Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand.
Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich, Basel 4058, Switzerland.
Trends Pharmacol Sci. 2022 Feb;43(2):123-135. doi: 10.1016/j.tips.2021.11.010. Epub 2021 Dec 9.
The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.
单克隆抗体 (mAb) 候选药物的生物物理和功能特性通常通过蛋白质工程方法进行改进,以提高临床疗效的可能性。一种新兴的方法是深度突变扫描 (DMS),它将彻底的蛋白质诱变和功能筛选与深度测序和生物信息学相结合。DMS 的应用显著提高了几种临床前抗体的亲和力、特异性和稳定性,同时还开辟了一些新的应用,如引入多特异性结合特性。DMS 也已直接应用于靶抗原,以精确绘制抗体结合表位,特别是分析病毒靶标的突变逃逸潜力(例如,SARS-CoV-2 变体)。最后,DMS 与机器学习相结合,使治疗性抗体的计算筛选和工程取得了进展。