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Deep mutational scanning for therapeutic antibody engineering.
Hanning, Kyrin R; Minot, Mason; Warrender, Annmaree K; Kelton, William; Reddy, Sai T.
  • Hanning KR; Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand.
  • Minot M; Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich, Basel 4058, Switzerland.
  • Warrender AK; Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand.
  • Kelton W; Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand. Electronic address: wkelton@waikato.ac.nz.
  • Reddy ST; Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich, Basel 4058, Switzerland. Electronic address: sai.reddy@bsse.ethz.ch.
Trends Pharmacol Sci ; 43(2): 123-135, 2022 02.
Article in English | MEDLINE | ID: covidwho-1569095
ABSTRACT
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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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Prognostic study Topics: Variants Limits: Humans Language: English Journal: Trends Pharmacol Sci Year: 2022 Document Type: Article Affiliation country: J.tips.2021.11.010

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Prognostic study Topics: Variants Limits: Humans Language: English Journal: Trends Pharmacol Sci Year: 2022 Document Type: Article Affiliation country: J.tips.2021.11.010