Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK. Electronic address: https://twitter.com/@AlissaHummer.
Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK. Electronic address: https://twitter.com/@brennanaba.
Curr Opin Struct Biol. 2022 Jun;74:102379. doi: 10.1016/j.sbi.2022.102379. Epub 2022 Apr 28.
Antibodies are currently the most important class of biotherapeutics and are used to treat numerous diseases. Recent advances in computational methods are ushering in a new era of antibody design, driven in part by accurate structure prediction. Previously, structure-based antibody design has been limited to a relatively small number of cases where accurate structures or models of both the target antigen and antibody were available. As we move towards a time where it is possible to accurately model most antibodies and antigens, and to reliably predict their binding site, there is vast potential for true computational antibody design. In this review, we describe the latest methods that promise to launch a paradigm shift towards entirely in silico structure-based antibody design.
抗体是目前最重要的一类生物疗法，用于治疗多种疾病。计算方法的最新进展正在开创抗体设计的新时代，这在一定程度上是由准确的结构预测驱动的。以前，基于结构的抗体设计仅限于相对较少的情况，即目标抗原和抗体的准确结构或模型都可用。随着我们迈向能够准确模拟大多数抗体和抗原，并可靠预测其结合部位的时代，真正的计算抗体设计具有巨大的潜力。在这篇综述中，我们描述了最新的方法，这些方法有望引发向完全基于计算机的结构抗体设计的范式转变。
