The aim of the present study was to develop a linear regression model aiding to a quick scan of the most important sites for mutation of an anticancer biologic trastuzumab. The important sites identified on trastuzumab can be used to carry out site-directed mutagenesis to improve the binding affinity of the drug towards its antigen, human epidermal growth factor receptor 2 (HER2). This will lead to low dosage requirement of the drug for treating cancer patients, which in turn help to cut the cost and combat development of resistance. A quantitative structure-activity relationship (QSAR) model was built by multiple linear regressions using genetic algorithm-based feature selection (GA-MLR) method using 48 dependent variables (dissociation constant Kd ) and 226 independent variables (theoretical descriptors generated using a proteometrics approach). The final QSAR model selected in the study was more on the basis of ability to predict accurately independent test data and generalization ability of the model rather than mere statistical significance of the model. With combined analysis of descriptors presented in final QSAR model and most frequent descriptors pooled from all solution models, it was demonstrated that the modeling procedure was able to bring on the factors important for antigen-antibody interactions with an example of HER2-trastuzumab interaction reported in previous experimental studies. This paper will allow the prediction of the most preferable site to mutate for improving the binding affinity of trastuzumab with HER2 and also will be helpful in selecting most preferable amino acids to substitute in the selected site for mutations. This is the novel report on proteometrics approach with autocorrelation formalism for antibody engineering, which can be extended to other antibody-antigen pairs.

中文翻译：

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蛋白质组学方法在鉴定功能性突变位点中的应用，以提高抗癌生物曲妥珠单抗与其抗原人表皮生长因子受体2的结合亲和力。

本研究的目的是开发一种线性回归模型，以帮助快速扫描最重要的抗癌生物曲妥珠单抗突变位点。在曲妥珠单抗上鉴定的重要位点可用于进行定点诱变，以改善药物对其抗原人表皮生长因子受体2（HER2）的结合亲和力。这将导致用于治疗癌症患者的药物的剂量需求低，进而有助于降低成本并抵抗耐药性的发展。使用基于遗传算法的特征选择（GA-MLR）方法通过多重线性回归，使用48个因变量（解离常数Kd）和226个独立变量（使用蛋白质计量学方法生成的理论描述符），通过线性回归建立了定量构效关系模型（QSAR） ）。在研究中选择的最终QSAR模型更多地基于准确预测独立测试数据的能力和模型的泛化能力，而不仅仅是模型的统计意义。结合最终QSAR模型中描述的描述符分析和所有解决方案模型中最常用的描述符的组合分析，证明了建模过程能够带来对抗原-抗体相互作用重要的因素，并报道了HER2-曲妥珠单抗的相互作用实例。以前的实验研究。本文将有助于预测最优选的位点发生突变，以改善曲妥珠单抗与HER2的结合亲和力，也将有助于选择最优选的氨基酸替代所选的突变位点。