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Metal affinity capture tandem mass spectrometry for the selective detection of phosphopeptides.

ABSTRACT: We report a new method called metal affinity capture that when coupled with tandem mass spectrometry (MAC-MSMS) allows for the selective detection and identification of phosphopeptides in complex mixtures. Phosphopeptides are captured as ternary complexes with Ga(III) or Fe(III) and N(alpha),N(alpha)-bis(carboxymethyl)lysine (LysNTA) in solution and electrosprayed as doubly or triply charged positive ions. The gas-phase complexes uniformly dissociate to produce a common (LysNTA + H)+ ion that is used as a specific marker in precursor ion scans. The advantages of MAC-MSMS over the current methods of phosphopeptide detection are as follows. (1) MAC-MSMS uses metal complexes that self-assemble in solution at pH <5, which is favorable for the production of positive ions by electrospray. (2) Phosphorylation at tyrosine, serine, and threonine is detected by MAC-MSMS. (3) The phosphopeptide peaks in the mass spectra are encoded with the 69Ga-71Ga isotope pattern for selective recognition in mixtures. Detection by MAC-MSMS of singly and multiply phosphorylated peptides in tryptic digests is demonstrated at low-nanomolar protein concentrations.

SUBMITTER: Blacken GR

PROVIDER: S-EPMC2519875 | biostudies-literature | 2006 Sep

REPOSITORIES: biostudies-literature

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Metal affinity capture tandem mass spectrometry for the selective detection of phosphopeptides.

Blacken Grady R GR Gelb Michael H MH Turecek Frantisek F

Analytical chemistry 20060901 17

We report a new method called metal affinity capture that when coupled with tandem mass spectrometry (MAC-MSMS) allows for the selective detection and identification of phosphopeptides in complex mixtures. Phosphopeptides are captured as ternary complexes with Ga(III) or Fe(III) and N(alpha),N(alpha)-bis(carboxymethyl)lysine (LysNTA) in solution and electrosprayed as doubly or triply charged positive ions. The gas-phase complexes uniformly dissociate to produce a common (LysNTA + H)+ ion that is ...[more]

PMID: 16944885

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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.

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Metal affinity capture tandem mass spectrometry for the selective detection of phosphopeptides.

Publications

Metal affinity capture tandem mass spectrometry for the selective detection of phosphopeptides.

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