Dataset Information

Chemical Acetylation of Ligands and Two-Step Digestion Protocol for Reducing Codigestion in Affinity Purification-Mass Spectrometry.

ABSTRACT: We present an effective, fast, and user-friendly method to reduce codigestion of bead-bound ligands, such as antibodies or streptavidin, in affinity purification-mass spectrometry experiments. A short preincubation of beads with Sulfo-NHS-Acetate leads to chemical acetylation of lysine residues, making ligands insusceptible to Lys-C-mediated proteolysis. In contrast to similar approaches, our procedure offers the advantage of exclusively using nontoxic chemicals and employing mild chemical reaction conditions. After binding of bait proteins to Sulfo-NHS-Acetate treated beads, we employ a two-step digestion protocol with the sequential use of Lys-C protease for on-bead digestion followed by in-solution digestion of the released proteins with trypsin. The implementation of this protocol results in a strong reduction of contaminating ligand peptides, which allows significantly higher amounts of sample to be subjected to LC-MS analysis, improving sensitivity and quantitative accuracy.

SUBMITTER: Hollenstein DM

PROVIDER: S-EPMC10563155 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Chemical Acetylation of Ligands and Two-Step Digestion Protocol for Reducing Codigestion in Affinity Purification-Mass Spectrometry.

Hollenstein David M DM Maurer-Granofszky Margarita M Reiter Wolfgang W Anrather Dorothea D Gossenreiter Thomas T Babic Riccardo R Hartl Natascha N Kraft Claudine C Hartl Markus M

Journal of proteome research 20230915 10

We present an effective, fast, and user-friendly method to reduce codigestion of bead-bound ligands, such as antibodies or streptavidin, in affinity purification-mass spectrometry experiments. A short preincubation of beads with Sulfo-NHS-Acetate leads to chemical acetylation of lysine residues, making ligands insusceptible to Lys-C-mediated proteolysis. In contrast to similar approaches, our procedure offers the advantage of exclusively using nontoxic chemicals and employing mild chemical react ...[more]

PMID: 37712406

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Project description:BackgroundRecent 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.MethodsThis 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.ResultsExperimental 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.ConclusionsOur 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

Chemical Acetylation of Ligands and Two-Step Digestion Protocol for Reducing Codigestion in Affinity Purification-Mass Spectrometry.

Publications

Chemical Acetylation of Ligands and Two-Step Digestion Protocol for Reducing Codigestion in Affinity Purification-Mass Spectrometry.

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