Magnifying the Structural Components of Biomembranes: A Prototype for the Study of the Self‐Assembly of Giant Lipids. (12th February 2020)

• Record Type: Journal Article
• Authors: Yan, Xiao‐Yun; Lin, Zhiwei; Zhang, Wei; Xu, Hui; Guo, Qing‐Yun; Liu, Yuchu; Luo, Jiancheng; Liu, Xian‐You; Zhang, Rongchun; Huang, Jiahao; Liu, Tong; Su, Zebin; Zhang, Ruimeng; Zhang, Shuailin; Liu, Tianbo; Cheng, Stephen Z. D.
• Is Part Of: Angewandte Chemie. Volume 132:Number 13(2020)
• Page Start: 5264
• Page End: 5272
• Publication Date: 2020-02-12
• Subjects: Amphiphile -- Biomimetische Synthese -- Membranen -- Oligofluoren -- Supramolekulare Chemie; Chemistry -- Periodicals; 540
• DOI: 10.1002/ange.201916149
• Languages: English
• ISSNs: 0044-8249

Abstract

Abstract: How biomembranes are self‐organized to perform their functions remains a pivotal issue in biological and chemical science. Understanding the self‐assembly principles of lipid‐like molecules hence becomes crucial. Herein, we report the mesostructural evolution of amphiphilic sphere‐rod conjugates (giant lipids), and study the roles of geometric parameters (head–tail ratio and cross‐sectional area) during this course. As a prototype system, giant lipids resemble natural lipidic molecules by capturing their essential features. The self‐assembly behavior of two categories of giant lipids (I‐shape and T‐shape, a total of 8 molecules) is demonstrated. A rich variety of mesostructures is constructed in solution state and their molecular packing models are rationally understood. Giant lipids recast the phase behavior of natural lipids to a certain degree and the abundant self‐assembled morphologies reveal distinct physiochemical behaviors when geometric parameters deviate from natural analogues. Abstract : Je größer, umso besser : Zwei Klassen riesiger, lipidähnlicher amphiphiler Moleküle organisieren sich zu unterschiedlichen Mesostrukturen und bieten damit Einblick in die Prinzipien der Lipid‐Selbstorganisation. Die geometrischen Parameter der Riesenmoleküle wurden auf molekularer Ebene moduliert, um die physikochemischen Antriebskräfte hinter ihrer Selbstorganisation zu verstehen.