Suppr超能文献

用于表征脂质结合蛋白内腔的计算机模拟策略

In Silico Strategies for Characterizing Inner Cavities of Lipid-Binding Proteins.

作者信息

Sacher Sukriti, Ray Arjun

机构信息

Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India.

出版信息

Methods Mol Biol. 2025;2888:305-320. doi: 10.1007/978-1-0716-4318-1_20.

Abstract

Cavities in proteins perform diverse functions such as substrate binding, enzyme catalysis, passage for transportation of small molecules, and protein oligomerization. Often, the physical properties of these cavities are closely linked to the protein function; such as the hydrophobic lipid-binding cavities in lipid-binding proteins (LBPs) that protect lipid substrates from the larger aqueous milieu. Therefore, the characterization of protein cavities can provide valuable insights into protein structure-function relationships, hinting toward their mechanism of action while aiding in the identification of ligand binding sites that are essential for drug discovery approaches. Several algorithms have historically been designed to identify and characterize the different types of cavities in protein structures. We summarize these algorithms and provide a step-by-step guide for locating and characterizing internal cavities in proteins using CICLOP by using ATP-binding cassette transporter A1 (ABCA1) as an example.

摘要

蛋白质中的腔具有多种功能,如底物结合、酶催化、小分子运输通道以及蛋白质寡聚化。通常,这些腔的物理性质与蛋白质功能密切相关;例如脂质结合蛋白(LBP)中的疏水脂质结合腔可保护脂质底物免受较大的水性环境影响。因此,对蛋白质腔的表征可为蛋白质结构 - 功能关系提供有价值的见解,提示其作用机制,同时有助于识别对于药物发现方法至关重要的配体结合位点。历史上已经设计了几种算法来识别和表征蛋白质结构中不同类型的腔。我们总结这些算法,并以ATP结合盒转运蛋白A1(ABCA1)为例,提供使用CICLOP定位和表征蛋白质内部腔的分步指南。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验