Yugandhar Kumar, Gupta Shagun, Yu Haiyuan
Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA.
Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA.
Comput Struct Biotechnol J. 2019 Jun 20;17:805-811. doi: 10.1016/j.csbj.2019.05.007. eCollection 2019.
Studying protein-protein interaction networks provide key evidence for the underlying molecular mechanisms. Mass spectrometry-based proteomic approaches have been playing a pivotal role in deciphering these interaction networks, along with precise quantification for individual interactions. In this mini-review we discuss the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks. We then summarize the down-stream computational strategies for identification and quantification of interactions from those techniques. Finally, we highlight the challenges and limitations of current computational pipelines in eliminating false positive interactors, followed by a summary of the innovative algorithms to address these issues, along with the scope for future improvements.
研究蛋白质-蛋白质相互作用网络为潜在的分子机制提供了关键证据。基于质谱的蛋白质组学方法在破译这些相互作用网络以及对单个相互作用进行精确量化方面一直发挥着关键作用。在本综述中，我们讨论了用于定性和定量阐明蛋白质-蛋白质相互作用网络的现有技术和方法。然后，我们总结了从这些技术中识别和量化相互作用的下游计算策略。最后，我们强调了当前计算流程在消除假阳性相互作用物方面的挑战和局限性，接着总结了用于解决这些问题的创新算法以及未来改进的空间。
