Designing and Modeling of Multi-epitope Proteins for Diagnosis of Toxocara canis Infection

Ebrahimi, Maryam; Seyyedtabaei, Seyyed Javad; Ranjbar, Mohammad Mehdi; Tahvildar-Biderouni, Farid; Mamaghani, Amirreza Javadi

doi:10.1007/s10989-019-09940-1

Cited by 15 publications

(14 citation statements)

References 42 publications

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“…LepReact, a delayed-type hypersensitivity skin test, made from LID-1, was able to detect antigen-specific immune responses from M. leprae in guinea pigs and armadillos [72]. As to other diseases, Chagas disease detection can be improved using TcF43 and TcF26, proteins derived from the fusion of selected T. cruzi TR proteins [28]; Yin et al validated a high-accuracy ELISA assay using a recombinant protein for diagnosis of human brucellosis [27]; Ebrahimi [32].…”

Section: Discussionmentioning

confidence: 99%

“…The multi-epitope constructs enable a higher immunogenic density and diminish the cross-reaction risk of whole bacteria antigen, which is common in leprosy diagnosis [27]. Diseases such as hepatitis B [23], Chagas disease [28], cryptococcosis [25], leishmaniosis [29,30], tuberculosis [31], and toxocariasis [32] already have great results with recombinant multi-epitope proteins in their diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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In silico designing of a recombinant multi-epitope antigen for leprosy diagnosis

Lemes

Rodrigues

Jaiswal

et al. 2022

Journal of Genetic Engineering and Biotechnology

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Background Leprosy is caused by Mycobacterium leprae and Mycobacterium lepromatosis. Most of the affected population lives in low-income countries and may take up to 10 years to show any clinical signs, which is how physicians diagnose it. However, due to progressive cell damage, early diagnosis is very important. The best way to confirm leprosy is through bacilloscopic, which only confirms the diagnosis and has low accuracy or PCR, that requires specialized operators and is expensive. Since the bacteria are fastidious and do not grow in any culture media, therefore, diagnosing leprosy in the lab is still a challenge. In this concern, a recombinant multi-epitope protein can be a beneficial strategy in the management of the diagnosis, as diverse immunogenic epitopes are precisely selected to detect specific antibodies. Therefore, the purposes of the present study were to select immunogenic epitopes from different relevant proteins, with immunogenic properties, and then to construct a recombinant multi-epitope protein that accuses the presence of the antibodies in the early stages of the disease, making it more than appropriate to be applied as a diagnostic tool. Results We selected 22 common proteins from both species and, using bioinformatics tools, predicted B and T cell epitopes. After multiple filtering and analyzing, we ended up with 29 epitopes {MHC-I (total 18) and MHC-II (total 11)} from 10 proteins, which were then merged into one construct. Its secondary and tertiary structures were also predicted and refined to comprise the amino acid residues in the best conformation possible. The multi-epitope protein construct was stable, non-host homologous, non-allergic, non-toxic, and elicit humoral and cellular responses. It has conformational B cell epitopes and potential to elicit IFN-γ, IL-4, and IL-10 secretion. Conclusions This novel recombinant multi-epitope protein constructed using the common epitopes from M. leprae and M. lepromatosis has a huge immunological potential, is stable, and can be lyophilized to be used in ELISA plates or even in biosensors, which are user-friendly diagnosis tools, facilitating translation into human sample tests.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In silico designing of a recombinant multi-epitope antigen for leprosy diagnosis

Lemes

Rodrigues

Jaiswal

et al. 2022

Journal of Genetic Engineering and Biotechnology

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“…The application of bioinformatics methods is very helpful in predicting protein structure, their functions, biological features, specific immune response, avidity of antigen-antibody binding in vaccine design, and serologic diagnosis (Ebrahimi et al 2019;Mamaghani et al 2019;Habibi et al 2020;Karimi et al 2020). The epitope prediction servers based on the artificial intelligence have higher accuracy and sensitivity than those that perform epitope prediction based on physicochemical properties.…”

Section: Discussionmentioning

confidence: 99%

In-silico design of a multi-epitope for developing sero-diagnosis detection of SARS-CoV-2 using spike glycoprotein and nucleocapsid antigens

Mamaghani

Arab-Mazar

Heidarzadeh

et al. 2021

Netw Model Anal Health Inform Bioinforma

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COVID-19 is a pandemic disease caused by novel corona virus, SARS-CoV-2, initially originated from China. In response to this serious life-threatening disease, designing and developing more accurate and sensitive tests are crucial. The aim of this study is designing a multi-epitope of spike and nucleocapsid antigens of COVID-19 virus by bioinformatics methods. The sequences of nucleotides obtained from the NCBI Nucleotide Database. Transmembrane structures of proteins were predicted by TMHMM Server and the prediction of signal peptide of proteins was performed by Signal P Server. B-cell epitopes' prediction was performed by the online prediction server of IEDB server. Beta turn structure of linear epitopes was also performed using the IEDB server. Conformational epitope prediction was performed using the CBTOPE and eventually, eight antigenic epitopes with high physicochemical properties were selected, and then, all eight epitopes were blasted using the NCBI website. The analyses revealed that α-helices, extended strands, β-turns, and random coils were 28.59%, 23.25%, 3.38%, and 44.78% for S protein, 21.24%, 16.71%, 6.92%, and 55.13% for N Protein, respectively. The S and N protein three-dimensional structure was predicted using the prediction I-TASSER server. In the current study, bioinformatics tools were used to design a multi-epitope peptide based on the type of antigen and its physiochemical properties and SVM method (Machine Learning) to design multi-epitopes that have a high avidity against SARS-CoV-2 antibodies to detect infections by COVID-19.

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“…Data with higher antigenicity are closer to our predictive results. So we selected the first 10 data as the most relevant results and used them in our analysis (Ebrahimi et al, 2019). (Karplus & Schulz, 1985), accessibility (Emini et al, 1985), turns (Pellequer, Westhof, & Van Regenmortel, 1993), exposed surface (Janin et al, 1978) polarity (Ponnuswamy, Prabhakaran, & Manavalan, 1980) and antigenic propensity (Kolaskar & Tongaonkar, 1990).…”

Section: Prediction Of the Linear Epitopesmentioning

confidence: 99%

Prediction of B cell and T‐helper cell epitopes candidates of bovine leukaemia virus (BLV) by in silico approach

Hooshmand¹,

Fayazi²,

Tabatabaei³

et al. 2020

Veterinary Medicine & Sci

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The bovine leukaemia virus (BLV) is a retrovirus responsible for enzootic bovine leukaemia (EBL) disease, the most common cattle disease leading to high annual economic losses to the cattle breeding industry. Virus monitoring among the sheep and cattle herds is usually done by vaccination. Inactivated virus vaccines can partially protect the livestock from viral challenge. However, vaccinated animals are likely to be infected. So, there is an essential need for producing vaccine by other methods. Gp60 SU, encoded by Env gene, is the surface glycoprotein of BLV detected to be the major target for the host immunity against the virus. Different stages were performed to predict the potential B and T‐helper cell epitopes. The general framework of the method includes retrieving the amino acid sequence of gp60 SU, conducting the sequence alignment, getting the entropy plot, retrieving the previously found epitopes, predicting the hydropathy parameters, modelling the tertiary structure of the glycoprotein, minimizing the structure energy, validating the model by Ramachandran plot, predicting the linear and discontinuous epitopes by various servers and eventually choosing the consensus immunogenic regions. Ramachandran plot scrutiny has demonstrated that the modelled prediction is accurate and suitable. By surveying overlaps of various results, 4 and 2 immunogenic regions were selected as linear and conformational epitopes respectively. Amino acids 35–53, 67–97, 288–302 and 410–421 and those of numbers 37–58 and 72–100 were the regions selected as linear and conformational epitopes respectively. The tertiary structure of the final epitope was modelled as well. A comparison of the predicted epitopes structure with that of gp60 SU envelope, illustrated that the tertiary structure of these epitopes does not change after being separated from the primary complete one. The present achievements will lead to a better interpretation of the antigen–antibody interactions against gp60 in the designing process of safe and efficient vaccines.

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Designing and Modeling of Multi-epitope Proteins for Diagnosis of Toxocara canis Infection

Cited by 15 publications

References 42 publications

In silico designing of a recombinant multi-epitope antigen for leprosy diagnosis

In silico designing of a recombinant multi-epitope antigen for leprosy diagnosis

In-silico design of a multi-epitope for developing sero-diagnosis detection of SARS-CoV-2 using spike glycoprotein and nucleocapsid antigens

Prediction of B cell and T‐helper cell epitopes candidates of bovine leukaemia virus (BLV) by in silico approach

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