Kiyoshi Masato, Caaveiro Jose M M, Miura Eri, Nagatoishi Satoru, Nakakido Makoto, Soga Shinji, Shirai Hiroki, Kawabata Shigeki, Tsumoto Kouhei
Medical Genome Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan ; Laboratory of Medical Proteomics, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan.
PLoS One. 2014 Jan 27;9(1):e87099. doi: 10.1371/journal.pone.0087099. eCollection 2014.
The optimization of antibodies is a desirable goal towards the development of better therapeutic strategies. The antibody 11K2 was previously developed as a therapeutic tool for inflammatory diseases, and displays very high affinity (4.6 pM) for its antigen the chemokine MCP-1 (monocyte chemo-attractant protein-1). We have employed a virtual library of mutations of 11K2 to identify antibody variants of potentially higher affinity, and to establish benchmarks in the engineering of a mature therapeutic antibody. The most promising candidates identified in the virtual screening were examined by surface plasmon resonance to validate the computational predictions, and to characterize their binding affinity and key thermodynamic properties in detail. Only mutations in the light-chain of the antibody are effective at enhancing its affinity for the antigen in vitro, suggesting that the interaction surface of the heavy-chain (dominated by the hot-spot residue Phe101) is not amenable to optimization. The single-mutation with the highest affinity is L-N31R (4.6-fold higher affinity than wild-type antibody). Importantly, all the single-mutations showing increase affinity incorporate a charged residue (Arg, Asp, or Glu). The characterization of the relevant thermodynamic parameters clarifies the energetic mechanism. Essentially, the formation of new electrostatic interactions early in the binding reaction coordinate (transition state or earlier) benefits the durability of the antibody-antigen complex. The combination of in silico calculations and thermodynamic analysis is an effective strategy to improve the affinity of a matured therapeutic antibody.
抗体优化是开发更好治疗策略的一个理想目标。抗体11K2先前被开发用作治疗炎症性疾病的工具，对其抗原趋化因子MCP-1（单核细胞趋化蛋白-1）表现出非常高的亲和力（4.6 pM）。我们利用11K2的虚拟突变文库来鉴定潜在亲和力更高的抗体变体，并在成熟治疗性抗体的工程设计中建立基准。通过表面等离子体共振对虚拟筛选中鉴定出的最有前景的候选物进行检测，以验证计算预测，并详细表征它们的结合亲和力和关键热力学性质。只有抗体轻链中的突变在体外能有效增强其对抗原的亲和力，这表明重链的相互作用表面（以热点残基苯丙氨酸101为主）不易优化。亲和力最高的单突变是L-N31R（亲和力比野生型抗体高4.6倍）。重要的是，所有显示亲和力增加的单突变都包含一个带电荷的残基（精氨酸、天冬氨酸或谷氨酸）。相关热力学参数的表征阐明了能量机制。本质上，在结合反应坐标早期（过渡态或更早）形成新的静电相互作用有利于抗体-抗原复合物的稳定性。计算机模拟计算和热力学分析相结合是提高成熟治疗性抗体亲和力的有效策略。
