• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

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

网络分析和基于计算机的蛋白质-蛋白质相互作用预测及其在药物发现中的应用。

Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery.

机构信息

National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.

National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.

出版信息

Curr Opin Struct Biol. 2017 Jun;44:134-142. doi: 10.1016/j.sbi.2017.02.005. Epub 2017 Mar 30.

DOI:10.1016/j.sbi.2017.02.005
PMID:28364585
Abstract

Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.

摘要

蛋白质-蛋白质相互作用(PPIs)对于维持细胞内稳态至关重要。几种 PPI 的失调与各种疾病的病因有关,因此 PPI 已成为药物发现的有前途的靶点。PPIs 界面处的表面残基和热点残基形成核心区域,在调节细胞过程(如信号转导)中发挥关键作用,并被用作药物设计的起点。在这篇综述中,我们简要讨论了如何利用实验表征的 PPI 数据推断的 PPI 网络(PPIN)进行知识发现,以及计算方法在 PPI 特征描述中的应用如何有助于基于 PPIN 的生物研究。接下来,我们描述了计算 PPI 预测的原理,并调查了现有的用于药物发现的 PPI 和 PPI 位点预测服务器。最后,我们讨论了计算 PPI 预测在药物发现中的潜力。

相似文献

1
Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery.网络分析和基于计算机的蛋白质-蛋白质相互作用预测及其在药物发现中的应用。
Curr Opin Struct Biol. 2017 Jun;44:134-142. doi: 10.1016/j.sbi.2017.02.005. Epub 2017 Mar 30.
2
In silico structure-based approaches to discover protein-protein interaction-targeting drugs.基于结构的计算方法在发现靶向蛋白-蛋白相互作用药物中的应用。
Methods. 2017 Dec 1;131:22-32. doi: 10.1016/j.ymeth.2017.08.006. Epub 2017 Aug 9.
3
Evaluating protein-protein interaction (PPI) networks for diseases pathway, target discovery, and drug-design using 'in silico pharmacology'.使用“计算机药理学”评估疾病通路、靶点发现和药物设计的蛋白质-蛋白质相互作用(PPI)网络。
Curr Protein Pept Sci. 2014;15(6):561-71. doi: 10.2174/1389203715666140724090153.
4
In silico prediction of physical protein interactions and characterization of interactome orphans.计算机预测物理蛋白质相互作用及互作孤儿体的特征分析。
Nat Methods. 2015 Jan;12(1):79-84. doi: 10.1038/nmeth.3178. Epub 2014 Nov 17.
5
Multi-level bioinformatics resources support drug target discovery of protein-protein interactions.多层次生物信息学资源支持蛋白质-蛋白质相互作用的药物靶点发现。
Drug Discov Today. 2024 May;29(5):103979. doi: 10.1016/j.drudis.2024.103979. Epub 2024 Apr 10.
6
Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder.基于基因本体论(GO)和内在无序性探索枢纽蛋白与药物靶点之间的关系。
Comput Biol Chem. 2015 Jun;56:41-8. doi: 10.1016/j.compbiolchem.2015.03.003. Epub 2015 Mar 23.
7
Analyses of Protein Interaction Networks Using Computational Tools.使用计算工具分析蛋白质相互作用网络
Methods Mol Biol. 2018;1794:97-117. doi: 10.1007/978-1-4939-7871-7_7.
8
Triangle network motifs predict complexes by complementing high-error interactomes with structural information.三角形网络基序通过利用结构信息补充高误差相互作用组来预测复合物。
BMC Bioinformatics. 2009 Jun 27;10:196. doi: 10.1186/1471-2105-10-196.
9
Protein-protein interactions and prediction: a comprehensive overview.蛋白质-蛋白质相互作用与预测:全面综述
Protein Pept Lett. 2014;21(8):779-89. doi: 10.2174/09298665113209990056.
10
Prediction of Protein Interactions by Structural Matching: Prediction of PPI Networks and the Effects of Mutations on PPIs that Combines Sequence and Structural Information.通过结构匹配预测蛋白质相互作用:结合序列和结构信息预测蛋白质-蛋白质相互作用网络及突变对蛋白质-蛋白质相互作用的影响
Methods Mol Biol. 2017;1558:255-270. doi: 10.1007/978-1-4939-6783-4_12.

引用本文的文献

1
Mapping the interaction surface between Caβ and actin and its role in calcium channel clearance.绘制Caβ与肌动蛋白之间的相互作用表面及其在钙通道清除中的作用。
Nat Commun. 2025 May 10;16(1):4352. doi: 10.1038/s41467-025-59548-x.
2
Transcriptome and network analysis pinpoint ABA and plastid ribosomal proteins as main contributors to salinity tolerance in the rice variety, CSR28.转录组和网络分析确定脱落酸和质体核糖体蛋白是水稻品种CSR28耐盐性的主要贡献因素。
PLoS One. 2025 Apr 17;20(4):e0321181. doi: 10.1371/journal.pone.0321181. eCollection 2025.
3
Inspecting Biological Deregulation, Putative Markers, and Therapeutic Targets for Neurodegenerative Diseases Through an Integrative Bioinformatics Analysis of the Human Cerebrospinal Fluid Proteome: A Tutorial.
通过对人类脑脊液蛋白质组进行综合生物信息学分析来检查神经退行性疾病的生物失调、假定标志物和治疗靶点:教程
Methods Mol Biol. 2025;2914:275-302. doi: 10.1007/978-1-0716-4462-1_20.
4
Virtual screening combined with molecular docking for the !identification of new anti-adipogenic compounds.虚拟筛选结合分子对接用于鉴定新型抗脂肪生成化合物。
Sci Prog. 2025 Jan-Mar;108(1):368504251320313. doi: 10.1177/00368504251320313.
5
approaches supporting drug repurposing for Leishmaniasis: a scoping review.支持利什曼病药物再利用的方法:一项范围综述。
EXCLI J. 2024 Sep 3;23:1117-1169. doi: 10.17179/excli2024-7552. eCollection 2024.
6
Uncovering anti-inflammatory potential of Linn: Network pharmacology and in vitro studies.揭示林奈植物的抗炎潜力:网络药理学与体外研究
Narra J. 2024 Aug;4(2):e894. doi: 10.52225/narra.v4i2.894. Epub 2024 Aug 12.
7
Long Non-Coding RNAs, Nuclear Receptors and Their Cross-Talks in Cancer-Implications and Perspectives.长链非编码RNA、核受体及其在癌症中的相互作用——影响与展望
Cancers (Basel). 2024 Aug 22;16(16):2920. doi: 10.3390/cancers16162920.
8
A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions.一种平衡多尺度学习蛋白质和药物相互作用的变分期望最大化框架。
Nat Commun. 2024 May 25;15(1):4476. doi: 10.1038/s41467-024-48801-4.
9
In Silico Analysis of Protein-Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in .内质网金属肽酶1假定蛋白-蛋白相互作用的计算机模拟分析
Curr Issues Mol Biol. 2024 May 12;46(5):4609-4629. doi: 10.3390/cimb46050280.
10
Databases of ligand-binding pockets and protein-ligand interactions.配体结合口袋和蛋白质-配体相互作用的数据库。
Comput Struct Biotechnol J. 2024 Mar 24;23:1320-1338. doi: 10.1016/j.csbj.2024.03.015. eCollection 2024 Dec.