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A Comparison of Methods To Enhance Protein Detection of Lipoproteins by Mass Spectrometry.

ABSTRACT: We sought to develop a new method to more efficiently analyze lipid-bound proteins by mass spectrometry using a combination of a lipid removal agent (LRA) that selectively targets lipid-bound proteins and a mass spectrometry compatible detergent, anionic acid labile surfactant (AALS), that is capable of eluting proteins off the LRA. This method was compared to established methods that use the lipid removal agent alone and straight proteomic analysis of human plasma after organic solvent delipidation (OSD). Plasma from healthy individuals was separated by gel filtration chromatography and prepared for mass spectrometry analysis by each of the described methods. The addition of AALS to LRA increased the overall number of proteins detected in both the high and low density lipoprotein size range, the number of peptide counts for each protein, and the overall sequence coverage. Organic solvent delipidation detected the most proteins, though with some decrease in overall protein detection and sequence coverage due to the presence of nonlipid-bound proteins. The use of LRA allows for selection and analysis of lipid-bound proteins. The addition of a mass spectrometry compatible detergent improved detection of lipid-bound proteins from human plasma using LRA.

SUBMITTER: Heink A

PROVIDER: S-EPMC4714602 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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A Comparison of Methods To Enhance Protein Detection of Lipoproteins by Mass Spectrometry.

Heink Anna A Davidson W Sean WS Swertfeger Debi K DK Lu L Jason LJ Shah Amy S AS

Journal of proteome research 20150612 7

We sought to develop a new method to more efficiently analyze lipid-bound proteins by mass spectrometry using a combination of a lipid removal agent (LRA) that selectively targets lipid-bound proteins and a mass spectrometry compatible detergent, anionic acid labile surfactant (AALS), that is capable of eluting proteins off the LRA. This method was compared to established methods that use the lipid removal agent alone and straight proteomic analysis of human plasma after organic solvent delipida ...[more]

PMID: 26039899

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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.

Dataset Information

A Comparison of Methods To Enhance Protein Detection of Lipoproteins by Mass Spectrometry.

Publications

A Comparison of Methods To Enhance Protein Detection of Lipoproteins by Mass Spectrometry.

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