PDF(Pubmed)
Abstract：
Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragment variable (scFv) derived from monoclonal antibodies (mAbs). scFv selection is consequently a fundamental step for CAR construction, to ensure accurate and effective CAR signaling toward tumor antigen binding. However, conventional in vitro and in vivo biological approaches to compare different scFv-derived CARs are expensive and labor-intensive. With the aim to predict the finest scFv binding before CAR-T cell engineering, we performed artificial intelligence (AI)-guided molecular docking and steered molecular dynamics analysis of different anti-CD30 mAb clones. Virtual computational scFv screening showed comparable results to surface plasmon resonance (SPR) and functional CAR-T cell in vitro and in vivo assays, respectively, in terms of binding capacity and anti-tumor efficacy. The proposed fast and low-cost in silico analysis has the potential to advance the development of novel CAR constructs, with a substantial impact on reducing time, costs, and the need for laboratory animal use.
摘要：
嵌合抗原受体（CAR）T细胞代表了一种革命性的免疫疗法，可通过源自单克隆抗体（mAb）的独特单链片段变量（scFv）进行特异性肿瘤识别。因此，SCFv选择是CAR构建的基本步骤，以确保针对肿瘤抗原结合的准确有效的CAR信号传导。然而,比较不同scFv衍生CAR的常规体外和体内生物学方法是昂贵且劳动密集型的。为了预测CAR-T细胞工程之前最好的scFv结合，我们对不同的抗CD30mAb克隆进行了人工智能(AI)引导的分子对接和引导的分子动力学分析.虚拟计算scFv筛选显示了与表面等离子体共振（SPR）和功能性CAR-T细胞在体外和体内测定中相当的结果，分别,在结合能力和抗肿瘤功效方面。提出的快速和低成本的计算机分析有可能促进新型CAR构建体的开发。对减少时间有重大影响，成本,以及使用实验动物的需要。
