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用于发现超分子纳米材料的动态肽库。

Dynamic peptide libraries for the discovery of supramolecular nanomaterials.

机构信息

Advanced Science Research Center (ASRC), City University of New York, 85 St Nicholas Terrace, New York 10031, USA.

WestCHEM/Department of Pure &Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow G1 1XL, UK.

出版信息

Nat Nanotechnol. 2016 Nov;11(11):960-967. doi: 10.1038/nnano.2016.169. Epub 2016 Oct 3.

Abstract

Sequence-specific polymers, such as oligonucleotides and peptides, can be used as building blocks for functional supramolecular nanomaterials. The design and selection of suitable self-assembling sequences is, however, challenging because of the vast combinatorial space available. Here we report a methodology that allows the peptide sequence space to be searched for self-assembling structures. In this approach, unprotected homo- and heterodipeptides (including aromatic, aliphatic, polar and charged amino acids) are subjected to continuous enzymatic condensation, hydrolysis and sequence exchange to create a dynamic combinatorial peptide library. The free-energy change associated with the assembly process itself gives rise to selective amplification of self-assembling candidates. By changing the environmental conditions during the selection process, different sequences and consequent nanoscale morphologies are selected.

摘要

序列特异性聚合物,如寡核苷酸和多肽,可以作为功能性超分子纳米材料的构建块。然而,由于可用的组合空间非常大,因此设计和选择合适的自组装序列具有挑战性。在这里,我们报告了一种允许在肽序列空间中搜索自组装结构的方法。在这种方法中,未保护的同肽和杂肽(包括芳香族、脂肪族、极性和带电氨基酸)经受连续的酶缩合、水解和序列交换,以创建动态组合肽文库。与组装过程本身相关的自由能变化导致自组装候选物的选择性扩增。通过在选择过程中改变环境条件,可以选择不同的序列和相应的纳米级形态。

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