Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies
Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies
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DOI：
10.7717/peerj.8199
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发表时间：
2019-12-18
期刊：
影响因子：
2.7
通讯作者：
Warwicker, Jim
中科院分区：
文献类型：
--
作者：
Hebditch, Max;Warwicker, Jim
更好地了解介导蛋白质溶解度和抗聚集性的特性对于开发生物制药非常重要，更普遍的是在生物技术和合成生物学中。最近获取的抗体生物物理特性的大型数据集使得能够搜索预测模型。在本报告中，机器学习方法用于推导 12 种生物物理特性的模型。在分析模型时保持物理化学视角，观察到模型很大程度上根据电荷（交叉相互作用测量）和疏水性（自相互作用方法）进行聚类。在某些情况下，这两个特性也有重叠，例如在疏水相互作用色谱变化的新解释中。由于模型是根据抗体可变环的差异开发的，下一阶段是将模型扩展到更多样化的蛋白质组。
Improved understanding of properties that mediate protein solubility and resistance to aggregation are important for developing biopharmaceuticals, and more generally in biotechnology and synthetic biology. Recent acquisition of large datasets for antibody biophysical properties enables the search for predictive models. In this report, machine learning methods are used to derive models for 12 biophysical properties. A physicochemical perspective is maintained in analysing the models, leading to the observation that models cluster largely according to charge (cross-interaction measurements) and hydrophobicity (self-interaction methods). These two properties also overlap in some cases, for example in a new interpretation of variation in hydrophobic interaction chromatography. Since the models are developed from differences of antibody variable loops, the next stage is to extend models to more diverse protein sets.