Mathematics Department and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
BMC Bioinformatics. 2013 Oct 4;14:299. doi: 10.1186/1471-2105-14-299.
Comprehensive protein-protein interaction (PPI) maps are a powerful resource for uncovering the molecular basis of genetic interactions and providing mechanistic insights. Over the past decade, high-throughput experimental techniques have been developed to generate PPI maps at proteome scale, first using yeast two-hybrid approaches and more recently via affinity purification combined with mass spectrometry (AP-MS). Unfortunately, data from both protocols are prone to both high false positive and false negative rates. To address these issues, many methods have been developed to post-process raw PPI data. However, with few exceptions, these methods only analyze binary experimental data (in which each potential interaction tested is deemed either observed or unobserved), neglecting quantitative information available from AP-MS such as spectral counts.
We propose a novel method for incorporating quantitative information from AP-MS data into existing PPI inference methods that analyze binary interaction data. Our approach introduces a probabilistic framework that models the statistical noise inherent in observations of co-purifications. Using a sampling-based approach, we model the uncertainty of interactions with low spectral counts by generating an ensemble of possible alternative experimental outcomes. We then apply the existing method of choice to each alternative outcome and aggregate results over the ensemble. We validate our approach on three recent AP-MS data sets and demonstrate performance comparable to or better than state-of-the-art methods. Additionally, we provide an in-depth discussion comparing the theoretical bases of existing approaches and identify common aspects that may be key to their performance.
Our sampling framework extends the existing body of work on PPI analysis using binary interaction data to apply to the richer quantitative data now commonly available through AP-MS assays. This framework is quite general, and many enhancements are likely possible. Fruitful future directions may include investigating more sophisticated schemes for converting spectral counts to probabilities and applying the framework to direct protein complex prediction methods.
全面的蛋白质-蛋白质相互作用（PPI）图谱是揭示遗传相互作用的分子基础并提供机制见解的有力资源。在过去的十年中，已经开发了高通量实验技术来生成蛋白质组规模的 PPI 图谱，首先使用酵母双杂交方法，最近则通过亲和纯化结合质谱（AP-MS）。不幸的是，两种方案的数据都容易出现高假阳性和假阴性率。为了解决这些问题，已经开发了许多方法来对原始 PPI 数据进行后处理。然而，除了少数例外，这些方法仅分析二进制实验数据（其中测试的每个潜在相互作用都被视为观察到或未观察到），忽略了来自 AP-MS 的定量信息，例如光谱计数。
我们提出了一种将 AP-MS 数据的定量信息纳入分析二进制相互作用数据的现有 PPI 推断方法的新方法。我们的方法引入了一个概率框架，该框架对共纯化观察中固有的统计噪声进行建模。我们使用基于抽样的方法，通过生成可能的替代实验结果的集合来对具有低光谱计数的相互作用的不确定性进行建模。然后，我们将现有的首选方法应用于每个替代结果，并在集合上汇总结果。我们在三个最近的 AP-MS 数据集上验证了我们的方法，并证明了与最先进方法相当或更好的性能。此外，我们还提供了对现有方法理论基础的深入讨论，并确定了可能对其性能至关重要的共同方面。
我们的抽样框架将使用二进制相互作用数据进行 PPI 分析的现有工作扩展到现在通过 AP-MS 测定通常可用的更丰富的定量数据。该框架非常通用，并且可能会有许多增强功能。未来有希望的方向可能包括研究将光谱计数转换为概率的更复杂方案，并将该框架应用于直接蛋白质复合物预测方法。
