Qiu Tianyi, Xiao Han, Zhang Qingchen, Qiu Jingxuan, Yang Yiyan, Wu Dingfeng, Cao Zhiwei, Zhu Ruixin
Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Department of Computer Science, University of Helsinki, Helsinki, FI-00014, Finland.
PLoS One. 2015 Apr 22;10(4):e0122416. doi: 10.1371/journal.pone.0122416. eCollection 2015.
Despite the high specificity between antigen and antibody binding, similar epitopes can be recognized or cross-neutralized by paratopes of antibody with different binding affinities. How to accurately characterize this slight variation which may or may not change the antigen-antibody binding affinity is a key issue in this area. In this report, by combining cylinder model with shell structure model, a new fingerprint was introduced to describe both the structural and physical-chemical features of the antigen and antibody protein. Furthermore, beside the description of individual protein, the specific epitope-paratope interaction fingerprint (EPIF) was developed to reflect the bond and the environment of the antigen-antibody interface. Finally, Proteochemometric Modeling of the antigen-antibody interaction was established and evaluated on 429 antigen-antibody complexes. By using only protein descriptors, our model achieved the best performance (R2 = 0.91, Qtest(2) = 0.68) among peers. Further, together with EPIF as a new cross-term, our model (R2 = 0.92, Qtest(2) = 0.74) can significantly outperform peers with multiplication of ligand and protein descriptors as a cross-term (R2 ≤ 0.81, Qtest(2) ≤ 0.44). Results illustrated that: 1) our newly designed protein fingerprints and EPIF can better describe the antigen-antibody interaction; 2) EPIF is a better and specific cross-term in Proteochemometric Modeling for antigen-antibody interaction. The fingerprints designed in this study will provide assistance to the description of antigen-antibody binding, and in future, it may be valuable help for the high-throughput antibody screening. The algorithm is freely available on request.
尽管抗原与抗体结合具有高度特异性，但具有不同结合亲和力的抗体互补位仍可识别相似表位或进行交叉中和。如何准确表征这种可能改变或不改变抗原-抗体结合亲和力的细微差异是该领域的关键问题。在本报告中，通过将圆柱模型与壳结构模型相结合，引入了一种新的指纹图谱来描述抗原和抗体蛋白的结构及物理化学特征。此外，除了对单个蛋白质的描述，还开发了特异性表位-互补位相互作用指纹图谱（EPIF）以反映抗原-抗体界面的结合情况和环境。最后，建立了抗原-抗体相互作用的蛋白质化学计量学模型，并在429对抗原-抗体复合物上进行了评估。仅使用蛋白质描述符，我们的模型在同类研究中表现最佳（R2 = 0.91，Qtest(2) = 0.68）。此外，将EPIF作为新的交叉项，我们的模型（R2 = 0.92，Qtest(2) = 0.74）相比以配体和蛋白质描述符的乘积作为交叉项的同类模型（R2 ≤ 0.81，Qtest(2) ≤ 0.44）能显著胜出。结果表明：1）我们新设计的蛋白质指纹图谱和EPIF能更好地描述抗原-抗体相互作用；2）在抗原-抗体相互作用的蛋白质化学计量学模型中，EPIF是一个更好的特异性交叉项。本研究设计的指纹图谱将有助于描述抗原-抗体结合，未来可能对高通量抗体筛选有重要帮助。该算法可应要求免费获取。
