Automated descriptors for high-throughput screening of peptide self-assembly†

IF 3.1 3区 化学 Q2 Chemistry
Raj Kumar Rajaram Baskaran, Alexander van Teijlingen and Tell Tuttle
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引用次数: 0

Abstract

We present five automated descriptors: Aggregate Detection Index (ADI); Sheet Formation Index (SFI); Vesicle Formation Index (VFI); Tube Formation Index (TFI); and Fibre Formation Index (FFI), that have been designed for analysing peptide self-assembly in molecular dynamics simulations. These descriptors, implemented as Python modules, enhance analytical precision and enable the development of screening methods tailored to specific structural targets rather than general aggregation. Initially tested on the FF dipeptide, the descriptors were validated using a comprehensive dipeptide dataset. This approach facilitates the identification of promising self-assembling moieties with nanoscale properties directly linked to macroscale functions, such as hydrogel formation.

Abstract Image

用于高通量筛选肽自组装的自动描述符。
我们提出了五种自动描述符:聚合检测索引(ADI);纸张形成指数;囊泡形成指数;管形成指数;和纤维形成指数(FFI),设计用于分析分子动力学模拟中的肽自组装。这些描述符以Python模块的形式实现,提高了分析精度,并能够开发针对特定结构目标而不是一般聚合的筛选方法。最初在FF二肽上进行测试,描述符使用综合二肽数据集进行验证。这种方法有助于识别有希望的自组装部分,这些自组装部分具有纳米级性质,与宏观功能(如水凝胶形成)直接相关。
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来源期刊
Faraday Discussions
Faraday Discussions CHEMISTRY, PHYSICAL-
CiteScore
4.90
自引率
0.00%
发文量
259
审稿时长
2.8 months
期刊介绍: Discussion summary and research papers from discussion meetings that focus on rapidly developing areas of physical chemistry and its interfaces
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