Cell Epitope Prediction Research Articles

B cell epitope prediction by capturing spatial clustering property of the epitopes using graph attention network.

Knowledge of B cell epitopes is critical to vaccine design, diagnostics, and therapeutics. As experimental validation for epitopes is time-consuming and costly, many in silico tools have been developed to computationally predict the B cell epitopes. While most methods show poor performance, deep learning methods in recent years have shown promising results. We developed a method called EpiGraph that outperformed previous methods, including those that showed a significant improvement in performance in recent years. Our model's performance can be attributed to the following factors: (1) a combination of structure and sequence feature embeddings obtained from pretrained ESM-IF1 and ESM-2 models could capture the structural and evolutionary features of B cell epitopes, (2) a graph attention network could learn the spatial proximity of B cell epitopes with high graph homophily, and (3) residual connections in the model framework mitigate the over-smoothing problem in the graph neural network. Our model achieved the highest performance on an independent benchmark dataset. The results were also consistent on a different dataset. The datasets and source codes are available at https://github.com/sj584/EpiGraph . A user-friendly web server is freely available at http://epigraph.kaist.ac.kr .

Comparison of "framework Shuffling" and "CDR Grafting" in humanization of a PD-1 murine antibody.

Humanization is typically adopted to reduce the immunogenicity of murine antibodies generated by hybridoma technology when used in humans. Two different strategies of antibody humanization are popularly employed, including "complementarity determining region (CDR) grafting" and "framework (FR) shuffling" to humanize a murine antibody against human programmed death-1 (PD-1), XM PD1. In CDR-grafting humanization, the CDRs of XM PD-1, were grafted into the human FR regions with high homology to the murine FR counterparts, and back mutations of key residues were performed to retain the antigen-binding affinities. While in FR-shuffling humanization, a combinatorial library of the six murine CDRs in-frame of XM PD-1 was constructed to a pool of human germline FRs for high-throughput screening for the most favorable variants. We evaluated many aspects which were important during antibody development of the molecules obtained by the two methods, including antibody purity, thermal stability, binding efficacy, predicted humanness, and immunogenicity, along with T cell epitope prediction for the humanized antibodies. While the ideal molecule was not achieved through CDR grafting in this particular instance, FR-shuffling proved successful in identifying a suitable candidate. The study highlights FR-shuffling as an effective complementary approach that potentially increases the success rate of antibody humanization. It is particularly noted for its accessibility to those with a biological rather than a computational background. The insights from this comparison are intended to assist other researchers in selecting appropriate humanization strategies for drug development, contributing to broader application and understanding in the field.

T lymphocyte cell epitopes prediction of newcastle disease virus isolated from pigeon (Columba livia domestica)

T lymphocyte cell epitopes prediction of newcastle disease virus isolated from pigeon (Columba livia domestica)

Deep learning of antibody epitopes using positional permutation vectors

BackgroundThe accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies. ResultsHere we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate “induced fit” biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training. ConclusionsWith the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

Cloning, Expression, and Bioinformatics Modeling of Human Papillomavirus Type 52 L1/L2 Chimeric Protein in Escherichia coli BL21 (DE3)

Human papillomavirus (HPV) L1 major capsid protein generates a highly immunogenic virus like particles (VLPs), which have been used as the main component of its prophylactic vaccine. However, the neutralizing antibodies against L1 VLPs are mostly type specific and may not be effective to prevent infection from different strains of HPV. On the other hand, HPV L2 minor capsid protein has low antigenic variation, thus can induce cross-neutralization. This study aims to obtain HPV 52 L1/L2 chimeric protein, which is designed based on HPV type 52 as one of the most circulated high-risk types in Indonesia, to develop a broad-spectrum HPV vaccine. Substitution of HPV 52 H4 helix L1 region with an HPV 52 L2 epitope was carried out using overlap extension PCR. HPV 52 L1/L2 chimeric gene was constructed into pET-SUMO expression vector and expressed in Escherichia coli BL21 (DE3). Bioinformatics modeling suggested that L2 epitope was located inside of the loop region in monomer form, and on the contrary, it was located outside of the pentamer surface. Furthermore, B cell and T cell epitopes predictions were conducted using Immune Epitope Database (IEDB) analysis. The B cell epitopes prediction revealed eleven potential epitopes, whereas the T cell epitopes prediction showed seven potential epitopes for each MHC class I and MHC class II. This study showed that HPV 52 L1/L2 chimeric protein has the potential to induce cross-neutralizing antibodies and can be developed as a promising candidate for a new HPV vaccine.

Прогнозування РНК, siRNA з подальшим визначенням імунологічної відповіді запропонованої вакцини (Vac534) проти COVID-19

Despite the decline in Coronavirus infections, care must be taken to avoid new mutations that allow the virus to escape vaccination and treatment. Viral RNA is responsible for virus replication and assembly in the host cell. Aim. The goal of this work was to predict the RNA secondary structure of the vaccination we developed before (Vac534). In addition, the degree of RNA overlaps while translating into ribosomes, refolding, and evaluating the protein immune response are all considered. Methods. Different immunobioinformatics tools and servers were utilized for analysis. RNA folding was executed through the application of RNAstructure 6.4 and RNAfold software. The unique sequence context led to the emergence of potential RNA structures at the site where ribosome binding occurs. A new method, C-IMMSIM, which relies on immune cell epitope prediction, was employed to gain fresh insights into comprehending the immune system. Results. А high probability of ≥ 99% is shown between nucleotides with the stability of loops and motifs of the RNA 2D structure. The predicted siRNA sequences, which were located in three places, were used to calculate total energy, self-folding, and a free end with a high accessibility score. RNA translation into the ribosome is required to determine the optimum direction of translation. The short docking fragment of 24 nucleotides of RNA (Vac534) generated six robust binds with MPRO at high energy. Conclusions. The immunological evaluation of the vaccination is critical for stimulating immune cells and detecting interleukins and cytokine production. This study is viewed as a step toward developing Coronavirus treatments. The number of loops and motifs is increasing, with a probability of above 99% for most sequences. The predicted siRNA sequences, which were located in three places, were used to calculate total energy, self-folding, and a free end with a high accessibility score.

Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections.

B cell epitope prediction methods are separated into linear sequence-based predictors and conformational epitope predictions that typically use the measured or predicted protein structure. Most linear predictions rely on the translation of the sequence to biologically based representations and the applications of machine learning on these representations. We here present CALIBER 'Conformational And LInear B cell Epitopes pRediction', and show that a bidirectional long short-term memory with random projection produces a more accurate prediction (test set AUC=0.789) than all current linear methods. The same predictor when combined with an Evolutionary Scale Modeling-2 projection also improves on the state of the art in conformational epitopes (AUC = 0.776). The inclusion of the graph of the 3D distances between residues did not increase the prediction accuracy. However, the long-range sequence information was essential for high accuracy. While the same model structure was applicable for linear and conformational epitopes, separate training was required for each. Combining the two slightly increased the linear accuracy (AUC 0.775 versus 0.768) and reduced the conformational accuracy (AUC = 0.769).

Prediction and identification of HLA-A*0201-restricted epitopes from cancer testis antigen CT23

ABSTRACT Cancer-testis antigen CT23 is a class of tumor-associated antigens (TAA) characterized by restricted expression in male germ cells and a variety of tumor tissues. Numerous studies have shown that CT23 is closely related to tumor cell viability, proliferation, metastasis and invasion. CT23 is immunogenic and can cause specific immune response in tumor patients. Therefore, it is considered to be one of the best target antigens for designing therapeutic tumor vaccines and T-cell-mediated tumor immunotherapy. In this study, we initially obtained seven HLA-A*0201-restricted CT23 epitope candidate peptides through the T cell epitope prediction program. Subsequently, a T2 cell binding assay revealed the potential binding of all candidate peptides with HLA-A2 molecules. Notably, peptide P7 (ALLVLCYSI) exhibited the highest affinity, as evidenced by a fluorescence index (FI) of 2.19. Dendritic cells (DCs) loaded with CT23 candidate peptide can stimulate CD8+T cell activation and proliferation, and compared with other candidate peptides, candidate peptide P7 is superior. The cytotoxic T lymphocytes (CTLs) stimulated by the peptide P7 had killing effect on tumor cells (HLA-A*0201+, CT23+), but no killing effect on tumor cells (HLA-A*0201−, CT23+). The CTLs induced by the peptide P7 also had a specific killing effect on T2 cells bearing the peptide P7. In summary, our findings suggest that the CT23 peptide P7 (ALLVLCYSI) can induce immune responses and holds potential for tumor-specific CTL therapy.

Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence.

During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8+ T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses.

Family-Specific Training Improves Linear B Cell Epitope Prediction for Emerging Viruses.

The rational design of vaccines and antibody-based therapeutics against newly emerging viruses relies on B cell epitopes mainly. To predict the B cell epitopes of a novel virus, several algorithms have been developed. While most existing algorithms are trained on a dataset in which B cell epitopes are classified as 'Positive' or 'Negative'. However, we found that training on such data contaminates the target pattern of specific viruses, leading to inaccurate predictions in some cases. In this paper, we introduce a novel framework for predicting linear B cell epitopes of novel viruses by exclusively using highly similar viruses for training data. We employed kernel regression based on seropositive rates, which are the percentages of seropositive samples among the population, to predict the potential epitopes. To assess our method, we conducted simulations and utilized two real-world datasets. Our method significantly outperformed other existing methods on the testing data of four viruses with seropositive rates. Also, our strategy showed a better prediction in a larger dataset from the IEDB. Thus, a novel framework providing better linear B cell prediction of newly emerging viruses is established, which will benefit the rational design of vaccines and antibody-based therapeutics in the future.

Identification of conserved immunogenic peptides of SARS-CoV-2 nucleocapsid protein

The emergence of the new SARS-CoV-2 variants has led to major concern regarding the efficacy of approved vaccines. Nucleocapsid is a conserved structural protein essential for replication of the virus. This study focuses on identifying conserved epitopes on the nucleocapsid (N) protein of SARS-CoV-2. Using 510 unique amino acid sequences of SARS-CoV-2 N protein, two peptides (193 and 215 aa) with 90% conservancy were selected for T cell epitope prediction. Three immunogenic peptides containing multiple T cell epitopes were identified which were devoid of autoimmune and allergic immune response. These peptides were also conserved (100%) in recent Omicron variants reported in Jan-August 2023. HLA analysis reveals that these peptides are predicted as binding to large number of HLA alleles and 71–90% population coverage in six continents. Identified peptides displayed good binding score with both HLA class I and HLA class II molecules in the docking study. Also, a vaccine construct docked with TLR-4 receptor displays strong interaction with 20 hydrogen bonds and molecular simulation analysis reveals that docked complex are stable. Additionally, the immunogenicity of these N protein peptides was confirmed using SARS-CoV-2 convalescent serum samples. We conclude that the identified N protein peptides contain highly conserved and antigenic epitopes which could be used as a target for the future vaccine development against SARS-CoV-2. Communicated by Ramaswamy H. Sarma

Molecular Characteristics of Bovine Viral Diarrhea Virus Strains Isolated from Persistently Infected Cattle.

In this study, we reported the isolation, identification, and molecular characteristics of nine BVDV strains that were isolated from the serum of persistently infected cattle. The new strains were designated as BVDV TJ2101, TJ2102, TJ2103, TJ2104, TJ2105, TJ2106, TJ2107, TJ2108 and TJ2109. The TJ2102 and TJ2104 strains were found to be cytopathic BVDV, and the other strains were non-cytopathic BVDV. An alignment and phylogenetic analysis showed that the new isolates share 92.2-96.3% homology with the CP7 strain and, thus, were classified as the BVDV-1b subgenotype. A recombination analysis of the genome sequences showed that the new strains could be recombined by the major parent BVDV-1a NADL strain and the minor parent BVDV-1m SD-15 strain. Some genome variations or unique amino acid mutations were found in 5'-UTR, E0 and E2 of these new isolates. In addition, a potential linear B cell epitopes prediction showed that the potential linear B cell epitope at positions 56-61 is highly variable in BVDV-1b. In conclusion, the present study has identified nine strains of BVDV from persistently infected cattle in China. Further studies on the virulence and pathogenesis of these new strains are recommended.

Characterization of Duffy Binding Protein II-specific CD4+T cell responses in Plasmodium vivax patients

Plasmodium vivax Duffy Binding Protein region II (PvDBPII) is a leading vaccine candidate against blood-stage vivax malaria. Anti-PvDBPII antibodies potentially block parasite invasion by inhibition of erythrocyte binding. However, knowledge of PvDBPII-specific T cell responses is limited. Here, to assess the responses of PvDBPII-specific CD4+T cells in natural P. vivax infection, three cross-sectional studies were conducted in recovered subjects. In silico analysis was used for potential T cell epitope prediction and selection. PBMCs from P. vivax subjects were stimulated with selected peptides and examined for cytokine production by ELISPOT or intracellular cytokine staining. Six dominant T cell epitopes were identified. Peptide-driven T cell responses showed effector memory CD4+T cell phenotype, secreting both IFN-γ and TNF-α cytokines. Single amino acid substitutions in three T cell epitopes altered levels of IFN-γ memory T cell responses. Seropositivity of anti-PvDBPII antibodies were detected during acute malaria (62%) and persisted up to 12 months (11%) following P. vivax infection. Further correlation analysis showed four out of eighteen subjects had positive antibody and CD4+T cell responses to PvDBPII. Altogether, PvDBPII-specific CD4+T cells were developed in natural P. vivax infections. Data on their antigenicity could facilitate development of an efficacious vivax malaria vaccine.

ITCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features.

Neoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. However, these methods mainly consider the neoantigen end and ignore the interaction between peptide-TCR and the preference of each residue in TCRs, resulting in the filtered peptides often fail to truly elicit an immune response. Here, we propose a novel encoding approach for peptide-TCR representation. Subsequently, a deep learning framework, namely iTCep, was developed to predict the interactions between peptides and TCRs using fusion features derived from a feature-level fusion strategy. The iTCep achieved high predictive performance with AUC up to 0.96 on the testing dataset and above 0.86 on independent datasets, presenting better prediction performance compared with other predictors. Our results provided strong evidence that model iTCep can be a reliable and robust method for predicting TCR binding specificities of given antigen peptides. One can access the iTCep through a user-friendly web server at http://biostatistics.online/iTCep/, which supports prediction modes of peptide-TCR pairs and peptide-only. A stand-alone software program for T cell epitope prediction is also available for convenient installing at https://github.com/kbvstmd/iTCep/.

The antigen presentation of MCMV specific Qa-1-restricted CD8 +T cells

Abstract Murine cytomegalovirus (MCMV) is a well-established model system for human CMV (HCMV), a ubiquitous herpesvirus that affects over 70% of people worldwide. Although the classical CD8 +T cell response to MCMV has been well characterized, little is known about the non-classical CD8 +T cell response. Our laboratory recently demonstrated that Qa-1-restricted CD8 +T cells are sufficient to protect mice from MCMV induced lethality. Our recent data shows that this protective T cell population has limited αβ TCR diversity. To identify the MCMV peptide(s) presented by Qa-1, we utilized bioinformatic tools that predict MHC binding, including NetMHC 4.0 and T Cell Epitope Prediction tools available on the Immune Epitope Database and Analysis Resource. Using these tools, we queried the entire MCMV coding sequence to identify potential nonamers predicted to bind to Qa-1. Furthermore, these candidates were also queried to identify protease cleavage sites to ensure that they are predicted to exist in vivo. Approximately 40 potential peptides were selected and tested in an in vitroscreen using Qa-1 expressing cell lines. Our results suggest that several candidate peptides bind to Qa-1 as strongly as well-defined canonical binding partners. Taken together, these data suggest that an MCMV-responsive T cell population with limited αβ TCR diversity recognizes MCMV-derived peptides presented by Qa-1. This population is likely to be of importance for viral protection when classical CD8 +T cell immunity is compromised. This work is supported by the NIH Research Grant AI046709. Shanelle Reilly is supported by research supplement R01 3AI046709-18S1 to promote diversity.

Biomaterial-Based In Situ Cancer Vaccines.

Cancer immunotherapies have reshaped the paradigm for cancer treatment over the past decade. Among them, therapeutic cancer vaccines that aim to modulate antigen-presenting cells and subsequent T cell priming processes are among the first FDA-approved cancer immunotherapies. However, despite showing benign safety profiles and the capability to generate antigen-specific humoral and cellular responses, cancer vaccines have been limited by the modest therapeutic efficacy, especially for immunologically cold solid tumors. One key challenge lies in the identification of tumor-specific antigens, which involves a costly and lengthy process of tumor cell isolation, DNA/RNA extraction, sequencing, mutation analysis, epitope prediction, peptide synthesis, and antigen screening. To address these issues, in situ cancer vaccines have been actively pursued to generate endogenous antigens directly from tumors and utilize the generated tumor antigens to elicit potent cytotoxic T lymphocyte (CTL) response. Biomaterials-based in situ cancer vaccines, in particular, have achieved significant progress by taking advantage of biomaterials that can synergize antigens and adjuvants, troubleshoot delivery issues, home, and manipulate immune cells in situ. This review will provide an overview of biomaterials-based in situ cancer vaccines, either living or artificial materials, under development or in the clinic, and discuss the design criteria for in situ cancer vaccines.

Evaluation of a mimotope of the Rickettsia outer membrane protein A (OmpA) as an antigen in enzyme-linked immunosorbent assay to detect rickettsiosis in capybaras (Hydrochoerus hydrochaeris), horses (Equus caballus), and opossums (Didelphis sp.).

Rickettsia rickettsii is the etiological agent of Rocky Mountain spotted fever, which is an important tick-borne zoonosis and, in Brazil, it causes Brazilian spotted fever, which has high lethality rate. This study aimed to evaluate a synthetic peptide corresponding to a segment of the outer membrane protein A (OmpA) as an antigen in a serological test for the diagnosis of rickettsial infections. The amino acid sequence of the peptide was selected by predicting B cell epitopes using B Cell Epitope Prediction (Immune Epitope Database and Analysis Resource) and Epitopia and OmpA sequences of Rickettsia rickettsii strain 'Brazil' and Rickettsia parkeri strains 'Maculatum 20' and 'Portsmouth'. A peptide with amino acid sequence common to both Rickettsia species was synthesized and arbitrarily named OmpA-pLMC. To evaluate this peptide in enzyme-linked immunosorbent assay (ELISA), serum samples of capybara (Hydrochoerus hydrochaeris), horse (Equus caballus), and opossum (Didelphis albiventris) that had been previously tested by indirect immunofluorescence assay (IFA) for rickettsial infection were separated into IFA-positive and IFA-negative groups and used in the assay. There were no significant differences in ELISA optical density (OD) values between IFA-positive and IFA-negative groups with horse samples. The mean OD values were significantly higher in the IFA-positive capybara serum samples (IFA-pos vs. IFA-neg = 2.389 ± 0.761 vs. 1.760 ± 0.840). However, receiver operating characteristic (ROC) curve analysis did not show significant diagnostic parameters. On the other hand, 12 out of 14 (85.7%) opossum samples of the IFA-positive group showed reactivity in ELISA, and this was significantly higher than of the IFA-negative group (0.7196 ± 0.440 vs. 0.2318 ± 0.098, respectively; 85.7% sensitivity, 100% specificity). Therefore, our results show that OmpA-pLMC has a potential to be used in immunodiagnostic assays to detect spotted fever group rickettsial infections.

Identification of a novel porcine Teschovirus 2 strain as causative agent of encephalomyelitis in suckling piglets with high mortality in China

BackgroundPorcine Teschovirus (PTV), also named Teschovirus A, is prevalent in pig populations, mainly causing neurological symptoms, diarrhea, pneumonia, and reproductive failure, however the morbidity and mortality are usually low in pig farms.Case presentationIn this study, we reported a PTV outbreak investigation in one large-scale pig farm in China with severe symptoms including diarrhea, lethargy, locomotor ataxia, nystagmus, paralysis of the hind limbs, and coma in piglets. More importantly, the mortality reached 38% in suckling pigs, which is remarkably high in PTV history. A novel PTV strain, named HeNZ1, was isolated from cerebral samples of one suckling pig and the genome sequence was obtained by NGS sequencing. Phylogenetic and evolutionary divergence analyses revealed that HeNZ1 belongs to PTV genotype 2. Surprisingly, the VP1 coding region of HeNZ1 shares the highest sequence similarity with European PTV-2 strains, instead of China domestic PTV-2 strains, implying it may not derive from China local PTV-2 strains. Multiple sequence alignment and B cell epitope prediction of PTV VP1 and VP2 protein revealed 10 B cell epitopes, 5 mutant clusters and 36 unique mutation sites, of which 19 unique mutation sites are located in B cell epitopes and exposed on the surface of VP1 or VP2, implying significant antigenic drift potential of HeNZ1.ConclusionThese results indicate that HeNZ1 is a highly virulent PTV-2 strain, which capable of causing severe neurological symptoms and high mortality in piglets. Bioinformatic analysis suggest that HeNZ1 is genetically and antigenically different from other Chinese PTV-2 strains. Overall, current case expanded our understanding of PTV-2 clinical spectrum and revealed the emergence of a highly virulent PTV-2 strain with substantial genetic diversity and antigenic drift potential in VP1 and VP2.

Denovo Structural Modeling and B cell and T cell Epitope Prediction against SARS-COV-2 PLpro to Cure COVID-19: Vaccinomics Based Approach

Denovo Structural Modeling and B cell and T cell Epitope Prediction against SARS-COV-2 PLpro to Cure COVID-19: Vaccinomics Based Approach

Immunoinformatics Approach for Epitope Mapping of Immunogenic Regions (N, F and H Gene) of Small Ruminant Morbillivirus and Its Comparative Analysis with Standard Vaccinal Strains for Effective Vaccine Development

Outbreaks of small ruminant morbillivirus (SRMV) are regularly occurring in Pakistan despite vaccine availability. This study was designed to identify substitutions within the immunogenic structural and functional regions of the nucleocapsid, fusion, and hemagglutinin genes of SRMV and their comparison with vaccinal strains of Nigerian and Indian origin. Swabs and tissue samples were collected from diseased animals. RT-PCR was used to characterize selected genes encoded by viral RNA. The study's N, F, and H protein sequences and vaccinal strains were analyzed for B and T cell epitope prediction using ABCpred, Bipred, and IEDB, respectively. Significant substitutions were found on the C terminus of the nucleocapsid, within the fusion motif region of the fusion gene and in the immunoreactive region of the hemagglutinin gene. Our results emphasize the need for the development of effective vaccines that match the existing variants of SRMV strains circulating in Pakistan.