Israel Journal of Chemistry ( IF 2.3 ) Pub Date : 2023-06-21 , DOI: 10.1002/ijch.202300078 Ivan Smirnov 1, 2, 3 , Alexey Belogurov 1, 4 , Andrey Golovin 1, 5, 6 , Alexey Stepanov 7 , Hongkai Zhang 8 , G. Michael Blackburn 9 , Alexander Gabibov 1, 10, 11
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1 Introduction
The chemistry of Ig-mediated catalysis is based on the initial proposal of Linus Pauling, who wrote that catalytic activity of an antibody molecule might be achieved through its preferential stabilization of the transition state (TS) of the chemical reaction.1 That was followed by the anti-idiotypic network concept of the immune system proposed by Niels Ernie.2 Numerous experimental data of the last three decades has supported the validity of these hypotheses. These two proposals may explain, in part, the appearance of naturally occurring immunocatalysis in pathology of human and animal models of disease. Unfortunately, Fisher's Lock& Key theory3 as well as Pauling's TS theory4 have serious conceptual limitations that limit the improvement of kinetic parameters for immunocatalysis. These limitations arise from the need to provide appropriate dynamics of an Ig template to eliminate reaction products and effectively to simulate several TS niches. Effective catalysis requires an unusual configuration of the amino acids that make up CDRs. This requires the functional analysis of millions, rather than hundreds, of antibody clones.
Such obstacles were overcome in part by the pioneering implementation of combinatorial chemistry together with high throughput screening technologies. Indeed, selection from an extraordinarily large collection of Ig templates increases the likelihood that antibodies with the proper configuration of an active center can be isolated.5 Single B cell selection using microfluidic technology;6 combinatorial library screening of highly representative libraries;7 machine learning (ML) and structural computing;8 quantum mechanics/molecular mechanics (QM/MM) calculations and purely robotic procedures are used for the elaboration of more effective immunoglobulin-derived catalytic templates,9 ultimately leading to development of therapeutic antibodies with catalytic function and catalytic Chimeric Antigen Receptors (catCARs) with tunable pharmacokinetic parameters.10
中文翻译：
免疫球蛋白 Go：催化组合学的协同作用
1 简介
Ig 介导的催化化学基于 Linus Pauling 的最初提议，他写道，抗体分子的催化活性可能是通过其对化学反应过渡态 (TS) 的优先稳定来实现的。1随后 Niels Ernie 提出了免疫系统的抗独特型网络概念。2过去三十年的大量实验数据支持了这些假设的有效性。这两个提议可以部分解释人类和动物疾病模型病理学中自然发生的免疫催化的出现。不幸的是，Fisher 的锁与钥匙理论3以及 Pauling 的 TS 理论4具有严重的概念局限性，限制了免疫催化动力学参数的改进。这些限制源于需要提供 Ig 模板的适当动力学以消除反应产物并有效地模拟多个 TS 生态位。有效的催化需要构成 CDR 的氨基酸的不寻常配置。这需要对数百万个而不是数百个抗体克隆进行功能分析。
通过组合化学和高通量筛选技术的开创性实施，这些障碍在一定程度上得到了克服。事实上，从大量 Ig 模板中进行选择增加了分离具有正确活性中心构型的抗体的可能性。5使用微流控技术选择单个B细胞；6具有高度代表性的文库组合文库筛选；7机器学习（ML）和结构计算；8量子力学/分子力学 (QM/MM)计算和纯机器人程序用于细化更有效的免疫球蛋白衍生催化模板，9最终导致具有催化功能的治疗性抗体和催化嵌合抗原受体的开发（ catCAR）具有可调的药代动力学参数。10
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