谷歌浏览器插件
订阅小程序
在清言上使用

Differentiating Two Adsorption Modes of Membrane-Bound Antimicrobial Peptides Via Sum Frequency Generation.

Chu Wang, Chenxi Hou, Jiayou Pu, Siyu Li, Shiao Luo,Xiaofeng Han,Xiaolin Lu

Langmuir(2023)

引用 0|浏览4
暂无评分
摘要
Multidrug-resistant (MDR) pathogens have been a growing threat to human health over the years. Antimicrobial peptides (AMPs) with broad-spectrum antibiotic activity, as a promising therapeutic candidate, have shown tremendous capability against MDR pathogens. To acquire novel AMPs with better efficacy, we should dig into the antimicrobial mechanism by which AMPs perform their functions. In this study, the interaction processes between three representative AMPs (maculatin 1.1-G15, cupiennin 1a, and aurein 1.2) and the model membrane dDPPG/DPPG bilayer were investigated via sum frequency generation (SFG) vibrational spectroscopy. Two interaction modes for the membrane-bound AMPs were differentiated, i.e., the loosely adsorbed one and the tightly adsorbed one. In the loosely adsorbed mode, AMPs are bound to the bilayer mainly by the electrostatic attraction between the positively charged residues of AMPs and the negatively charged head groups of the lipids. After the charged AMPs and lipids were neutralized by the counter ions, the desorption of AMPs from the membrane lipids happened, as evidenced by the disappearance of the SFG signals from membrane-bound AMPs. While in the tightly adsorbed mode, besides the charged attraction, AMPs are additionally inserted into the membrane lipids via the hydrophobic interaction. Even when the electrostatic attraction was neutralized by the counter ions, the hydrophobic interaction still led to the firm adsorption of AMPs onto the already-neutralized bilayer lipids, as evidenced by the presence of clear SFG signals from membrane-bound AMPs. We thus established a feasible protocol to expand the application of SFG, namely classifying the adsorption modes of AMPs. Such knowledge will surely promote the development and application of AMPs with high efficacy.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要