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Modeling protein assemblies in the proteome.

ABSTRACT: Most (if not all) proteins function when associated in multimolecular assemblies. Attaining the structures of protein assemblies at the atomic scale is an important aim of structural biology. Experimentally, structures are increasingly available, and computations can help bridge the resolution gap between high- and low-resolution scales. Existing computational methods have made substantial progress toward this aim; however, current approaches are still limited. Some involve manual adjustment of experimental data; some are automated docking methods, which are computationally expensive and not applicable to large-scale proteome studies; and still others exploit the symmetry of the complexes and thus are not applicable to nonsymmetrical complexes. Our study aims to take steps toward overcoming these limitations. We have developed a strategy for the construction of protein assemblies computationally based on binary interactions predicted by a motif-based protein interaction prediction tool, PRISM (Protein Interactions by Structural Matching). Previously, we have shown its power in predicting pairwise interactions. Here we take a step toward multimolecular assemblies, reflecting the more prevalent cellular scenarios. With this method we are able to construct homo-/hetero-complexes and symmetric/asymmetric complexes without a limitation on the number of components. The method considers conformational changes and is applicable to large-scale studies. We also exploit electron microscopy density maps to select a solution from among the predictions. Here we present the method, illustrate its results, and highlight its current limitations.

SUBMITTER: Kuzu G

PROVIDER: S-EPMC3945916 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Modeling protein assemblies in the proteome.

Kuzu Guray G Keskin Ozlem O Nussinov Ruth R Gursoy Attila A

Molecular & cellular proteomics : MCP 20140120 3

Most (if not all) proteins function when associated in multimolecular assemblies. Attaining the structures of protein assemblies at the atomic scale is an important aim of structural biology. Experimentally, structures are increasingly available, and computations can help bridge the resolution gap between high- and low-resolution scales. Existing computational methods have made substantial progress toward this aim; however, current approaches are still limited. Some involve manual adjustment of ...[more]

PMID: 24445405

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Project description:BackgroundThe recent outbreak of severe infections with Shiga toxin (Stx) producing Escherichia coli (STEC) serotype O104:H4 highlights the need to understand horizontal gene transfer among E. coli strains, identify novel virulence factors and elucidate their pathogenesis. Quantitative shotgun proteomics can contribute to such objectives, allowing insights into the part of the genome translated into proteins and the connectivity of biochemical pathways and higher order assemblies of proteins at the subcellular level.Methodology/principal findingsWe examined protein profiles in cell lysate fractions of STEC strain 86-24 (serotype O157:H7), following growth in cell culture or bacterial isolation from intestines of infected piglets, in the context of functionally and structurally characterized biochemical pathways of E. coli. Protein solubilization in the presence of Triton X-100, EDTA and high salt was followed by size exclusion chromatography into the approximate M(r) ranges greater than 280 kDa, 280-80 kDa and 80-10 kDa. Peptide mixtures resulting from these and the insoluble fraction were analyzed by quantitative 2D-LC-nESI-MS/MS. Of the 2521 proteins identified at a 1% false discovery rate, representing 47% of all predicted E. coli O157:H7 gene products, the majority of integral membrane proteins were enriched in the high M(r) fraction. Hundreds of proteins were enriched in a M(r) range higher than that predicted for a monomer supporting their participation in protein complexes. The insoluble STEC fraction revealed enrichment of aggregation-prone proteins, including many that are part of large structure/function entities such as the ribosome, cytoskeleton and O-antigen biosynthesis cluster.SignificanceNearly all E. coli O157:H7 proteins encoded by prophage regions were expressed at low abundance levels or not detected. Comparative quantitative analyses of proteins from distinct cell lysate fractions allowed us to associate uncharacterized proteins with membrane attachment, potential participation in stable protein complexes, and susceptibility to aggregation as part of larger structural assemblies.

Project description:Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved.In this study, we developed a new method to discover and model protein interactions employing an exhaustive all-to-all docking strategy. This approach integrates molecular modeling, structural bioinformatics, machine learning, and functional annotation filters in order to provide interaction data for the bottom-up assembly of protein interaction networks. Encouragingly, the success rates for dimer modeling is 57.5 and 48.7% when experimental and computer-generated monomer structures are employed, respectively. Further, our protocol correctly identifies 81% of protein-protein interactions at the expense of only 19% false positive rate. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Finally, we validated our method against the human immune disease pathway.Protein docking supported by evolutionary restraints and machine learning can be used to reliably identify and model biologically relevant protein assemblies at the proteome scale. Moreover, the accuracy of the identification of protein-protein interactions is improved by considering only those protein pairs co-localized in the same cellular compartment and involved in the same biological process. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms and pathways as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques.

Dataset Information

Modeling protein assemblies in the proteome.

Publications

Modeling protein assemblies in the proteome.

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