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TWN-RENCOD:一种用于蛋白质结合位点比较的新方法。

TWN-RENCOD: A novel method for protein binding site comparison.

作者信息

Choi Kwang-Eun, Balupuri Anand, Kang Nam Sook

机构信息

Graduate School of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, South Korea.

出版信息

Comput Struct Biotechnol J. 2022 Dec 19;21:425-431. doi: 10.1016/j.csbj.2022.12.014. eCollection 2023.

Abstract

Several diverse proteins possess similar binding sites. Protein binding site comparison provides valuable insights for the drug discovery and development. Binding site similarities are useful in understanding polypharmacology, identifying potential off-targets and repurposing of known drugs. Many binding site analysis and comparison methods are available today, however, these methods may not be adequate to explain variation in the activity of a drug or a small molecule against a number of similar proteins. Water molecules surrounding the protein surface contribute to structure and function of proteins. Water molecules form diverse types of hydrogen-bonded cyclic water-ring networks known as topological water networks (TWNs). Analysis of TWNs in binding site of proteins may improve understanding of the characteristics of binding sites. We propose TWN-based residue encoding (TWN-RENCOD), a novel binding site comparison method which compares the aqueous environment in binding sites of similar proteins. As compared to other existing methods, results obtained using our method correlated better with differences in wide range of activity of a known drug (Sunitinib) against nine different protein kinases (KIT, PDGFRA, VEGFR2, PHKG2, ITK, HPK1, MST3, PAK6 and CDK2).

摘要

几种不同的蛋白质具有相似的结合位点。蛋白质结合位点比较为药物发现和开发提供了有价值的见解。结合位点的相似性有助于理解多药理学、识别潜在的脱靶效应以及已知药物的重新利用。如今有许多结合位点分析和比较方法,然而,这些方法可能不足以解释药物或小分子对多种相似蛋白质的活性差异。蛋白质表面周围的水分子对蛋白质的结构和功能有贡献。水分子形成了多种类型的氢键连接的环状水环网络,称为拓扑水网络(TWNs)。分析蛋白质结合位点中的TWNs可能有助于更好地理解结合位点的特征。我们提出了基于TWN的残基编码(TWN-RENCOD),这是一种新颖的结合位点比较方法,用于比较相似蛋白质结合位点中的水环境。与其他现有方法相比,使用我们的方法获得的结果与已知药物(舒尼替尼)对九种不同蛋白激酶(KIT、PDGFRA、VEGFR2、PHKG2、ITK、HPK1、MST3、PAK6和CDK2)的广泛活性差异相关性更好。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74ff/9798139/29a372e08bed/ga1.jpg

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