Department of Medical BioSciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands.
Medical Microbiology, Radboud Community for Infectious Diseases, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands.
J Proteome Res. 2024 Oct 4;23(10):4467-4479. doi: 10.1021/acs.jproteome.4c00414. Epub 2024 Sep 12.
Complexome profiling is an experimental approach to identify interactions by integrating native separation of protein complexes and quantitative mass spectrometry. In a typical complexome profile, thousands of proteins are detected across typically ≤100 fractions. This relatively low resolution leads to similar abundance profiles between proteins that are not necessarily interaction partners. To address this challenge, we introduce the Gaussian Interaction Profiler (GIP), a Gaussian mixture modeling-based clustering workflow that assigns protein clusters by modeling the migration profile of each cluster. Uniquely, the GIP offers a way to prioritize actual interactors over spuriously comigrating proteins. Using previously analyzed human fibroblast complexome profiles, we show good performance of the GIP compared to other state-of-the-art tools. We further demonstrate GIP utility by applying it to complexome profiles from the transmissible lifecycle stage of malaria parasites. We unveil promising novel associations for future experimental verification, including an interaction between the vaccine target Pfs47 and the hypothetical protein PF3D7_0417000. Taken together, the GIP provides methodological advances that facilitate more accurate and automated detection of protein complexes, setting the stage for more varied and nuanced analyses in the field of complexome profiling. The complexome profiling data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD050751.
复杂蛋白质组分析是一种通过整合蛋白质复合物的天然分离和定量质谱来鉴定相互作用的实验方法。在典型的复杂蛋白质组分析中，通常在≤100 个馏分中检测到数千种蛋白质。这种相对较低的分辨率导致了并非相互作用伙伴的蛋白质之间相似的丰度分布。为了解决这个挑战，我们引入了高斯相互作用分析器（Gaussian Interaction Profiler，GIP），这是一种基于高斯混合模型的聚类工作流程，通过对每个聚类的迁移分布进行建模来分配蛋白质聚类。GIP 的独特之处在于提供了一种区分真正相互作用蛋白和假共迁移蛋白的方法。利用之前分析的人类成纤维细胞复杂蛋白质组分析，我们展示了 GIP 与其他最先进工具相比的良好性能。我们进一步通过将其应用于疟原虫可传播生命周期阶段的复杂蛋白质组分析来证明 GIP 的实用性。我们揭示了有希望的新关联，供未来的实验验证，包括疫苗靶标 Pfs47 与假定蛋白 PF3D7_0417000 之间的相互作用。总之，GIP 提供了方法学上的进步，有助于更准确和自动地检测蛋白质复合物，为复杂蛋白质组分析领域的更广泛和细致的分析奠定了基础。复杂蛋白质组分析数据已被存入 ProteomeXchange 联盟，数据集标识符为 PXD050751。
