Center for Drug Design Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki 567-0085, Japan.
Department of Bioengineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
Protein Eng Des Sel. 2019 Dec 31;32(9):423-431. doi: 10.1093/protein/gzaa006.
Computer-guided library generation is a plausible strategy to optimize antibodies. Herein, we report the improvement of the affinity of a single-domain camelid antibody for its antigen using such approach. We first conducted experimental and computational alanine scanning to describe the precise energetic profile of the antibody-antigen interaction surface. Based on this characterization, we hypothesized that in-silico mutagenesis could be employed to guide the development of a small library for phage display with the goal of improving the affinity of an antibody for its antigen. Optimized antibody mutants were identified after three rounds of selection, in which an alanine residue at the core of the antibody-antigen interface was substituted by residues with large side-chains, generating diverse kinetic responses, and resulting in greater affinity (>10-fold) for the antigen.
计算机指导文库生成是优化抗体的一种可行策略。在此,我们报告了使用这种方法来提高单域骆驼科抗体对其抗原的亲和力。我们首先进行了实验和计算丙氨酸扫描,以描述抗体-抗原相互作用表面的精确能量分布。基于这一特征,我们假设可以在计算机上进行诱变,以指导噬菌体展示文库的开发,从而提高抗体对其抗原的亲和力。经过三轮选择,确定了优化后的抗体突变体,其中在抗体-抗原界面的核心处用具有较大侧链的残基取代了丙氨酸残基,产生了不同的动力学响应,从而使抗原的亲和力提高了(> 10 倍)。