Zafar Sidra, Bai Yuhe, Muhammad Syed Aun, Guo Jinlei, Khurram Haris, Zafar Saba, Muqaddas Iraj, Shaikh Rehan Sadiq, Bai Baogang
Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University Multan, Multan, Punjab, Pakistan.
Department of Computer Science, Sorbonne University, Paris, France.
PLoS One. 2025 Jan 3;20(1):e0309049. doi: 10.1371/journal.pone.0309049. eCollection 2025.
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.
肝癌是全球第六大常见恶性肿瘤，也是第四大主要死因。目前的治疗方法仅在癌症早期有效。为了克服治疗挑战并探索免疫治疗方案，广谱治疗性疫苗可能会产生重大影响。基于免疫信息学和综合机器学习工具，我们预测了肝癌潜在的治疗性疫苗候选物。在本研究中，基于机器学习和分子动力学模拟的方法被有效地用于设计有助于免疫系统对抗肝癌的T细胞表位。通过抗原性、分子量、亚细胞定位和表达位点预测来筛选与肝癌相关的蛋白质，包括AMBP、CFB、CDHR5、VTN、APOBR、AFP、SERPINA1和APOE。我们预测了这些蛋白质的CD8 + T细胞表位，包括LGEGATEAE、LLYIGKDRK、EDIGTEADV、QVDAAMAGR、HLEARKKSK、HLCIRHEMT、LKLSKAVHK、EQGRVRAAT以及CD4 + T细胞表位VLGEGATEA、WVTKQLNEI、VEEDTKVNS、FTRINCQGK、WGILGREEA、LQDGEKIMS、VKFNKPFVF、VRAATVGSL。我们观察到这些表位具有大量的物理化学性质，与MHC分子具有显著的结合亲和力。使用合适的连接子和佐剂设计了这些表位的多价构建体，其与MHC I类和II类分子显示出显著的结合能（>-10.5 kcal/mol）。基于计算机克隆，我们发现这种多价构建体与大肠杆菌表达系统具有相当的兼容性及其在宿主中的翻译效率。基于系统水平和机器学习的交叉验证表明，这些T细胞表位作为治疗肝癌的潜在疫苗候选物可能具有效果。
