Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist’s Perspective

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. 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 cross-linking mass spectrometry or cell biology methods. In addition, 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. 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. We argue that progress in, and 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.