Idrees Sobia, Paudel Keshav Raj
School of Biotechnology and Biomolecular Sciences University of New South Wales Sydney New South Wales Australia.
Centre for Inflammation Centenary Institute and the University of Technology Sydney School of Life Sciences Faculty of Science Sydney New South Wales Australia.
J Cell Commun Signal. 2024 Jan 19;18(1):e12014. doi: 10.1002/ccs3.12014. eCollection 2024 Mar.
Protein-protein interactions (PPIs) play a crucial role in various biological processes by establishing domain-motif (DMI) and domain-domain interactions (DDIs). While the existence of real DMIs/DDIs is generally assumed, it is rarely tested; therefore, this study extensively compared high-throughput methods and public PPI repositories as sources for DMI and DDI prediction based on the assumption that the human interactome provides sufficient data for the reliable identification of DMIs and DDIs. Different datasets from leading high-throughput methods (Yeast two-hybrid [Y2H], Affinity Purification coupled Mass Spectrometry [AP-MS], and Co-fractionation-coupled Mass Spectrometry) were assessed for their ability to capture DMIs and DDIs using known DMI/DDI information. High-throughput methods were not notably worse than PPI databases and, in some cases, appeared better. In conclusion, all PPI datasets demonstrated significant enrichment in DMIs and DDIs (-value <0.001), establishing Y2H and AP-MS as reliable methods for predicting these interactions. This study provides valuable insights for biologists in selecting appropriate methods for predicting DMIs, ultimately aiding in SLiM discovery.
蛋白质-蛋白质相互作用(PPI)通过建立结构域-基序(DMI)和结构域-结构域相互作用(DDI)在各种生物过程中发挥关键作用。虽然通常假定存在真实的DMI/DDI,但很少对其进行测试;因此,本研究基于人类相互作用组提供了足够的数据用于可靠识别DMI和DDI这一假设,广泛比较了高通量方法和公共PPI数据库作为DMI和DDI预测来源的情况。使用已知的DMI/DDI信息评估了来自领先高通量方法(酵母双杂交[Y2H]、亲和纯化联用质谱[AP-MS]和共分级联用质谱)的不同数据集捕获DMI和DDI的能力。高通量方法并不明显比PPI数据库差,在某些情况下似乎更好。总之,所有PPI数据集在DMI和DDI方面都显示出显著富集(-值<0.001),确立了Y2H和AP-MS作为预测这些相互作用的可靠方法。本研究为生物学家选择预测DMI的合适方法提供了有价值的见解,最终有助于发现短线性基序(SLiM)。