可解释的网络传播及其在扩展与SARS-CoV-2相互作用的人类蛋白质库中的应用
Interpretable Network Propagation with Application to Expanding the Repertoire of Human Proteins that Interact with SARS-CoV-2
摘要：
背景：近20年来，网络传播被广泛用于预测基因功能和表型。尽管这种方法很受欢迎，但很少有人注意到在这种情况下追踪出处的问题，例如，确定输入中的任何实验观察对每个预测的得分有多大贡献。结果如下：我们设计了一个包含两个新组件的网络传播框架，并将其应用于预测直接或间接与SARS-CoV-2蛋白相互作用的人类蛋白质。首先，我们将每个预测的出处追溯到实验验证的来源，在我们的案例中，这些来源是实验确定与病毒蛋白相互作用的人类蛋白质。其次，我们设计了一种技术，有助于减少用户手动调整参数。我们发现，对于每一个排名靠前的预测，其得分的最高贡献来自人类蛋白质相互作用网络中的直接邻居。我们进一步分析这些结果，以发展对SARS-CoV-2的功能性见解，扩展已知的生物学，如内质网应激，HSPA 5和抗凝剂之间的联系。结论：我们研究了我们的起源跟踪方法可以推广到广泛的一类基于网络的算法。我们为SARS-CoV-2社区提供了一个有用的资源，涉及许多以前没有记录的蛋白质与病毒感染的假定功能关系。这种资源包括可以机会性地重新定位以靶向这些蛋白质的潜在药物。我们还讨论了如何将我们的整体框架扩展到其他新出现的病毒。
英文摘要：
Background: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. Results: We design a network propagation framework with two novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. Conclusions: We examine how our provenance tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly-emerging viruses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
