化学
生物信息学
催化作用
克隆选择
模板
选择(遗传算法)
抗体
纳米技术
计算生物学
分子力学
组合化学
分子动力学
计算机科学
人工智能
计算化学
生物化学
基因
遗传学
免疫学
生物
材料科学
作者
Иван Смирнов,Alexey A. Belogurov,Andrey V. Golovin,А. В. Степанов,Hongkai Zhang,G. Michael Blackburn,Alexander Gabibov
标识
DOI:10.1002/ijch.202300078
摘要
Abstract The maturation of B cells leads to the synthesis of highly evolved immunoglobulins (Igs) that enable efficient antigen‐antibody recognition. Here we discuss a non‐opportunistic, combinatorial concept of “maturation” of Igs in silico for the production of artificially‐evolved immunocatalysts. Several recent breakthroughs including: (i) single B cell selection using microfluidic technology (ii) combinatorial approaches powered by library screening (iii) structural computing and machine learning, (iv) quantum mechanics/molecular mechanics (QM/MM) evaluations of catalytic reaction leading to optimistic prospect for the elaboration of more effective immunoglobulin‐derived catalytic templates and redirection the selection process to a purely robotic procedure. The synergy of these approaches enable catalytic antibody become a great prospect for biomedical purposes. The most recent breakthroughs include therapeutic antibodies and catalytic C himeric A ntigen R eceptors (catCARs) with controllable pharmacokinetic parameters.
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