Literature DB >> 34996283

Design of Lipid-Protein Conjugates Using Amphiphilic Peptide Substrates of Microbial Transglutaminase.

Mari Takahara1, Rie Wakabayashi, Kosuke Minamihata, Masahiro Goto, Noriho Kamiya.   

Abstract

Lipid modification of proteins plays a significant role in regulating the cellular environment. Mimicking natural lipidated proteins is a key technique for assessing the function of proteins modified with lipids and also to render self-assembly of lipids to a target protein. Herein, we report a facile method of conjugating proteins with lipid-fused peptides under homogeneous physiological conditions by using the microbial transglutaminase (MTG) reaction. MTG catalyzes the cross-linking reaction between a specific glutamine (Q) in a protein and a lysine (K) in newly designed lipid-fused peptides. The water-soluble peptide substrates for lipid modification, C14-X-MRHKGS, were newly synthesized, where C14, X, and MRHKGS represent myristic acid, linker peptides composed of G, P, or S, and MTG-reactive K surrounded with basic amino acids, respectively. The MTG-mediated cross-linking reaction between a protein fused with LLQG at the C-terminus and C14-X-MRHKGS (5 molar eq) dissolved in a phosphate saline solution resulted in lipid-protein conjugates with yields of 70 to 100%. The anchoring ability of the obtained lipid-protein conjugates to cell membranes was dependent on the number of G residues in the GnS linker, suggesting that self-assembly and hydrophobicity of the GnS motif serves to enhance membrane anchoring of lipid-protein conjugates.

Entities:  

Keywords:  lipid; lipid−protein conjugate; microbial transglutaminase; peptide; self-assembly

Year:  2018        PMID: 34996283     DOI: 10.1021/acsabm.8b00271

Source DB:  PubMed          Journal:  ACS Appl Bio Mater        ISSN: 2576-6422


  1 in total

1.  Comprehensive analysis of transglutaminase substrate preference by cDNA display coupled with next-generation sequencing and bioinformatics.

Authors:  Jasmina Damnjanović; Nana Odake; Jicheng Fan; Maurizio Camagna; Beixi Jia; Takaaki Kojima; Naoto Nemoto; Kiyotaka Hitomi; Hideo Nakano
Journal:  Sci Rep       Date:  2022-08-09       Impact factor: 4.996

  1 in total

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