通过互补决定区嫁接技术,对抗表皮生长因子受体支架进行硅设计和优化

Razieh Rezaei Adriani, S. M. Mousavi Gargari, Hamid Bakherad, J. Amani
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摘要

单克隆抗体通过其互补决定区(CDR)与靶点特异性结合,是治疗多种人类疾病的极具吸引力的药物。结构上保留了 CDR 的小蛋白是很有前景的抗体模拟物。在这项硅学研究中,我们通过 CDRs 嫁接技术,提出了针对癌症标志物表皮生长因子受体(EGFR)的新型抗体模拟物。我们利用计算机从三个小型蛋白质支架中筛选出了十个能有效固定接枝 CDR 环的潜在接枝受体位点。然后通过环路随机化技术,将从帕尼单抗抗表皮生长因子受体结构域 III 晶体结构中提取的抗体-受体相互作用中涉及最多的三个 CDR 环路嫁接到选定的支架上。通过结合能计算发现,3个CDR环和10个嫁接位点的36种组合中有3种与表皮生长因子受体DIII有特异性结合。因此,本策略和所选的小蛋白支架是设计具有高结合能的表皮生长因子受体新结合体的有效工具。
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In silico designing and optimization of anti‐epidermal growth factor receptor scaffolds by complementary‐determining regions‐grafting technique
Monoclonal antibodies are attractive therapeutic agents in a wide range of human disorders that bind specifically to their target through their complementary‐determining regions (CDRs). Small proteins with structurally preserved CDRs are promising antibodies mimetics. In this in silico study, we presented new antibody mimetics against the cancer marker epidermal growth factor receptor (EGFR) created by the CDRs grafting technique. Ten potential graft acceptor sites that efficiently immobilize the grafted CDR loops were selected from three small protein scaffolds using a computer. The three most involved CDR loops in antibody‐receptor interactions extracted from panitumumab antibody against the EGFR domain III crystal structure were then grafted to the selected scaffolds through the loop randomization technique. The combination of three CDR loops and 10 grafting sites revealed that three of the 36 combinations showed specific binding to EGFR DIII by binding energy calculations. Thus, the present strategy and selected small protein scaffolds are promising tools in the design of new binders against EGFR with high binding energy.
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Deterministic modelling of asymptomatic spread and disease stage progression in vaccine preventable infectious diseases Perspectives on benchmarking foundation models for network biology In silico designing and optimization of anti‐epidermal growth factor receptor scaffolds by complementary‐determining regions‐grafting technique Mathematical modeling of evolution of cell networks in epithelial tissues A  substructure‐aware graph neural network incorporating relation features for drug–drug interaction prediction
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