Kyrilis Fotis L, Belapure Jaydeep, Kastritis Panagiotis L
Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Front Mol Biosci. 2021 Apr 15;8:660542. doi: 10.3389/fmolb.2021.660542. eCollection 2021.
Native cell extracts hold great promise for understanding the molecular structure of ordered biological systems at high resolution. This is because higher-order biomolecular interactions, dubbed as protein communities, may be retained in their (near-)native state, in contrast to extensively purifying or artificially overexpressing the proteins of interest. The distinct machine-learning approaches are applied to discover protein-protein interactions within cell extracts, reconstruct dedicated biological networks, and report on protein community members from various organisms. Their validation is also important, e.g., by the cross-linking mass spectrometry or cell biology methods. In addition, the cell extracts are amenable to structural analysis by cryo-electron microscopy (cryo-EM), but due to their inherent complexity, sorting structural signatures of protein communities derived by cryo-EM comprises a formidable task. The application of image-processing workflows inspired by machine-learning techniques would provide improvements in distinguishing structural signatures, correlating proteomic and network data to structural signatures and subsequently reconstructed cryo-EM maps, and, ultimately, characterizing unidentified protein communities at high resolution. In this review article, we summarize recent literature in detecting protein communities from native cell extracts and identify the remaining challenges and opportunities. We argue that the progress in, and the integration of, machine learning, cryo-EM, and complementary structural proteomics approaches would provide the basis for a multi-scale molecular description of protein communities within native cell extracts.
天然细胞提取物在高分辨率下理解有序生物系统的分子结构方面具有巨大潜力。这是因为与广泛纯化或人工过表达感兴趣的蛋白质相比，被称为蛋白质群落的高阶生物分子相互作用可能以其（近）天然状态得以保留。不同的机器学习方法被用于发现细胞提取物中的蛋白质-蛋白质相互作用、重建专门的生物网络以及报告来自各种生物体的蛋白质群落成员。它们的验证也很重要，例如通过交联质谱法或细胞生物学方法。此外，细胞提取物适用于冷冻电子显微镜（cryo-EM）结构分析，但由于其固有的复杂性，对cryo-EM衍生的蛋白质群落结构特征进行分类是一项艰巨的任务。受机器学习技术启发的图像处理工作流程的应用将在区分结构特征、将蛋白质组学和网络数据与结构特征以及随后重建的cryo-EM图谱相关联，以及最终在高分辨率下表征未鉴定的蛋白质群落方面提供改进。在这篇综述文章中，我们总结了从天然细胞提取物中检测蛋白质群落的最新文献，并确定了剩余的挑战和机遇。我们认为，机器学习、cryo-EM和互补结构蛋白质组学方法的进展及其整合将为天然细胞提取物中蛋白质群落的多尺度分子描述提供基础。
