Chen Zhengshan, Chi Xiangyang, Fan Pengfei, Zhang Guanying, Wang Meirong, Yu Changming, Chen Wei
Institute of Biotechnology, Academy of Military Medical Science, Chinese Academy of Military Sciences, Beijing 100071, China.
Sheng Wu Gong Cheng Xue Bao. 2018 Jun 25;34(6):993-1001. doi: 10.13345/j.cjb.170493.
Given the increasing exploitation of antibodies in different contexts such as molecular diagnostics and therapeutics, it would be beneficial to unravel properties of antigen-antibody interaction with modeling of computational protein-protein docking, especially, in the absence of a cocrystal structure. However, obtaining a native-like antigen-antibody structure remains challenging due in part to failing to reliably discriminate accurate from inaccurate structures among tens of thousands of decoys after computational docking with existing scoring function. We hypothesized that some important physicochemical and energetic features could be used to describe antigen-antibody interfaces and identify native-like antigen-antibody structure. We prepared a dataset, a subset of Protein-Protein Docking Benchmark Version 4.0, comprising 37 nonredundant 3D structures of antigen-antibody complexes, and used it to train and test multivariate logistic regression equation which took several important physicochemical and energetic features of decoys as dependent variables. Our results indicate that the ability to identify native-like structures of our method is superior to ZRANK and ZDOCK score for the subset of antigen-antibody complexes. And then, we use our method in workflow of predicting epitope of anti-Ebola glycoprotein monoclonal antibody-4G7 and identify three accurate residues in its epitope.
鉴于抗体在分子诊断和治疗等不同领域的应用日益广泛，利用计算蛋白质-蛋白质对接模型来揭示抗原-抗体相互作用的特性将大有裨益，尤其是在缺乏共晶体结构的情况下。然而，获得类似天然的抗原-抗体结构仍然具有挑战性，部分原因是在使用现有评分函数进行计算对接后，难以从数以万计的诱饵结构中可靠地区分准确结构和不准确结构。我们假设一些重要的物理化学和能量特征可用于描述抗原-抗体界面并识别类似天然的抗原-抗体结构。我们准备了一个数据集，它是蛋白质-蛋白质对接基准版本4.0的一个子集，包含37个非冗余的抗原-抗体复合物三维结构，并将其用于训练和测试多变量逻辑回归方程，该方程将诱饵的几个重要物理化学和能量特征作为因变量。我们的结果表明，对于抗原-抗体复合物子集，我们的方法识别类似天然结构的能力优于ZRANK和ZDOCK评分。然后，我们将我们的方法应用于预测抗埃博拉糖蛋白单克隆抗体-4G7表位的工作流程中，并在其表位中识别出三个准确的残基。
