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Medicinal Chemistry


ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

Research Article

The Inhibition of Polysialyltranseferase ST8SiaIV Through Heparin Binding to Polysialyltransferase Domain (PSTD)

Author(s): Li-Xin Peng, Xue-Hui Liu, Bo Lu, Si-Ming Liao, Feng Zhou, Ji-Min Huang, Dong Chen, Frederic A. Troy II, Guo-Ping Zhou* and Ri-Bo Huang*

Volume 15, Issue 5, 2019

Page: [486 - 495] Pages: 10

DOI: 10.2174/1573406415666181218101623

Price: $65


Background: The polysialic acid (polySia) is a unique carbohydrate polymer produced on the surface Of Neuronal Cell Adhesion Molecule (NCAM) in a number of cancer cells, and strongly correlates with the migration and invasion of tumor cells and with aggressive, metastatic disease and poor clinical prognosis in the clinic. Its synthesis is catalyzed by two polysialyltransferases (polySTs), ST8SiaIV (PST) and ST8SiaII (STX). Selective inhibition of polySTs, therefore, presents a therapeutic opportunity to inhibit tumor invasion and metastasis due to NCAM polysialylation. Heparin has been found to be effective in inhibiting the ST8Sia IV activity, but no clear molecular rationale. It has been found that polysialyltransferase domain (PSTD) in polyST plays a significant role in influencing polyST activity, and thus it is critical for NCAM polysialylation based on the previous studies.

Objective: To determine whether the three different types of heparin (unfractionated hepain (UFH), low molecular heparin (LMWH) and heparin tetrasaccharide (DP4)) is bound to the PSTD; and if so, what are the critical residues of the PSTD for these binding complexes?

Methods: Fluorescence quenching analysis, the Circular Dichroism (CD) spectroscopy, and NMR spectroscopy were used to determine and analyze interactions of PSTD-UFH, PSTD-LMWH, and PSTD-DP4.

Results: The fluorescence quenching analysis indicates that the PSTD-UFH binding is the strongest and the PSTD-DP4 binding is the weakest among these three types of the binding; the CD spectra showed that mainly the PSTD-heparin interactions caused a reduction in signal intensity but not marked decrease in α-helix content; the NMR data of the PSTD-DP4 and the PSTDLMWH interactions showed that the different types of heparin shared 12 common binding sites at N247, V251, R252, T253, S257, R265, Y267, W268, L269, V273, I275, and K276, which were mainly distributed in the long α-helix of the PSTD and the short 3-residue loop of the C-terminal PSTD. In addition, three residues K246, K250 and A254 were bound to the LMWH, but not to DP4. This suggests that the PSTD-LMWH binding is stronger than the PSTD-DP4 binding, and the LMWH is a more effective inhibitor than DP4.

Conclusion: The findings in the present study demonstrate that PSTD domain is a potential target of heparin and may provide new insights into the molecular rationale of heparin-inhibiting NCAM polysialylation.

Keywords: NCAM polysialylation, polysialyltransferase (polyST), ST8SiaIV, heparin, inhibitor, polysialyltransferase domain (PSTD), polybasic region (PBR), NMR, CD, and fluorescence spectra.

Graphical Abstract
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Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, Z.C.; Chou, K.C. iPTM-mLys: Identifying multiple lysine PTM sites and their different types. Bioinformatics, 2016, 32, 3116-3123.
Qiu, W.R.; Xiao, X.; Xu, Z.C.; Chou, K.C. iPhos-PseEn: Identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget, 2016, 7, 51270-51283.
Zhang, C.J.; Tang, H.; Li, W.C.; Lin, H.; Chen, W.; Chou, K.C. iOri-Human: Identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget, 2016, 7, 69783-69793.
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K.C. iRNA-AI: Identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget, 2017, 8, 4208-4217.
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mPlant: Predict subcellular localization of multi-location plant proteins via incorporating the optimal GO information into general PseAAC. Mol. Biosyst., 2017, 13, 1722-1727.
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene, 2017, 628, 315-321.
Cheng, X.; Zhao, S.G.; Lin, W.Z.; Xiao, X.; Chou, K.C. pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics, 2017, 33, 3524-3531.
Cheng, X.; Zhao, S.G.; Xiao, X.; Chou, K.C. iATC-mISF: A multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics, 2017, 33, 341-346.
Cheng, X.; Zhao, S.G.; Xiao, X.; Chou, K.C. iATC-mHyb: A hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals. Oncotarget, 2017, 8, 58494-58503.
Feng, P.; Ding, H.; Yang, H.; Chen, W.; Lin, H.; Chou, K.C. iRNA-PseColl: Identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol. Ther. Nucleic Acids, 2017, 7, 155-163.
Liu, B.; Yang, F.; Huang, D.S.; Chou, K.C. iPromoter-2L: A two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics, 2018, 34, 33-40.
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, Z.C.; Jia, J.H.; Chou, K.C. iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier. Genomics, 2018, 110, 239-246.
Su, Z.D.; Huang, Y.; Zhang, Z.Y.; Zhao, Y.W.; Wang, D.; Chen, W.; Chou, K.C.; Lin, H. iLoc-lncRNA: Predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics, 2018, 34(24), 4196-4204.
Xuao, X.; Cheng, X.; Chen, G.; Mao, Q.; Chou, K.C. pLoc_balmGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics,, 2018, pii: S0888-7543. (18) 30260-X.
Yang, H.; Qiu, W.R.; Liu, G.; Guo, F.B.; Chen, W.; Chou, K.C.; Lin, H. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int. J. Biol. Sci., 2018, 14, 883-891.
Chou, K.C. Impacts of bioinformatics to medicinal chemistry. Med. Chem., 2015, 11, 218-234.
Chou, K.C. An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr. Top. Med. Chem., 2017, 17, 2337-2358.

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