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Modeling of protein binary complexes using structural mass spectrometry data.

ABSTRACT: In this article, we describe a general approach to modeling the structure of binary protein complexes using structural mass spectrometry data combined with molecular docking. In the first step, hydroxyl radical mediated oxidative protein footprinting is used to identify residues that experience conformational reorganization due to binding or participate in the binding interface. In the second step, a three-dimensional atomic structure of the complex is derived by computational modeling. Homology modeling approaches are used to define the structures of the individual proteins if footprinting detects significant conformational reorganization as a function of complex formation. A three-dimensional model of the complex is constructed from these binary partners using the ClusPro program, which is composed of docking, energy filtering, and clustering steps. Footprinting data are used to incorporate constraints-positive and/or negative-in the docking step and are also used to decide the type of energy filter-electrostatics or desolvation-in the successive energy-filtering step. By using this approach, we examine the structure of a number of binary complexes of monomeric actin and compare the results to crystallographic data. Based on docking alone, a number of competing models with widely varying structures are observed, one of which is likely to agree with crystallographic data. When the docking steps are guided by footprinting data, accurate models emerge as top scoring. We demonstrate this method with the actin/gelsolin segment-1 complex. We also provide a structural model for the actin/cofilin complex using this approach which does not have a crystal or NMR structure.

SUBMITTER: Kamal JK

PROVIDER: S-EPMC2144602 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

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Modeling of protein binary complexes using structural mass spectrometry data.

Kamal J K Amisha JK Chance Mark R MR

Protein science : a publication of the Protein Society 20071127 1

In this article, we describe a general approach to modeling the structure of binary protein complexes using structural mass spectrometry data combined with molecular docking. In the first step, hydroxyl radical mediated oxidative protein footprinting is used to identify residues that experience conformational reorganization due to binding or participate in the binding interface. In the second step, a three-dimensional atomic structure of the complex is derived by computational modeling. Homology ...[more]

PMID: 18042684

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Project description:BACKGROUND: Recent 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. METHODS: This 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. RESULTS: Experimental 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. CONCLUSIONS: Our 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.

Project description:Ortholog protein complexes are responsible for equivalent functions in different organisms. However, during evolution, each organism adapts to meet its physiological needs and the environmental challenges imposed by its niche. This selection pressure leads to structural diversity in protein complexes, which are often difficult to specify, especially in the absence of high-resolution structures. Here, we describe a multilevel experimental approach based on native mass spectrometry (MS) tools for elucidating the structural preservation and variations among highly related protein complexes. The 20S proteasome, an essential protein degradation machinery, served as our model system, wherein we examined five complexes isolated from different organisms. We show that throughout evolution, from the Thermoplasma acidophilum archaeal prokaryotic complex to the eukaryotic 20S proteasomes in yeast (Saccharomyces cerevisiae) and mammals (rat - Rattus norvegicus, rabbit - Oryctolagus cuniculus and human - HEK293 cells), the proteasome increased both in size and stability. Native MS structural signatures of the rat and rabbit 20S proteasomes, which heretofore lacked high-resolution, three-dimensional structures, highly resembled that of the human complex. Using cryoelectron microscopy single-particle analysis, we were able to obtain a high-resolution structure of the rat 20S proteasome, allowing us to validate the MS-based results. Our study also revealed that the yeast complex, and not those in mammals, was the largest in size and displayed the greatest degree of kinetic stability. Moreover, we also identified a new proteoform of the PSMA7 subunit that resides within the rat and rabbit complexes, which to our knowledge have not been previously described. Altogether, our strategy enables elucidation of the unique structural properties of protein complexes that are highly similar to one another, a framework that is valid not only to ortholog protein complexes, but also for other highly related protein assemblies.

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Modeling of protein binary complexes using structural mass spectrometry data.

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Modeling of protein binary complexes using structural mass spectrometry data.

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