Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

Tan, Dan; Li, Qiang; Zhang, Meijun; Liu, Chao; Ma, Chunyang; Zhang, Pan; Ding, Yue-He; Fan, Shidong; Тао, Ли; Yang, Bing; Li, Xiangke; ShouCai, Ma; Liu, Junjie; Feng, Boya; Liu, Xiaohui; Wang, Hongwei; He, Shan; Gao, Ning; Ye, Keqiong; Dong, Meng‐Qiu; Lei, Xiaoguang

doi:10.7554/elife.12509

Cited by 108 publications

(124 citation statements)

References 86 publications

(138 reference statements)

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“…This allowed for the identification of sub-complexes, which were accurate when compared to known structural models and when compared to known functions. However, correlation did not consistently reveal the directly bound protein pairs that other experiments such as yeast two-hybrid [8, 9] and chemical crosslinking [10–14] can reveal across large portions of the proteome. Other computational approaches have been proposed to identify direct contacts by analyzing co-occurrence of proteins in mass spectrometry experiments but they have only been applied to AP-MS datasets [15].…”

Section: Introductionmentioning

confidence: 68%

Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets

et al. 2017

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Determining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes. There are several methods for determining co-complex interactions between proteins, among them co-fractionation / mass spectrometry (CF-MS), but it remains difficult to identify directly contacting subunits within a multi-protein complex. Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes. To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on human cell lines. We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures. We demonstrate the method’s value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure (the exocyst and tRNA multi-synthetase complex) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex, GON7/C14ORF142, providing a more complete 3D model of the complex. Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available.

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Section: Introductionmentioning

confidence: 68%

Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets

et al. 2017

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“…To date, there are no publications describing the use of Skyline for analysis of cross-linked peptides. Recent advances in PIR and other cross-linking experiments have resulted in the reliable identification of hundreds to thousands of cross-linked peptide pairs, each providing information on the conformation of protein and protein complex structures [3, 7, 8, 13, 15–18]. Furthermore, quantitative principles from traditional proteomics experiments have been extended to cross-linking experiments allowing for insight into the dynamic changes of protein conformations and interactions [7].…”

Section: Resultsmentioning

confidence: 99%

“…Importantly qXL-MS is able to provide insight on the dynamics of protein conformations, interactions and the composition of protein complexes. To date most qXL-MS studies have relied on light and heavy isotopically labeled cross-linker molecules [1, 3–5]. Recent applications of qXL-MS utilizing deuterated cross-linkers include the comparison of unphosphorylated and phosphorylated states of a spinach chloroplast ATPase [4], distinguishing conformational states of the chaperonin TRiC/CCT complex, and characterizing structural differences between the complement components C3 and C3(H2O) [5].…”

Section: Introductionmentioning

confidence: 99%

A General Method for Targeted Quantitative Cross-Linking Mass Spectrometry

et al. 2016

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Chemical cross-linking mass spectrometry (XL-MS) provides protein structural information by identifying covalently linked proximal amino acid residues on protein surfaces. The information gained by this technique is complementary to other structural biology methods such as x-ray crystallography, NMR and cryo-electron microscopy[1]. The extension of traditional quantitative proteomics methods with chemical cross-linking can provide information on the structural dynamics of protein structures and protein complexes. The identification and quantitation of cross-linked peptides remains challenging for the general community, requiring specialized expertise ultimately limiting more widespread adoption of the technique. We describe a general method for targeted quantitative mass spectrometric analysis of cross-linked peptide pairs. We report the adaptation of the widely used, open source software package Skyline, for the analysis of quantitative XL-MS data as a means for data analysis and sharing of methods. We demonstrate the utility and robustness of the method with a cross-laboratory study and present data that is supported by and validates previously published data on quantified cross-linked peptide pairs. This advance provides an easy to use resource so that any lab with access to a LC-MS system capable of performing targeted quantitative analysis can quickly and accurately measure dynamic changes in protein structure and protein interactions.

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“…The use of complementary XL reagents alleviates these limitations to some extent8,40. With the development of novel cross-linking technologies and detection capabilities29,45,46, such limitations are continuing to be addressed, which will increase the applicability of our modeling method.…”

Section: Introductionmentioning

confidence: 99%

Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data

et al. 2018

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This protocol describes a workflow for creating structural models of proteins or protein complexes using distance restraints derived from cross-linking mass spectrometry experiments. The distance restraints are used (i) to adjust preliminary models that are calculated based on a homologous template and primary sequence and (ii) to select the model that is in best agreement with the experimental data. In the case of protein complexes, the cross-linking data is further used to dock the subunits to one another to generate models of the interacting proteins. Predicting models in such a manner has the potential to indicate multiple conformations and dynamic changes that occur in solution. This modeling protocol is compatible with many cross-linking workflows and uses open-source programs or programs that are free for academic users and do not require expertise in computational modeling. This protocol is an excellent additional application with which to use cross-linking results for building structural models of proteins. The established protocol is expected to take 6-12 days to complete, depending on the size of the proteins and the complexity of the cross-linking data.

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Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

Cited by 108 publications

References 86 publications

Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets

Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets

A General Method for Targeted Quantitative Cross-Linking Mass Spectrometry

Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data

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