Integrative structure of a 10-megadalton eukaryotic pyruvate dehydrogenase complex from native cell extracts

Kyrilis, Fotis L.; Semchonok, Dmitry A.; Skalidis, Ioannis; Tüting, Christian; Hamdi, Farzad; O’Reilly, Francis J.; Rappsilber, Juri; Kastritis, Panagiotis L.

doi:10.1016/j.celrep.2021.108727

Cited by 41 publications

(99 citation statements)

References 74 publications

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“…This multi-scale, integrative characterization of protein communities can only be performed in native cell extracts and was previously discussed (Kyrilis et al, 2019). This integrative, systematic analysis was performed for eukaryotic communities involved in the synthesis of fatty acids (Kastritis et al, 2017) and in the metabolism of oxoacids (Kyrilis et al, 2021).…”

Section: Higher-order Complexity Of Protein Communities: An Ideal Test Bed For Machine Learningmentioning

confidence: 99%

“…Even if high-density cross-linking retrieves interacting proteins and their relative interacting distances, the community structure is unknown, including stoichiometry. It is noted that deriving stoichiometry for protein communities is not trivial, and a combination of cryo-EM, immunoblotting data, MS, and crosslinking MS in fractionated extracts was recently performed to derive approximate stoichiometry for the higher-order structure of the endogenous pyruvate dehydrogenase complex (Kyrilis et al, 2021). Direct methods, such as electron microscopy, can, therefore, be applied to observe cell extracts and were previously used in combination with MS at low resolution to visualize protein complexes (Han et al, 2009).…”

Section: Processing (Cryo-)em Images From Native Extracts With a Focus On Machine Learningmentioning

confidence: 99%

“…Recent results in the field also showed that abundant complexes can be reconstructed de novo (Ho et al, 2020), but not as members of protein communities. We also recently communicated the structural and functional characterization of communities involved in oxo acid metabolism by integrative methods (Kyrilis et al, 2021). Despite these advances, the high complexity of the imaged cell extract hinders proper quantification and 3D reconstruction of the interacting molecules within the extracts, and this is because of multiple issues regarding the specimen complexity.…”

Section: Processing (Cryo-)em Images From Native Extracts With a Focus On Machine Learningmentioning

confidence: 99%

“…Recent studies not only increased the achievable resolution (Arimura et al, 2020;Ho et al, 2020;Su et al, 2021), particularly in the membrane (Su et al, 2021) or nuclear extracts (Arimura et al, 2020) but also determined the snapshots of higher-order organization of in-extract flexible, functional metabolons (Kyrilis et al, 2021). The importance and challenges of integrative structural studies of native extracts and the correlation between structural disorder and function for inextract metabolons were recently reviewed (Kyrilis et al, 2019;McCafferty et al, 2020;Skalidis et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…For example, they may efficiently transfer substrates along with enzymatic pathways [dubbed metabolons, reviewed in (Kastritis and Gavin, 2018)], effectively transduce signals, and regulate protein synthesis on local cellular demand. However, their inherent complexity limits probing their intrinsic structure to a few abundant biomolecular complexes, e.g., functional pyruvate dehydrogenase higher-order architecture (Kyrilis et al, 2021). The review of machine-learning approaches that are already applied in various intermediate analysis steps demonstrates an optimistic perspective in addressing this issue, and thus allowing a deeper understanding of protein communities in the future.…”

Section: Introductionmentioning

confidence: 99%

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

Kyrilis

Belapure

Kastritis

2021

Front. Mol. Biosci.

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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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Section: Higher-order Complexity Of Protein Communities: An Ideal Test Bed For Machine Learningmentioning

confidence: 99%

Section: Processing (Cryo-)em Images From Native Extracts With a Focus On Machine Learningmentioning

confidence: 99%

Section: Processing (Cryo-)em Images From Native Extracts With a Focus On Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Kyrilis

Belapure

Kastritis

2021

Front. Mol. Biosci.

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A hub for regulation of mitochondrial metabolism: Fatty acid and lipoic acid biosynthesis

Dieckmann

2023

IUBMB Life

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Having evolved from a prokaryotic origin, mitochondria retain pathways required for the catabolism of energy‐rich molecules and for the biosynthesis of molecules that aid catabolism and/or participate in other cellular processes essential for life of the cell. Reviewed here are details of the mitochondrial fatty acid biosynthetic pathway (FAS II) and its role in building both the octanoic acid precursor for lipoic acid biosynthesis (LAS) and longer‐chain fatty acids functioning in chaperoning the assembly of mitochondrial multisubunit complexes. Also covered are the details of mitochondrial lipoic acid biosynthesis, which is distinct from that of prokaryotes, and the attachment of lipoic acid to subunits of pyruvate dehydrogenase, α‐ketoglutarate dehydrogenase, and glycine cleavage system complexes. Special emphasis has been placed on presenting what is currently known about the interconnected paths and loops linking the FAS II–LAS pathway and two other mitochondrial realms, the organellar translation machinery and Fe‐S cluster biosynthesis and function.

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Enabling cryo‐EM density interpretation from yeast native cell extracts by proteomics data and AlphaFold structures

et al. 2023

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In the cellular context, proteins participate in communities to perform their function. The detection and identification of these communities as well as in‐community interactions has long been the subject of investigation, mainly through proteomics analysis with mass spectrometry. With the advent of cryogenic electron microscopy and the “resolution revolution,” their visualization has recently been made possible, even in complex, native samples. The advances in both fields have resulted in the generation of large amounts of data, whose analysis requires advanced computation, often employing machine learning approaches to reach the desired outcome. In this work, we first performed a robust proteomics analysis of mass spectrometry (MS) data derived from a yeast native cell extract and used this information to identify protein communities and inter‐protein interactions. Cryo‐EM analysis of the cell extract provided a reconstruction of a biomolecule at medium resolution (∼8 Å (FSC = 0.143)). Utilizing MS‐derived proteomics data and systematic fitting of AlphaFold‐predicted atomic models, this density was assigned to the 2.6 MDa complex of yeast fatty acid synthase. Our proposed workflow identifies protein complexes in native cell extracts from Saccharomyces cerevisiae by combining proteomics, cryo‐EM, and AI‐guided protein structure prediction.

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Integrative structure of a 10-megadalton eukaryotic pyruvate dehydrogenase complex from native cell extracts

Cited by 41 publications

References 74 publications

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

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

A hub for regulation of mitochondrial metabolism: Fatty acid and lipoic acid biosynthesis

Enabling cryo‐EM density interpretation from yeast native cell extracts by proteomics data and AlphaFold structures

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