Computational Biology and Clinical Informatics, Baker Institute, Melbourne, VIC 3004, Australia.
Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia.
Nucleic Acids Res. 2020 Jul 2;48(W1):W125-W131. doi: 10.1093/nar/gkaa389.
While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.
虽然抗体正成为一种日益重要的治疗类别，特别是在个性化医学中，但它们的开发和优化在很大程度上是通过实验探索进行的。虽然已经有许多努力来开发计算工具来指导理性抗体工程，但大多数方法在应用于抗体设计时准确性有限，而且主要限于一次分析单个点突变。为了克服这一差距，我们整理了一个包含 242 个实验确定的抗体-靶复合物中多个点突变对结合亲和力影响的数据集（89 个增加和 153 个降低结合亲和力）。在这里，我们表明，通过使用我们基于图的特征和原子相互作用信息，我们可以准确分析多点突变对抗原结合亲和力的影响。我们的方法在交叉验证和两个独立的盲测中均优于其他可用工具，达到了高达 0.95 的 Pearson 相关系数。我们已经将我们的新方法 mmCSM-AB 实现为一个网络服务器，该服务器可以帮助指导抗体设计中的亲和力成熟过程。mmCSM-AB 可免费在 http://biosig.unimelb.edu.au/mmcsm_ab/ 获取。
