A multi-resolution graph convolution network for contiguous epitope prediction

2021 ◽  
Author(s):  
Lisa Oh ◽  
Bowen Dai ◽  
Chris Bailey-Kellogg
Keyword(s):  
Epitope Prediction ◽  
Resolution Graph

Multi-Epitope Vaccines (MEVs), as a Novel Strategy Against Infectious Diseases

2020 ◽  
Vol 17 (5) ◽  
pp. 354-364
Author(s):  
Mohammad Mahmoudi Goumari ◽  
Ibrahim Farhani ◽  
Navid Nezafat ◽  
Shirin Mahmoodi
Keyword(s):  
Infectious Diseases ◽  
Epitope Prediction ◽  
Cost Effective ◽  
Antimicrobial Drugs ◽  
Antibiotic Resistant ◽  
Time Consumption ◽  
Preclinical Evaluation ◽  
Structural Evaluation ◽  
Novel Strategy ◽  
Epitope Vaccines

Infectious diseases have caused historical pandemics in the world. Three strategies, including sanitation programs, antimicrobial drugs, and vaccines are considered for the prevention and treatment of infectious diseases. Today, some infectious diseases cause millions of mortalities universally. Due to the emergence of antibiotic-resistant pathogens, as well as some limitations of traditional vaccines, focusing on novel strategies is essential. Multi-Epitope Vaccines (MEVs), as a novel strategy, have been designed based on immunoinformatics methods; epitope prediction by authentic servers, attachment of epitopes using proper linkers, physicochemical, immunological and structural evaluation by bioinformatics tools that are basic stages in MEVs designing. Advantages such as cost-effective, high safety, less time consumption in designing, the application of natural adjuvants, and satisfactory preclinical evaluation outstand MEVs than other types of vaccines. Therefore, MEVs are promising vaccines against resistant diseases such as lower respiratory infection and diarrhea.

Prediction of the Omp16 Epitopes for the Development of an Epitope-based Vaccine Against Brucellosis

2019 ◽  
Vol 19 (1) ◽  
pp. 36-45 ◽  
Author(s):  
Marzieh Rezaei ◽  
Mohammad Rabbani-khorasgani ◽  
Sayyed Hamid Zarkesh-Esfahani ◽  
Rahman Emamzadeh ◽  
Hamid Abtahi
Keyword(s):  
Immune Responses ◽  
B Cell ◽  
Dairy Products ◽  
Bioinformatics Analysis ◽  
Epitope Prediction ◽  
Animal Husbandry ◽  
Zoonotic Diseases ◽  
Recombinant Vaccines ◽  
Protective Capacity ◽  
Tertiary Structures

Background:Brucellosis is an infectious disease caused by Brucella bacteria that cause disease in animals and humans. Brucellosis is one of the most common zoonotic diseases transmitted from animals-to-human through direct contact with infected animals and also consumption of unpasteurized dairy products. Due to the wide incidence of brucellosis in Iran and economical costs in industrial animal husbandry, Vaccination is the best way to prevent this disease. All of the available commercial vaccines against brucellosis are derived from live attenuated strains of Brucella but because of the disadvantage of live attenuated vaccines, protective subunit vaccine against Brucella may be a good candidate for the production of new recombinant vaccines based on Brucella Outer Membrane Protein (OMP) antigens. In the present study, comprehensive bioinformatics analysis has been conducted on prediction software to predict T and B cell epitopes, the secondary and tertiary structures and antigenicity of Omp16 antigen and the validation of used software confirmed by experimental results.Conclusion:The final epitope prediction results have proposed that the three epitopes were predicted for the Omp16 protein with antigenicity ability. We hypothesized that these epitopes likely have the protective capacity to stimulate both the B-cell and T-cell mediated immune responses and so may be effective as an immunogenic candidate for the development of an epitope-based vaccine against brucellosis.

scholarly journals Design of a multi-epitope vaccine against cervical cancer using immunoinformatics approaches

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Samira Sanami ◽  
Fatemeh Azadegan-Dehkordi ◽  
Mahmoud Rafieian-Kopaei ◽  
Majid Salehi ◽  
Maryam Ghasemi-Dehnoo ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
T Lymphocytes ◽  
Tertiary Structure ◽  
Therapeutic Vaccine ◽  
Epitope Prediction ◽  
Therapeutic Effects ◽  
Epitope Vaccine ◽  
In Vivo Experiments

AbstractCervical cancer, caused by human papillomavirus (HPV), is the fourth most common type of cancer among women worldwide. While HPV prophylactic vaccines are available, they have no therapeutic effects and do not clear up existing infections. This study aims to design a therapeutic vaccine against cervical cancer using reverse vaccinology. In this study, the E6 and E7 oncoproteins from HPV16 were chosen as the target antigens for epitope prediction. Cytotoxic T lymphocytes (CTL) and helper T lymphocytes (HTL) epitopes were predicted, and the best epitopes were selected based on antigenicity, allergenicity, and toxicity. The final vaccine construct was composed of the selected epitopes, along with the appropriate adjuvant and linkers. The multi-epitope vaccine was evaluated in terms of physicochemical properties, antigenicity, and allergenicity. The tertiary structure of the vaccine construct was predicted. Furthermore, several analyses were also carried out, including molecular docking, molecular dynamics (MD) simulation, and in silico cloning of the vaccine construct. The results showed that the final proposed vaccine could be considered an effective therapeutic vaccine for HPV; however, in vitro and in vivo experiments are required to validate the efficacy of this vaccine candidate.

scholarly journals Efficient Identification of Novel Hla-A*0201–Presented Cytotoxic T Lymphocyte Epitopes in the Widely Expressed Tumor Antigen Prame by Proteasome-Mediated Digestion Analysis

2001 ◽  
Vol 193 (1) ◽  
pp. 73-88 ◽  
Author(s):  
Jan H. Kessler ◽  
Nico J. Beekman ◽  
Sandra A. Bres-Vloemans ◽  
Pauline Verdijk ◽  
Peter A. van Veelen ◽  
...  
Keyword(s):  
T Lymphocyte ◽  
Cytotoxic T Lymphocyte ◽  
Epitope Prediction ◽  
Binding Prediction ◽  
Ctl Epitopes ◽  
High Affinity ◽  
Tumor Associated Antigen ◽  
Immunotherapy Of Cancer ◽  
Binding Peptides

We report the efficient identification of four human histocompatibility leukocyte antigen (HLA)-A*0201–presented cytotoxic T lymphocyte (CTL) epitopes in the tumor-associated antigen PRAME using an improved “reverse immunology” strategy. Next to motif-based HLA-A*0201 binding prediction and actual binding and stability assays, analysis of in vitro proteasome-mediated digestions of polypeptides encompassing candidate epitopes was incorporated in the epitope prediction procedure. Proteasome cleavage pattern analysis, in particular determination of correct COOH-terminal cleavage of the putative epitope, allows a far more accurate and selective prediction of CTL epitopes. Only 4 of 19 high affinity HLA-A*0201 binding peptides (21%) were found to be efficiently generated by the proteasome in vitro. This approach avoids laborious CTL response inductions against high affinity binding peptides that are not processed and limits the number of peptides to be assayed for binding. CTL clones induced against the four identified epitopes (VLDGLDVLL, PRA100–108; SLYSFPEPEA, PRA142–151; ALYVDSLFFL, PRA300–309; and SLLQHLIGL, PRA425–433) lysed melanoma, renal cell carcinoma, lung carcinoma, and mammary carcinoma cell lines expressing PRAME and HLA-A*0201. This indicates that these epitopes are expressed on cancer cells of diverse histologic origin, making them attractive targets for immunotherapy of cancer.

scholarly journals Data curation to improve the pattern recognition performance of B-cell epitope prediction by support vector machine

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Li Cen Lim ◽  
Yee Ying Lim ◽  
Yee Siew Choong
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
B Cell ◽  
Recognition Performance ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Support Vector ◽  
B Cell Epitopes ◽  
Prediction Module ◽  
B Cell Epitope

Abstract B-cell epitope will be recognized and attached to the surface of receptors in B-lymphocytes to trigger immune response, thus are the vital elements in the field of epitope-based vaccine design, antibody production and therapeutic development. However, the experimental approaches in mapping epitopes are time consuming and costly. Computational prediction could offer an unbiased preliminary selection to reduce the number of epitopes for experimental validation. The deposited B-cell epitopes in the databases are those with experimentally determined positive/negative peptides and some are ambiguous resulted from different experimental methods. Prior to the development of B-cell epitope prediction module, the available dataset need to be handled with care. In this work, we first pre-processed the B-cell epitope dataset prior to B-cell epitopes prediction based on pattern recognition using support vector machine (SVM). By using only the absolute epitopes and non-epitopes, the datasets were classified into five categories of pathogen and worked on the 6-mers peptide sequences. The pre-processing of the datasets have improved the B-cell epitope prediction performance up to 99.1 % accuracy and showed significant improvement in cross validation results. It could be useful when incorporated with physicochemical propensity ranking in the future for the development of B-cell epitope prediction module.

B Cell Epitope Prediction

2013 ◽  
pp. 63-67
Author(s):  
Yasser EL-Manzalawy ◽  
Vasant Honavar
Keyword(s):  
B Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
B Cell Epitope

scholarly journals Fast Groupwise Registration Using Multi-Level and Multi-Resolution Graph Shrinkage

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Pei Dong ◽  
Xiaohuan Cao ◽  
Pew-Thian Yap ◽  
Dinggang Shen
Keyword(s):  
Resolution Graph ◽  
Multi Level ◽  
Groupwise Registration
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