Deep mutational scanning for therapeutic antibody engineering

计算生物学 抗体 医学 心理学 生物 遗传学
作者
Kyrin R. Hanning,Mason Minot,Annmaree K. Warrender,William Kelton,Sai T. Reddy
出处
期刊:Trends in Pharmacological Sciences [Elsevier BV]
卷期号:43 (2): 123-135 被引量:37
标识
DOI:10.1016/j.tips.2021.11.010
摘要

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.
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