHL**	0.946	8.053	15354.38
DSEKVLHLV**	1.600	5.414	12759.76
SAFLYGVKM*	1.695	2.275	8362.81
SVLTDSEKV*	1.383	2.249	8314.10
VLTDSEKVL*	1.988	8.192	15472.46
Allele SLA-1:0401
Weak binding peptides	%Rank EL	%Rank BA	Affinity Nm	Strong binding peptides	%Rank EL	%Rank BA	Affinity Nm	Class 1 immunogenicity
PVDEVIEHL	1.470	4.053	4846.79	VLQYGHYKY**	0.211	0.270	238.88	-0.10699
EVIEHLRNW	1.011	1.352	1420.59	SLFDAWASF**	0.189	0.164	148.60	0.2429
TILLVVLQY**	0.972	2.606	3009.49	ASSGTGWAF**	0.226	0.126	110.35	0.2599
SVFLYGVKM	1.859	2.498	2882.55	AVSNPSSVL*	0.481	0.763	751.51	-0.34224
ASFQVNWVF**	1.431	1.567	1700.35
QVNWVFFAF**	1.889	0.978	987.97
RLWRRTHSW**	0.689	0.582	552.91
LTLLSGTLF	0.953	0.933	936.80
TVAKATTTI**	1.186	1.485	1599.22
SSGTGWAFY**	0.863	0.382	339.85
AVSNPSAVL	0.553	0.692	679.83
LTDSEKVLH**	0.794	1.551	1682.18
VQVSQLPNF*	1.454	2.016	2249.84
PVDEVIQHL*	0.726	2.470	2842.62
EVIQHLRNW*	1.597	2.071	2314.73
Allele SLA-1:0801
Weak binding peptides	%Rank EL	%Rank BA	Affinity Nm	Strong binding peptides	%Rank EL	%Rank BA	Affinity Nm	Class 1 immunogenicity
EVIEHLRNW	0.837	1.907	6253.17	TILLVVLQY**	0.409	1.346	4685.44	-0.1274
HLRNWNFTW**	1.228	0.856	3057.97	VLQYGHYKY**	0.126	0.214	850.54	-0.10699
LVVLQYGHY**	1.508	0.767	2751.72	SLFDAWASF**	0.039	0.011	117.99	0.24329
SVFLYGVKM	0.944	1.175	4166.01	RLWRRTHSW**	0.451	0.445	1647.27	0.14156
ILWPLVLAL**	1.053	0.874	3124.42	VQVSQLPNF**	0.441	0.678	2445.83	-0.28945
ASFQVNWVF**	0.827	0.776	2780.60	ASSGTGWAF**	0.322	0.188	762.6	0.2599
QVNWVFFAF**	1.312	0.513	1889.30
TLMLWIMYF**	1.126	0.569	2071.38
LTLLSGTLF	0.969	0.649	2342.52
SQLPNFVTV**	0.864	1.650	5612.88
AKATTTIVY**	0.599	1.520	5225.76
SSGTGWAFY**	1.186	0.635	2296.34
YVRSKHGDY**	1.263	1.539	5282.61
AVSNPSAVL	0.702	1.251	4399.64
SAFLYGVKM*	1.087	1.256	4413.65
AVSNPSSVL*	0.645	1.440	4974.67
EVIQHLRNW*	1.293	2.977	8876.16
IQHLRNWNF*	1.807	2.405	7434.73

* Unique epitopes in 2013MMV M protein.

** Epitopes that appear in CV777 and 2013MMV M protein.

Discussion

The porcine industry is affected each year by the loss of pigs due to viral infections, with porcine epidemic diarrhea being one of the most worrying diseases for producers. The disease, which is highly contagious, is caused by PEDV. PEDV is an etiological agent associated with infection outbreaks throughout the world, including Mexico. Although this disease is dealt with using biosecurity programs, adequate zootechnical management practices and vaccines, the occurrence of mutations in the viral genome motivates the continuous search for new control strategies. This requires molecular knowledge of the potential viral triggers of the host’s immune response. The M protein fulfills several important viral functions in different regions of the viral particle structure. Knowledge of these functions could help develop new strategies to control the disease. However, structure of the M protein of PEDV is unknown. Thus, the present work generated a 3D model of the M protein and compared it to validated models of SARS-CoV-2. The potential regions inducing an immune response were evaluated in the 3D model; 4 promising sites were found that should be subjected to further study. The 3D model was constructed based on the amino acid sequence of the M protein of CV777 and on that of a Mexican strain isolated in Michoacán, Mexico, showing changes of amino acids compared to the prototype. This could help to determine if the 3D structure of the protein is significantly affected when changes in the amino acid sequence occur, especially in the globular region of the protein where the residues associated with protein functions are found. To confirm that the amino acid sequences selected to obtain the 3D model represent the M protein of PEDV, a comparison was made with strains from different outbreaks around the world. The sequences were clustered into three groups related to G2a, G2b, G1 strains of PEDV. The analysis included the genogroups reported for the PEDV. There are different genogroups of PEDV, G1 and G2 (classic strain and variant strains), which can be divided into G1a and G1b, G2a and G2b. The G1a genogroup includes classic strains such as the virulent CV777 prototype strain and other ancient PEDV strains. The G1b genogroup contains strains isolated in Asia with deletions in different genes, mainly in the genes encoding to the nsp3 and ORF3 proteins [40]. Genogroup G2 differs from G1 mainly due to amino acid changes in the NTD domain of protein S. Within the genogroup G2a there are circulating Asian strains reported in reemerging cases of PEDV and in the genogroup G2b there are circulating strains related to outbreaks in the United States and other parts of the world [41]. The data obtained support the construction of the 3D model from the sequences of CV777 strain and the Mexican sequence. The present work proposes for the first time a predicted 3D model of the M protein of PEDV. The model was generated by Robetta using comparative modelling, using the ORF3 protein of SARS-CoV-2 as one of the main modeling templates. The structure of ORF3 (PDB: 6XDC) was solved in 2021 using Cryo-EM and has the same topology in the globular endodomain M as the PEDV M protein [42]. The 3D model of the M protein shown in Fig 2 shows a topology consistent with the architecture reported for the coronavirus M protein. It is organized into two transmembrane and globular domains, with 3 α-helices that would cross the membrane, with loop regions inside and outside the viral envelope and a globular domain formed mainly by antiparallel β-strands. Interestingly, the 3D model preserves the topology reported in Uniprot for the CV777 M protein: virion surface (positions 1–11), transmembrane (12–32, 21 residues of length), intravirion (33–41, 9 residues), transmembrane (42–62, 21 residues), virion surface (63–75, 13 residues), transmembrane (76–96, 21 residues), intravirion (97–226, 130 residues) (https://www.uniprot.org/uniprot/P59771).

Analysis of the positioning in the lipid bilayer of the theoretical 3D structure of M proteins by the PPM server showed the overall conservation of the theoretical topology, but the amino acid changes in the 2013MMV M protein change the spatial arrangement in the extracellular region, the tilt angle of the transmembrane alpha helices and the globular region. The PPM server added red and blue pseudoatoms to mark the extracellular and cytoplasmic hydrophobic boundaries of the lipid bilayer, respectively (Fig 2D and 2E). Although PPM was only able to calculate both membrane limits in the positioning of the 2013MMV M protein but not in the CV777 M protein, in the last one only the extracellular limit was calculated. This interesting finding will be analyzed later by in silico and experimental studies to evaluate the potential effect on the recognized functions of the M protein in the conformation of the virion and on some processes in cells infected by PEDV.

The M protein is the most abundant structural protein in the viral envelope of PEDV and other coronaviruses and it is known to play a central role in the assembly of the virus. Homodimerization of the M protein through the transmembrane region has been reported [43]. In Mouse Hepatitis Virus (MHV), the interaction of the M protein with the E protein is the minimum requirement for the assembly of the viral particle [43], Kuo and Masters reported an interaction between the M and N proteins of MHV through the C-terminal end of the M protein [44]. Interestingly, it was shown that, in the absence of the N protein, the MHV M protein can interact with synthetic RNAs containing a viral packaging signal that favors the specific packaging of RNAs in coronavirus-like particles [45]. A recent study showed that the PEDV M protein can interact with S and E viral proteins, with ORF3 and with 218 host proteins. Some of them are associated with 131 signaling pathways and 10 biological processes [46]. The M protein interacts with the translation initiation factor 3 (eIF3L) whose downregulation significantly increases viral production. The translation initiation factor 3 could act as a negative regulator of PEDV replication [46]. It has been shown that the M protein of three coronaviruses, SARS, MHV and the feline coronavirus (FCoV), adopts 2 conformations, called long and compact, that influence the curvature of the viral envelope. The long conformation (M-long) provides rigidity and uniformity to the viral envelope [47]. It has also been demonstrated that the M protein forms homodimers through contact with region corresponding to the endodomain, and that the M-long conformation interacts mainly with N and S proteins in a stoichiometry of the 8M2: 4N: 1S3 type [47]. The multimerization of the M protein is a conserved process among coronaviruses [47]. Recently, Lim Heo and Michael Feig generated models of homodimers of the M protein, including 4 homodimers of the M protein interacting with a trimer in the transmembrane region of the S protein of SARS-CoV-2. The models are deposited in GitHub (https://github.com/feiglab/sars-cov-2-proteins) [39]. Knowing the interactions of the M protein, as well as knowing its 3D structure, or even having a good 3D model thereof, would allow making a deep analysis of the intracellular processes this protein carries out, which would in turn allow the design of drugs and other alternatives that could stop the M protein from performing its functions as a method for controlling infections caused by PEDV and other coronaviruses. The lack of efficient control strategies to reduce the spread of the PEDV and, therefore, the death of piglets, keeps alive the interest in the development of new control methods. However, the constantly changing viral genome makes difficult to achieve this goal. Different studies have reported the existence of different recombination events between PEDV strains [48-51]. A recombination event between a PEDV strain and one of the transmissible gastroenteritis virus (TGEV) was reported in Russia and Italy [52, 53]. A recombination event between a highly virulent PEDV strain and an attenuated PEDV strain has also been reported; this event generated a new virulent strain [54]. Developing an efficient control strategy has been hindered by the occurrence of mutations and recombinations among PEDV strains in the coding region of protein S. Therefore, it is important to study the immunological and functional potential of other structural proteins of the virus. The M protein is a good candidate due to its low genetic variability, its structure, and the functions it might perform. It is particularly interesting to analyze the potential of the M protein to induce the activation of the immune system. It has been shown that in other coronaviruses, such as MHV, the immune system produces monoclonal antibodies directed to the M protein; these antibodies protect mice from viral infection [55]. The immunization of 8 kittens with the recombinant M protein of the feline infectious peritonitis virus (FIPV) allowed 3 of them to survive the viral infection [56]. B-cell epitopes have also been found in the M protein of SARS-CoV as well as the avian infectious bronchitis virus [57, 58]. It has also been experimentally demonstrated that some peptides of the SARS-CoV M protein are highly reactive with sera from the convalescent phase of SARS patients. High titers of antibodies against the M protein were also elicited in immunized mice and rabbits, which suggests that the M protein is a highly immunogenic component in inactivated SARS vaccine preparations [59]. An experimental study identified a B cell epitope in the PEDV M protein that corresponds to the 195WAFYVR200 motif [8] covering the C-terminus of M protein. In the present study, the prediction of B cell epitopes of the M protein of PEDV was carried out and although there is agreement in the results for most of the programs used, it is noteworthy that the epitope 195WAFYVR200 found by Zhang [8] was not predicted. Some epitopes in the globular region at the C-terminus of M protein were located before or after the 195WAFYVR200 sequence. Further research of the immunogenic potential of the M protein could involve expressing the M protein of coronaviruses in eukaryotic and prokaryotic systems to analyze possible changes in the conformation of the protein. It would also be necessary to analyze the contact regions of the M protein with the antibodies produced by pigs immunized with the PEDV M protein [8].

The in silico results of the present study suggest that the M protein is a potential candidate for the activation of the cellular immune response against to the PEDV infection. Finally, a high degree of conservation of each of the B and T cell epitopes predicted in this study was also demonstrated by comparing different strains of PEDV circulating in Mexico and other parts of the world.

Conclusion

Predicted 3D models of the CV777 M and 2013MMV M proteins were obtained. In silico identification of continuous and discontinuous B cell epitopes and T cell epitopes on the M protein was also performed. The comparison between the 3D models of PEDV M proteins obtained in this study and the M protein models from SARS-CoV-2 obtained by the AlphaFold group and the Feig lab showed a high conservation of the 3D structure in both viruses, as well as the presence of discontinuous epitopes in the same regions in both proteins. The 3D model of the PEDV M protein generated in the present study could be a useful tool to carry out further research on the development of vaccines or drugs to control PED.

Superimposition of the 3D M protein models from PEDV and SARS-CoV-2.

(PDF)

Click here for additional data file.

Comparison of the quality of the 3D models of the M proteins from PEDV and SARS-CoV-2.

(PDF)

Click here for additional data file.

Prediction of discontinuous epitopes with ElliPro in the 3D M protein models.

(PDF)

Click here for additional data file.

Prediction of discontinuous epitopes with DiscoTope in the 3D M protein models.

(PDF)

Click here for additional data file.

31 Aug 2021

PONE-D-21-16531

Predicted 3D model of the M protein of Porcine Epidemic Diarrhea Virus and analysis of its immunogenic potential

PLOS ONE

Dear Dr. Rosas-Murrieta,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The reviewers raised important points about the manuscript. All points must be addressed in the point-by-point rebuttal letter and in most cases should be incorporated in the manuscript itself. In particular, Reviewer 2 raises a point about the intrinsic inaccuracy of the prediction of T cell epitopes. The authors must provide comments about the limitations of the approach and how it affects the validity of the prediction. As suggested, shwoing a consensus prediction may be helpful. This reviewer also stresses out the validity of Autodock Vina in analyzing protein-peptide interactions. Again, the authors must address the limitations of this approach and provide the explanations required by this reviewer (assigning binding energy, standard deviations and reference test) in the manuscript. The authors should also comment on the suggestion from Reviewer 1 of modeling the M protein in membrane.

In addition to the reviewer comments. a list of comments from the Academic Editor is added below. Please address these points in the manuscript as well. In particular, care must be taken to correct the referencing system, since the current numbering of references in the text and in the bibliography do not match. The accuracy of the references could not be evaluated and will be reviewed in the revised manuscript. In addition, because PLOS One does not provide copy-editing, it is important to revise the manuscript for clarity and typographic errors. I listed below a number of changes to consider in a revised manuscript to improve clarity.

Academic Editor's comments:

1) The referencing system is inaccurate and references numbers in the text do not match references listed in the bibliography. In the text, references are numbered in order of appearance, while in the list, references are numbered alphabetically. Thus numbers do not match. Please use the PLOS One recommended format from Instructions to authors:  References are listed at the end of the manuscript and numbered in the order that they appear in the text. In the text, cite the reference number in square brackets (e.g., “We used the techniques developed by our colleagues [19] to analyze the data”).

When revising the references, please make sure the formatting is consistent. In particular the use of Title Capitalization is inconsistent (e.g .refs 11 and 27, maybe others)

2) Legend of figure 1 contains unformatted references. Due to potential formatting issues, I do not recommend placing references in figure legends. It is better to provide the detailed methodological description in the material and methods section. In the legend, provide a simplified description of the approach and indicate “see material and methods for details and references”.

3) There appears to be a discrepancy between the QMEAN values cited in the text (L131) and in Table 1.  The text mentions -14.90 but the table mentions -14.60. Please correct or clarify.

There is another discrepancy of numbers between line 91 and 336 (percent identity). Please carefully revise numerical data since such discrepancies may negatively reflect on the study.

4) Ensure that figure legends are complete:

- Figure legend 1: indicate the significance of arrows. The text (L93) refers to three groups, clarify how these groups are identifiable from the tree.

- Figure legend 2: although panel C is described in a subsequent paragraph, the complete legend for figure 2 should be kept together (L189-192 added at L157)

5) Ensure that all figures have a sufficient resolution so that they are easily readable when magnified.

6) Typographic errors and minor changes:

L33: … virus (PEDV) is …

L38: interferon pathways

L91: 97.35-99.56%

L95: as a reference

L124: It is clear

L132: remove period before parenthesis

Table S6: (Excl. Gly and Pro)

Table S6: define asterisks

L146: It is mentioned that the most important template was ORF3. It’s not clear how “importance” is defined and evaluated. Pleas clarify. If the different models had different wight in generating the models, it should be indicated in the table.

Figure S7A: for consistency use “Model 1 of 2013MMV M protein”

L207: reference needed for both approaches

Figure S8: provide significance of arrows and colors in legend

L215: 33% amino acid sequence identity

Table S9: PROCHECK

L243: B cell

Table S10: first Row: Parker, Conservation. Hyphenate or adjust column width.

Table S10: 2013MMV first peptide. Position cannot be 110-112.

Figure 5: it would be helpful if the colored residues were identified on the models

Table S13: Remove rows in Spanish for 0401 and 0801.

Table S13: the title of the table refers to epitopes of CV777, but is contains a number of epitopes unique to 2013MMV (*). Modify the title of the table so that it better reflects its content.

Table 3: it’s not clear to me why there is a range of identity. Please clarify.

L304: the data do not really address the “feasibility” od a candidate vaccine, rather, it suggest the potential of the M protein for the development of a candidate vaccine. Please clarify.

Figure 6: since the first B cell epitope is colored in red  inf figure s 4 and 5, it is confusing to color Tcell epitope also in red in figure 6. Make sure that the two types of epitopes are clearly differentiated in this figure.

Figure 6: for consistency and simplification, use the 2013MMV abbreviation rather than the full name

L319: practices

L320: triggers of the host’s

L324: PEDV was unknown

L326: the potential regions inducing an immune response

L328: constructed

L333-335: unclear sentence. Please rephrase

L336: 99.11% identity

L336: the identity numbers are different from the same sentence in line 91. Please explain or correct.

L348: beta-strands

L349: residues in length

L358: remove reference labeled by author names

L362: … 218 host proteins [reference needed]

L363, L368, L372: [reference also needed for these statements]

L376: verify the link (space in the link suggest it is incorrect)

L400: reference format [31] [32] should be [31-32]

L405: reference for Zhang paper

L406: “last figure of the article” unclear which article this refers to. The last figure of this manuscript does not offer a comparison with other epitopes. The discussion comparing the present data and previously known M protein epitopes must be detailed and clarified (L398-399 and L407)

L413: the docking data do not confirm that these epitopes can bind MHC. The data suggest that the epitope can bind MHC. Binding has to be demonstrated before being confirmed.

L416: do you mean “demonstrated by comparing different strains”? please clarify.

L420: B cell and T cell not hyphenated

L420: this sentence could be interpreted that the epitopes were identified functionally. Indicate that these are proposed epitopes identified in silico.

L425: Unclear: “showed to be” appears incorrect. Do you mean “will be”?

L435: corresponds

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Academic Editor

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Reviewer #1: Yes

Reviewer #2: Partly

**********

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Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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5. Review Comments to the Author

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Reviewer #1: Rodriguez-Enrigquez et al report a molecular modeling study of the M protein of PEDV. The homology model was build, the potential epitopes were predicted and the binding energy between the putative epitopes and the swine MHC-I were calculated. Because the experimental structure is not available yet, this study provides valuable structural information on this important protein. I would recommend this study if the authors address the following minor points:

1). Is there the M-gene knockout study to decipher the precise biological role of the M protein? Any literature evidence to support that the M protein is a good therapeutic target?

2). The M protein structure model should be placed in the membrane. The author can try OPM server (https://opm.phar.umich.edu/).

Reviewer #2: The manuscript reports a study aimed at predicting the 3D structure of the PEDV M protein. Then, the model has been utilized to derive its immunogenic profile using in-silico approaches.

In principle, the manuscript is an interesting demonstration of the usefulness of the application of in-silico methods to characterize the structure and function of a protein. However, in my opinion, there are several flaws that need to be fixed.

In general, the manuscript is difficult to read and should be reorganized and shortened. Only relevant information should be reported. For example, several paragraphs in the Materials and methods can be merged.

Major points:

line 93: authors claim that three groups have been identified in the tree. This is not clear from Fig. 1 . Please, explain.

Line 107. ambiguous positions: not very clear. Please, explain

Lines 112-126: all these data appear not strictly necessary. The paraghraph may be shortened

line 128: Please add a reference for the CAMEO project

line 138: it is not clear how the new PEDV sequence has been selected for modelling

line 149: The Table is not essential.

line 188. Fig. 2 legend is split. Is that correct?

line 194. Table 2 can be moved to the Supplementary data

line 287: Prediction of T-cell epitopes for the PEDV M protein: these methods are intrinsically inaccurate. Indeed, different prediction are obtained for different models of the same protein (Fig. 4), It may be interesting to show the consensus prediction.

Also, peptide docking is problematic. Indeed, Autodock Vina has not been specifically conceived for protein-peptide docking. Authors should explain better how they assigned binding energies (Table 3). A standard deviation should be calculated for each energy to account for the stocastic nature of the Vina algorithm. As a reference test, they should also calculate the binding energy of peptides not predicted to bind the SLA-01:0401 allele.

Minor points:

Please, reformat carefully all references (for example, line 105)

Figure 3 should be moved to Supplementary data.

**********

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Reviewer #1: Yes: Chun Wu

Reviewer #2: No

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19 Nov 2021

Dear Reviewers and Academic editor

I hope you are well. The authors apply all major and minor suggestions to the manuscript indicated by the reviewers and the academic editor. Many changes were made, so they are not attached in this communication window due to the number of points reviewed and corrected, but all comments and suggestions are answered in the Response to Reviewers file.

The article was shortened to include only the most relevant information as suggested by reviewer 2. The 3D M protein model was analyzed in the context of the membrane using the OPM/PPM server as suggested by reviewer 1. We respond to the comment related to the revision of the prediction of T cell epitopes as well as the proposed molecular docking. Only 5 figures and 2 tables were selected to be included directly in the revised manuscript and 4 complementary information files were selected to be presented in this new version of the manuscript. The references were reviewed and corrected. Figures were adjusted at a resolution of 300 dpi.

We deliver the restructured work for evaluation and possible publication.

I really appreciate your kind attention.

Corresponding author

Submitted filename: Response to Reviewers PONE-D-21-16531.pdf

Click here for additional data file.

1 Dec 2021

Your revised manuscript was modified and addressed the concerns of the reviewers, as wells as my previous comments on the original manuscript. Since PLOS One does not perform final copy-editing of accepted manuscripts, I request that all changes must be done prior to acceptance. Upon reading your manuscript, I came upon a number of typographic errors that must be corrected. The list below is likely not complete and I recommend that you carefully check the complete manuscript for grammar and typographic errors. I would strongly recommend that the manuscript should corrected by a native English speaker, to make the text more fluent, thereby increasing the impact of your data on the reader.

Some specific editorial comments to address:

Title: update your title on manuscript title page. Currently it is duplicated with a second version, which seems to be the short title. Make sure both titles are separate to avoid confusion.L40 : remove “obtained”

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Claude Krummenacher, PhD

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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16 Jan 2022

Dear Reviewers

I hope you are well. The authors applied to the manuscript all the suggestions indicated by the academic editor. Typographic errors have been corrected, but are not attached to this communication window due to the number of points corrected. All comments and suggestions are answered in the Response to Reviewers file. The English language has been checked and corrected by an English speaker. 5 figures and 2 tables, and 4 complementary information files were included in this new version of the manuscript. Figures were adjusted at a resolution of 300 dpi. We deliver the restructured work for evaluation and possible publication.

I really appreciate your kind attention.

Corresponding author

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24 Jan 2022

Predicted 3D model of the M protein of Porcine Epidemic Diarrhea Virus and analysis of its immunogenic potential

PONE-D-21-16531R2

Dear Dr. Rosas-Murrieta,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Claude Krummenacher, PhD

Academic Editor

PLOS ONE

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31 Jan 2022

PONE-D-21-16531R2

Predicted 3D model of the M protein of Porcine Epidemic Diarrhea Virus and analysis of its immunogenic potential

Dear Dr. Rosas-Murrieta:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Claude Krummenacher

Academic Editor

PLOS ONE