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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
Published on: March 25, 2014
Molecular dynamics simulation based prediction of T-cell epitopes for the production of effector molecules for liver
Sidra Zafar1, Yuhe Bai2, Syed Aun Muhammad1
1Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University Multan, Multan, Punjab, Pakistan.
Abstract:
Liver cancer is the sixth most frequent malignancy and the fourth major cause of deaths worldwide. The current treatments are only effective in early stages of cancer. To overcome the therapeutic challenges and exploration of immunotherapeutic options, broad spectral therapeutic vaccines could have significant impact. Based on immunoinformatic and integrated machine learning tools, we predicted the potential therapeutic vaccine candidates of liver cancer. In this study, machine learning and MD simulation-based approach are effectively used to design T-cell epitopes that aid the immune system against liver cancer. Antigenicity, molecular weight, subcellular localization and expression site predictions were used to shortlist liver cancer associated proteins including AMBP, CFB, CDHR5, VTN, APOBR, AFP, SERPINA1 and APOE. We predicted CD8+ T-cell epitopes of these proteins containing LGEGATEAE, LLYIGKDRK, EDIGTEADV, QVDAAMAGR, HLEARKKSK, HLCIRHEMT, LKLSKAVHK, EQGRVRAAT and CD4+ T-cell epitopes of VLGEGATEA, WVTKQLNEI, VEEDTKVNS, FTRINCQGK, WGILGREEA, LQDGEKIMS, VKFNKPFVF, VRAATVGSL. We observed the substantial physicochemical properties of these epitopes with a significant binding affinity with MHC molecules. A polyvalent construct of these epitopes was designed using suitable linkers and adjuvant indicated significant binding energy (>-10.5 kcal/mol) with MHC class-I and II molecule. Based on in silico cloning, we found the considerable compatibility of this polyvalent construct with the E. coli expression system and the efficiency of its translation in host. The system-level and machine learning based cross validations showed the possible effect of these T-cell epitopes as potential vaccine candidates for the treatment of liver cancer.
Insights
Researchers identified potential liver cancer vaccine candidates using machine learning and immunoinformatics. These T-cell epitopes show promise for enhancing immune response against liver cancer, offering new therapeutic avenues.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Liver cancer is a major global health concern, ranking as the sixth most common malignancy and fourth leading cause of cancer-related deaths.
- Current treatments for liver cancer are often limited to early stages, highlighting the need for novel therapeutic strategies.
- Immunotherapeutic approaches, particularly broad-spectrum therapeutic vaccines, hold significant potential for overcoming treatment challenges.
Purpose of the Study:
- To predict and design potential therapeutic vaccine candidates for liver cancer using immunoinformatics and machine learning.
- To identify T-cell epitopes that can stimulate an immune response against liver cancer.
- To evaluate the efficacy and feasibility of a polyvalent construct of these epitopes as a vaccine.
Main Methods:
- Utilized machine learning and molecular dynamics (MD) simulations to design T-cell epitopes for liver cancer.
- Shortlisted liver cancer-associated proteins (e.g., AMBP, CFB, CDHR5, VTN, APOBR, AFP, SERPINA1, APOE) based on antigenicity, molecular weight, subcellular localization, and expression.
- Predicted and analyzed CD8+ and CD4+ T-cell epitopes, assessing their physicochemical properties and binding affinity to MHC molecules.
Main Results:
- Identified specific CD8+ T-cell epitopes (e.g., LGEGATEAE, LLYIGKDRK) and CD4+ T-cell epitopes (e.g., VLGEGATEA, WVTKQLNEI).
- Designed a polyvalent construct of these epitopes with significant binding energy to MHC class-I and II molecules.
- Demonstrated the construct's compatibility with the E. coli expression system and potential for translation in host systems.
Conclusions:
- The predicted T-cell epitopes exhibit favorable physicochemical properties and significant binding affinity to MHC molecules.
- The designed polyvalent construct shows promise as a potential liver cancer vaccine candidate.
- In silico validation supports the efficacy of these T-cell epitopes for developing novel immunotherapies against liver cancer.
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