Epitope prediction for MSP119 protein in Plasmodium yeolii using computational approaches

Abstract

Malaria disease is caused by the transmission of Plasmodium, through the bite of a female Anopheles mosquito. Although the Plasmodium life-cycle has been extensively characterized, relatively little is known about sporozoite interaction with host organelles and proteins. Individuals that survive continuous exposure to infection do eventually develop clinical immunity, suggesting that a vaccine against asexual blood stage of the parasite is achievable. The merozoite surface protein (MSP1 19 ) of Plasmodium yoelii was considered as the target protein for epitope prediction using the computational approaches. The T-cell and B-cell epitopes for MSP1 19 were predicted using a variety of computational tools. Out of these predicted epitopes, the epitopes being expressed by the protozoa were identified. The 3D structures of T-cell epitopes (MHC-I and MHC-II) were modeled by homology modeling method followed by validation using the SAVES server. Further, the MHC molecules were identified and their 3D structures were retrieved from the Protein Data Bank. The protein-protein docking of modeled epitopes with respective MHC molecules were also carried out. Total Six T-cell epitopes ('ELSEHYYDRY', 'LLIITIVFNI', 'MMYHIYKLK', 'IYQAMYNVIF', 'SEEDMPADDF', 'YVLLQNSTI') for MSP1 19 have been identified as promising vaccine candidates. Furthermore, six B-cell epitopes ('QPTET', 'SEETE', 'SDKYNKKKP', 'KEKKKE', 'CKKNKA', 'THPDNT') have also been identified as potential epitopes. In future, these predicted epitopes might be exploited in vaccine development against malarial infection.

Synthetic Peptides Selected by Immunoinformatics as Potential Tools for the Specific Diagnosis of Canine Visceral Leishmaniasis

“…In this study, bioinformatics tools were used to select linear epitopes for B-cells based on the combination of three prediction algorithms, BepiPred, AAP12, and BCPred12, based on the assumption that the combination of different algorithms has greater accuracy, especially when dealing with protozoa [30]. Our selected peptides show good score values, including algorithms that are considered more restrictive, which is the case for BepiPred [31,32]. Furthermore, using the PPI networks, it was possible to evidence the biological importance of the proteins that contained the selected peptides, as they can participate in the modulation of cellular activities like virulence, metabolism regulation, or latency behavior in some species of the parasite [33][34][35][36].…”

Magnetic Nanovectors for the Development of DNA Blood-Stage Malaria Vaccines

“…Therefore, the failure of further enhancement of T cell responses by magnetofection in our study may partially be due to the complexity of this MSP1 19 antigen processing and presentation in vivo. Additionally, a recent study using computational approaches to predict epitopes within MSP1 19 found that B cell epitopes have the lowest energy score compared to the T cell epitopes, indicating a strong binding affinity toward the receptor and, therefore, stronger humoral responses overall [ 48 ]. There were six potential B cell epitopes and two potential T cell epitopes predicted as potential candidates for vaccine development against malaria within the MSP1 19 antigen [ 48 ], suggesting a strong humoral-mediated immune response by this antigen.…”

Biochemical regulation and structural analysis of copper‐transporting ATPase in a human hepatoma cell line for Wilson disease

“…Ramachandran plot showed the distribution of ϕ, ψ angles with 89.0% residues in the most favorable core region, 6.4% residues in additional allowed region, and 2.5% residues in the generously allowed region. On the basis of this validation, we concluded in silico generated ATP7B model as accurate . All these 13 nsSNPs were subjected to Accelrys Discovery Studio 2, Build Mutant protocol, to generate a model for each functional nsSNP.…”

“…On the basis of this validation, we concluded in silico generated ATP7B model as accurate. 42 All these 13 nsSNPs were subjected to Accelrys Discovery Studio 2, Build Mutant protocol, to generate a model for each functional nsSNP. Energy minimizations for all structures were accomplished using the Accelrys Discovery Studio 2, Minimization protocol.…”