Cells detect changes in their external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and, thus, challenging to detect using traditional experimental techniques. Here, we tackle this challenge using a computational framework. The primary focus of the framework is to develop new tools to identify interactions between domains in the immunoglobulin (Ig) fold, which is the most abundant domain family in cell surface proteins. These interactions could be formed between ligands and receptors from different cells or between proteins on the same cell surface. In practice, we collected all structural data on Ig domain interactions and transformed them into an interface fragment pair library. A high-dimensional profile can then be constructed from the library for a given pair of query protein sequences. Multiple machine learning models were used to read this profile so that the probability of interaction between the query proteins could be predicted. We tested our models on an experimentally derived dataset that contains 564 cell surface proteins in humans. The cross-validation results show that we can achieve higher than 70% accuracy in identifying the PPIs within this dataset. We then applied this method to a group of 46 cell surface proteins in Caenorhabditis elegans. We screened every possible interaction between these proteins. Many interactions recognized by our machine learning classifiers have been experimentally confirmed in the literature. In conclusion, our computational platform serves as a useful tool to help identify potential new interactions between cell surface proteins in addition to current state-of-the-art experimental techniques. The tool is freely accessible for use by the scientific community. Moreover, the general framework of the machine learning classification can also be extended to study the interactions of proteins in other domain superfamilies.

中文翻译：

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通过机器学习预测细胞表面蛋白之间的相互作用

细胞检测外部环境的变化或通过其表面的蛋白质相互交流。这些细胞表面蛋白形成复杂的相互作用网络以实现其功能。细胞表面蛋白之间的相互作用是高度动态的，因此使用传统实验技术检测具有挑战性。在这里，我们使用计算框架来应对这一挑战。该框架的主要重点是开发新工具来识别免疫球蛋白 (Ig) 折叠中结构域之间的相互作用，免疫球蛋白 (Ig) 折叠是细胞表面蛋白中最丰富的结构域家族。这些相互作用可以在来自不同细胞的配体和受体之间或同一细胞表面上的蛋白质之间形成。在实践中，我们收集了有关 Ig 结构域相互作用的所有结构数据，并将其转化为界面片段对库。然后可以从文库中构建给定的一对查询蛋白质序列的高维概况。使用多个机器学习模型来读取该配置文件，以便可以预测查询蛋白质之间相互作用的概率。我们在包含 564 种人类细胞表面蛋白的实验数据集上测试了我们的模型。交叉验证结果表明，我们在识别该数据集中的 PPI 时可以达到 70% 以上的准确率。然后我们将此方法应用于秀丽隐杆线虫中的一组 46 种细胞表面蛋白。我们筛选了这些蛋白质之间所有可能的相互作用。我们的机器学习分类器识别的许多交互已经在文献中得到实验证实。总之，除了当前最先进的实验技术之外，我们的计算平台还可以作为一种有用的工具来帮助识别细胞表面蛋白质之间潜在的新相互作用。该工具可供科学界免费使用。此外，机器学习分类的总体框架还可以扩展到研究其他域超家族中蛋白质的相互作用。