美国科学家最近开发出一种计算机建模方法，它能够通过预测抗体的结构变化来提升药物的效力。相关研究论文9月23日在线发表于《自然—生物技术》上。
该研究成果源自美国麻省理工学院（MIT）Dane Wittrup和Bruce Tidor教授在实验和计算机模拟上的倾力合作。研究人员分析了一种特殊抗体的氨基酸取代的多种可能性，并计算出哪种取代方式所产生的结构变化能够导致抗体与标靶发生最紧密的作用。
利用该模型，研究人员已经对抗结肠癌药物爱必妥（Erbitux）进行了改造，并使抗体与标靶的亲和力增加到原始蛋白分子的10倍。此外，他们将该方法应用于一种抗溶解酵素抗体D44.1，提升了它的效力。论文第一作者Shaun Lippow表示，“将抗体蛋白结构和预测信息结合起来，我们就能做出最合理的选择，从而提升蛋白的功能。”
美国国立常规医学研究所（NIGMS）负责计算生物学项目的Janna Wehrle表示，“该项研究是现代计算技术用于加速药物开发的完美范例。”
传统的抗体药物开发方法主要通过自然选择，即先从小鼠体内提取出抗体，然后在实验室中进行培养，使其进化，并检测其功效。该方法从时间上和研究人员可操控的层面上都不理想。相比之下，MIT科学家的新方法能够快速分析抗体大量的可能变异和构造变化，并预测出该蛋白分子和标靶的亲和力。
研究人员表示，蛋白建模方法能够减少抗体药物开发的时间和成本，并有助于科学家设计改造其他用途的蛋白，比如将生物质转化为能源的酶。（科学网 任霄鹏/编译）
原始出处:
Nature Biotechnology
Published online: 23 September 2007 | doi:10.1038/nbt1336
Computational design of antibody-affinity improvement beyond in vivo maturation
Shaun M Lippow1,4, K Dane Wittrup1,2 & Bruce Tidor2,3
Antibodies are used extensively in diagnostics and as therapeutic agents. Achieving high-affinity binding is important for expanding detection limits, extending dissociation half-times, decreasing drug dosages and increasing drug efficacy. However, antibody-affinity maturation in vivo often fails to produce antibody drugs of the targeted potency1, making further affinity maturation in vitro by directed evolution or computational design necessary. Here we present an iterative computational design procedure that focuses on electrostatic binding contributions and single mutants. By combining multiple designed mutations, a tenfold affinity improvement to 52 pM was engineered into the anti–epidermal growth factor receptor drug cetuximab (Erbitux), and a 140-fold improvement in affinity to 30 pM was obtained for the anti-lysozyme model antibody D44.1. The generality of the methods was further demonstrated through identification of known affinity-enhancing mutations in the therapeutic antibody bevacizumab (Avastin) and the model anti-fluorescein antibody 4-4-20. These results demonstrate computational capabilities for enhancing and accelerating the development of protein reagents and therapeutics.
Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
Present address: Codon Devices, Inc., One Kendall Square, Building 300, Cambridge, Massachusetts 02139, USA.
Correspondence to: K Dane Wittrup1,2 e-mail: tidor@mit.edu
Correspondence to: Bruce Tidor2,3 e-mail: wittrup@mit.edu
