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Probing N-glycoprotein microheterogeneity by lectin affinity purification-mass spectrometry analysis.

ABSTRACT: Lectins are carbohydrate binding proteins that recognize specific epitopes present on target glycoproteins. Changes in lectin-reactive carbohydrate repertoires are related to many biological signaling pathways and recognized as hallmarks of several pathological processes. Consequently, lectins are valuable probes, commonly used for examining glycoprotein structural and functional microheterogeneity. However, the molecular interactions between a given lectin and its preferred glycoproteoforms are largely unknown due to the inherent complexity and limitations of methods used to investigate intact glycoproteins. Here, we apply a lectin-affinity purification procedure coupled with native mass spectrometry to characterize lectin-reactive glycoproteoforms at the intact protein level. We investigate the interactions between the highly fucosylated and highly branched glycoproteoforms of haptoglobin and ?1-acid glycoprotein using two different lectins Aleuria aurantia lectin (AAL) and Phaseolus vulgaris leucoagglutinin (PHA-L), respectively. Firstly we show a co-occurrence of fucosylation and N-glycan branching on haptoglobin, particularly among highly fucosylated glycoproteoforms. Secondly, we analyze the global heterogeneity of highly branched glycoproteoforms of haptoglobin and ?1-acid glycoprotein and reveal that while multi-fucosylation attenuates the lectin PHA-L binding to haptoglobin, it has no impact on AGP. Taken together, our lectin affinity purification native MS approach elucidates lectin specificities between intact glycoproteins, not achievable by other methods. Moreover, since aberrant glycosylation of Hp and AGP are potential markers for many diseases, including pancreatic, hepatic and ovarian cancers, understanding their interactions with lectins will help the development of carbohydrate-centric monitoring methods to understand their pathophysiological implications.

SUBMITTER: Wu D

PROVIDER: S-EPMC6524569 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Probing <i>N</i>-glycoprotein microheterogeneity by lectin affinity purification-mass spectrometry analysis.

Wu Di D Li Jingwen J Struwe Weston B WB Robinson Carol V CV

Chemical science 20190416 19

Lectins are carbohydrate binding proteins that recognize specific epitopes present on target glycoproteins. Changes in lectin-reactive carbohydrate repertoires are related to many biological signaling pathways and recognized as hallmarks of several pathological processes. Consequently, lectins are valuable probes, commonly used for examining glycoprotein structural and functional microheterogeneity. However, the molecular interactions between a given lectin and its preferred glycoproteoforms are ...[more]

PMID: 31183067

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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

Probing N-glycoprotein microheterogeneity by lectin affinity purification-mass spectrometry analysis.

Publications

Probing <i>N</i>-glycoprotein microheterogeneity by lectin affinity purification-mass spectrometry analysis.

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