Development and experimental validation of computational methods for human antibody affinity enhancement.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Junxin Li, Linbu Liao, Chao Zhang, Kaifang Huang, Pengfei Zhang, John Z H Zhang, Xiaochun Wan, Haiping Zhang
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引用次数: 0

Abstract

High affinity is crucial for the efficacy and specificity of antibody. Due to involving high-throughput screens, biological experiments for antibody affinity maturation are time-consuming and have a low success rate. Precise computational-assisted antibody design promises to accelerate this process, but there is still a lack of effective computational methods capable of pinpointing beneficial mutations within the complementarity-determining region (CDR) of antibodies. Moreover, random mutations often lead to challenges in antibody expression and immunogenicity. In this study, to enhance the affinity of a human antibody against avian influenza virus, a CDR library was constructed and evolutionary information was acquired through sequence alignment to restrict the mutation positions and types. Concurrently, a statistical potential methodology was developed based on amino acid interactions between antibodies and antigens to calculate potential affinity-enhanced antibodies, which were further subjected to molecular dynamics simulations. Subsequently, experimental validation confirmed that a point mutation enhancing 2.5-fold affinity was obtained from 10 designs, resulting in the antibody affinity of 2 nM. A predictive model for antibody-antigen interactions based on the binding interface was also developed, achieving an Area Under the Curve (AUC) of 0.83 and a precision of 0.89 on the test set. Lastly, a novel approach involving combinations of affinity-enhancing mutations and an iterative mutation optimization scheme similar to the Monte Carlo method were proposed. This study presents computational methods that rapidly and accurately enhance antibody affinity, addressing issues related to antibody expression and immunogenicity.

人类抗体亲和力增强计算方法的开发与实验验证。
高亲和力对抗体的有效性和特异性至关重要。由于涉及高通量筛选,抗体亲和力成熟的生物实验耗时长、成功率低。精确的计算辅助抗体设计有望加速这一过程,但目前仍缺乏有效的计算方法,无法准确定位抗体互补决定区(CDR)内的有益突变。此外,随机突变往往会导致抗体表达和免疫原性方面的挑战。在这项研究中,为了提高人类抗体对禽流感病毒的亲和力,我们构建了一个 CDR 库,并通过序列比对获得了进化信息,从而限制了突变的位置和类型。同时,根据抗体与抗原之间的氨基酸相互作用,开发了一种统计潜力方法,计算潜在的亲和力增强抗体,并对其进行分子动力学模拟。随后,实验验证证实,从 10 个设计中获得了亲和力增强 2.5 倍的点突变,使抗体亲和力达到 2 nM。此外,还开发了一个基于结合界面的抗体-抗原相互作用预测模型,在测试集上的曲线下面积(AUC)达到 0.83,精确度达到 0.89。最后,研究人员还提出了一种新方法,该方法涉及亲和力增强突变的组合以及与蒙特卡罗方法类似的迭代突变优化方案。本研究提出的计算方法可快速准确地增强抗体亲和力,解决抗体表达和免疫原性相关问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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