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Onto Grid Purification and 3D Reconstruction of Protein Complexes Using Matrix-Landing Native Mass Spectrometry.

ABSTRACT: Addressing mixtures and heterogeneity in structural biology requires approaches that can differentiate and separate structures based on mass and conformation. Mass spectrometry (MS) provides tools for measuring and isolating gas-phase ions. The development of native MS including electrospray ionization allowed for manipulation and analysis of intact noncovalent biomolecules as ions in the gas phase, leading to detailed measurements of structural heterogeneity. Conversely, transmission electron microscopy (TEM) generates detailed images of biomolecular complexes that show an overall structure. Our matrix-landing approach uses native MS to probe and select biomolecular ions of interest for subsequent TEM imaging, thus unifying information on mass, stoichiometry, heterogeneity, etc., available via native MS with TEM images. Here, we prepare TEM grids of protein complexes purified via quadrupolar isolation and matrix-landing and generate 3D reconstructions of the isolated complexes. Our results show that these complexes maintain their structure through gas-phase isolation.

SUBMITTER: Lee KW

PROVIDER: S-EPMC10002473 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Onto Grid Purification and 3D Reconstruction of Protein Complexes Using Matrix-Landing Native Mass Spectrometry.

Lee Kenneth W KW Salome Austin Z AZ Westphall Michael S MS Grant Timothy T Coon Joshua J JJ

Journal of proteome research 20230106 3

Addressing mixtures and heterogeneity in structural biology requires approaches that can differentiate and separate structures based on mass and conformation. Mass spectrometry (MS) provides tools for measuring and isolating gas-phase ions. The development of native MS including electrospray ionization allowed for manipulation and analysis of intact noncovalent biomolecules as ions in the gas phase, leading to detailed measurements of structural heterogeneity. Conversely, transmission electron m ...[more]

PMID: 36608276

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Project description:BackgroundRecent advances in molecular biology have led to the accumulation of large amounts of data on protein-protein interaction networks in different species. An important challenge for the analysis of these data is to extract functional modules such as protein complexes and biological processes from networks which are characterised by the present of a significant number of false positives. Various computational techniques have been applied in recent years. However, most of them treat protein interaction as binary. Co-complex relations derived from affinity purification/mass spectrometry (AP-MS) experiments have been largely ignored.MethodsThis paper presents a new algorithm for detecting protein complexes from AP-MS data. The algorithm intends to detect groups of prey proteins that are significantly co-associated with the same set of bait proteins. We first construct AP-MS data as a bipartite network, where one set of nodes consists of bait proteins and the other set is composed of prey proteins. We then calculate pair-wise similarities of bait proteins based on the number of their commonly shared neighbours. A hierarchical clustering algorithm is employed to cluster bait proteins based on the similarities and thus a set of 'seed' clusters is obtained. Starting from these 'seed' clusters, an expansion process is developed to identify prey proteins which are significantly associated with the same set of bait proteins. Then, a set of complete protein complexes is derived. In application to two real AP-MS datasets, we validate biological significance of predicted protein complexes by using curated protein complexes and well-characterized cellular component annotation from Gene Ontology (GO). Several statistical metrics have been applied for evaluation.ResultsExperimental results show that, the proposed algorithm achieves significant improvement in detecting protein complexes from AP-MS data. In comparison to the well-known MCL algorithm, our algorithm improves the accuracy rate by about 20% in detecting protein complexes in both networks and increases the F-Measure value by about 50% in Krogan_2006 network. Greater precision and better accuracy have been achieved and the identified complexes are demonstrated to match well with existing curated protein complexes.ConclusionsOur study highlights the significance of taking co-complex relations into account when extracting protein complexes from AP-MS data. The algorithm proposed in this paper can be easily extended to the analysis of other biological networks which can be conveniently represented by bipartite graphs such as drug-target networks.

Dataset Information

Onto Grid Purification and 3D Reconstruction of Protein Complexes Using Matrix-Landing Native Mass Spectrometry.

Publications

Onto Grid Purification and 3D Reconstruction of Protein Complexes Using Matrix-Landing Native Mass Spectrometry.

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