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OptMAVEn – A New Framework for the de novo Design of Antibody Variable Region Models Targeting Specific Antigen Epitopes

机译:OptMAVEn –一种针对特定抗原表位的抗体可变区模型从头设计的新框架

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摘要

Antibody-based therapeutics provides novel and efficacious treatments for a number of diseases. Traditional experimental approaches for designing therapeutic antibodies rely on raising antibodies against a target antigen in an immunized animal or directed evolution of antibodies with low affinity for the desired antigen. However, these methods remain time consuming, cannot target a specific epitope and do not lead to broad design principles informing other studies. Computational design methods can overcome some of these limitations by using biophysics models to rationally select antibody parts that maximize affinity for a target antigen epitope. This has been addressed to some extend by OptCDR for the design of complementary determining regions. Here, we extend this earlier contribution by addressing the de novo design of a model of the entire antibody variable region against a given antigen epitope while safeguarding for immunogenicity (Optimal Method for Antibody Variable region Engineering, OptMAVEn). OptMAVEn simulates in silico the in vivo steps of antibody generation and evolution, and is capable of capturing the critical structural features responsible for affinity maturation of antibodies. In addition, a humanization procedure was developed and incorporated into OptMAVEn to minimize the potential immunogenicity of the designed antibody models. As case studies, OptMAVEn was applied to design models of neutralizing antibodies targeting influenza hemagglutinin and HIV gp120. For both HA and gp120, novel computational antibody models with numerous interactions with their target epitopes were generated. The observed rates of mutations and types of amino acid changes during in silico affinity maturation are consistent with what has been observed during in vivo affinity maturation. The results demonstrate that OptMAVEn can efficiently generate diverse computational antibody models with both optimized binding affinity to antigens and reduced immunogenicity.
机译:基于抗体的疗法为多种疾病提供了新颖有效的疗法。设计治疗性抗体的传统实验方法依赖于在免疫动物中产生针对靶抗原的抗体或对所需抗原具有低亲和力的抗体的定向进化。但是,这些方法仍然很耗时,不能针对特定的抗原决定簇,也不能导致广泛的设计原则告知其他研究。计算设计方法可以通过使用生物物理模型合理地选择对靶抗原表位具有最大亲和力的抗体部分来克服这些局限性。 OptCDR已在某种程度上解决了互补决定区的设计问题。在这里,我们通过解决针对给定抗原表位的整个抗体可变区模型的从头设计,同时维护免疫原性(抗体可变区工程的最佳方法,OptMAVEn),扩展了这一早期的贡献。 OptMAVEn在计算机上模拟抗体生成和演化的体内步骤,并能够捕获负责抗体亲和力成熟的关键结构特征。此外,开发了人源化程序并将其并入OptMAVEn,以最大程度地减少设计的抗体模型的潜在免疫原性。作为案例研究,OptMAVEn被用于设计针对流感血凝素和HIV gp120的中和抗体的模型。对于HA和gp120,产生了与它们的靶表位具有许多相互作用的新颖的计算抗体模型。在计算机亲和力成熟期间观察到的突变率和氨基酸变化类型与在体内亲和力成熟期间观察到的一致。结果表明,OptMAVEn可以有效生成具有优化的抗原结合亲和力和降低的免疫原性的多种计算抗体模型。

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