Journal
FRONTIERS IN MOLECULAR BIOSCIENCES
Volume 8, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fmolb.2021.660542
Keywords
cellular homogenates; random forest; convolutional neural network; cryo-EM; mass spectrometry; structural biology; protein– protein interactions; metabolons
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Funding
- Federal Ministry for Education and Research (BMBF, ZIK program) [03Z22HN23, 03COV04]
- European Regional Development Funds for Saxony-Anhalt [EFRE: ZS/2016/04/78115]
- Deutsche Forschungsgemeinschaft (DFG) [391498659, RTG 2467]
- Martin-Luther University of Halle-Wittenberg
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Native cell extracts offer insight into the molecular structure of ordered biological systems at high resolution. Machine learning approaches are used to discover protein-protein interactions, reconstruct biological networks, and characterize protein communities. Image processing workflows inspired by machine learning techniques help distinguish structural signatures and characterize unidentified protein communities within cell extracts.
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.
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