• 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

相似文献

1
Implications of the small number of distinct ligand binding pockets in proteins for drug discovery, evolution and biochemical function.蛋白质中数量有限的独特配体结合口袋对药物发现、进化和生化功能的影响。
Bioorg Med Chem Lett. 2015 Mar 15;25(6):1163-70. doi: 10.1016/j.bmcl.2015.01.059. Epub 2015 Feb 3.
2
Interplay of physics and evolution in the likely origin of protein biochemical function.物理与进化在蛋白质生化功能起源中的相互作用。
Proc Natl Acad Sci U S A. 2013 Jun 4;110(23):9344-9. doi: 10.1073/pnas.1300011110. Epub 2013 May 20.
3
Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design.蛋白质口袋与腔的剖析:结合位点几何形状的测量及其对配体设计的影响
Protein Sci. 1998 Sep;7(9):1884-97. doi: 10.1002/pro.5560070905.
4
PoLi: A Virtual Screening Pipeline Based on Template Pocket and Ligand Similarity.PoLi:一种基于模板口袋和配体相似性的虚拟筛选流程
J Chem Inf Model. 2015 Aug 24;55(8):1757-70. doi: 10.1021/acs.jcim.5b00232. Epub 2015 Aug 12.
5
Visualisation of variable binding pockets on protein surfaces by probabilistic analysis of related structure sets.通过对相关结构集合的概率分析来可视化蛋白质表面上的变构结合口袋。
BMC Bioinformatics. 2012 Mar 14;13:39. doi: 10.1186/1471-2105-13-39.
6
Graph-Based Clustering of Predicted Ligand-Binding Pockets on Protein Surfaces.基于图的蛋白质表面预测配体结合口袋聚类。
J Chem Inf Model. 2015 Sep 28;55(9):1944-52. doi: 10.1021/acs.jcim.5b00045. Epub 2015 Sep 11.
7
Self-organizing fuzzy graphs for structure-based comparison of protein pockets.基于结构的蛋白质口袋比较的自组织模糊图。
J Proteome Res. 2010 Dec 3;9(12):6498-510. doi: 10.1021/pr100719n. Epub 2010 Oct 22.
8
Comparative assessment of strategies to identify similar ligand-binding pockets in proteins.比较评估鉴定蛋白质中相似配体结合口袋的策略。
BMC Bioinformatics. 2018 Mar 9;19(1):91. doi: 10.1186/s12859-018-2109-2.
9
On the importance of composite protein multiple ligand interactions in protein pockets.在蛋白质口袋中复合蛋白多配体相互作用的重要性。
J Comput Chem. 2017 Jun 5;38(15):1252-1259. doi: 10.1002/jcc.24523. Epub 2016 Nov 16.
10
A comprehensive survey of small-molecule binding pockets in proteins.蛋白质中小分子结合口袋的综合调查。
PLoS Comput Biol. 2013 Oct;9(10):e1003302. doi: 10.1371/journal.pcbi.1003302. Epub 2013 Oct 24.

引用本文的文献

1
Entabolons: How Metabolites Modify the Biochemical Function of Proteins and Cause the Correlated Behavior of Proteins in Pathways.代谢物组:代谢物如何改变蛋白质的生化功能并导致蛋白质在代谢途径中的相关行为。
J Chem Inf Model. 2025 Jun 9;65(11):5785-5800. doi: 10.1021/acs.jcim.5c00462. Epub 2025 May 16.
2
GraphSite: Ligand Binding Site Classification with Deep Graph Learning.GraphSite:基于深度图学习的配体结合位点分类。
Biomolecules. 2022 Jul 29;12(8):1053. doi: 10.3390/biom12081053.
3
Computational-approach understanding the structure-function prophecy of Fibrinolytic Protease RFEA1 from RSA1.从RSA1理解纤维蛋白溶解蛋白酶RFEA1的结构-功能预测的计算方法。
PeerJ. 2021 Jun 4;9:e11570. doi: 10.7717/peerj.11570. eCollection 2021.
4
Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.虚拟筛选网络服务器:在网络空间中设计化学探针和药物候选物。
Brief Bioinform. 2021 Mar 22;22(2):1790-1818. doi: 10.1093/bib/bbaa034.
5
BionoiNet: ligand-binding site classification with off-the-shelf deep neural network.BionoiNet:基于现成深度神经网络的配体结合位点分类。
Bioinformatics. 2020 May 1;36(10):3077-3083. doi: 10.1093/bioinformatics/btaa094.
6
Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.计算方法和工具用于识别蛋白质和小分子之间的结合位点:从经典的几何方法到现代机器学习策略。
J Comput Aided Mol Des. 2019 Oct;33(10):887-903. doi: 10.1007/s10822-019-00235-7. Epub 2019 Oct 18.
7
High Impact: The Role of Promiscuous Binding Sites in Polypharmacology.高影响力:别构结合位点在多靶标药物化学中的作用。
Molecules. 2019 Jul 10;24(14):2529. doi: 10.3390/molecules24142529.
8
3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns.3D-PP:一种发现保守三维蛋白质模式的工具。
Int J Mol Sci. 2019 Jun 28;20(13):3174. doi: 10.3390/ijms20133174.
9
Exploring a new ligand binding site of G protein-coupled receptors.探索G蛋白偶联受体的一个新配体结合位点。
Chem Sci. 2018 Jul 13;9(31):6480-6489. doi: 10.1039/c8sc01680a. eCollection 2018 Aug 21.
10
In silico fragment-mapping method: a new tool for fragment-based/structure-based drug discovery.基于片段的/基于结构的药物发现的新工具:计算片段映射方法。
J Comput Aided Mol Des. 2018 Nov;32(11):1229-1245. doi: 10.1007/s10822-018-0160-8. Epub 2018 Sep 8.

本文引用的文献

1
Structures of human steroidogenic cytochrome P450 17A1 with substrates.人甾体生成细胞色素P450 17A1与底物的结构。
J Biol Chem. 2014 Nov 21;289(47):32952-64. doi: 10.1074/jbc.M114.610998. Epub 2014 Oct 9.
2
Interaction between 2 extracellular loops influences the activity of the cystic fibrosis transmembrane conductance regulator chloride channel.两个细胞外环之间的相互作用影响囊性纤维化跨膜传导调节因子氯离子通道的活性。
Biochem Cell Biol. 2014 Oct;92(5):390-6. doi: 10.1139/bcb-2014-0066. Epub 2014 Aug 20.
3
Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization.使用因果推断-概率矩阵分解进行复杂疾病中的药物-疾病关联及药物重新定位预测
J Chem Inf Model. 2014 Sep 22;54(9):2562-9. doi: 10.1021/ci500340n. Epub 2014 Aug 22.
4
Experimental validation of FINDSITE(comb) virtual ligand screening results for eight proteins yields novel nanomolar and micromolar binders.对 FINDSITE(comb)虚拟配体筛选结果针对八种蛋白质进行实验验证,得到了新型纳摩尔和微摩尔结合物。
J Cheminform. 2014 Apr 26;6:16. doi: 10.1186/1758-2946-6-16. eCollection 2014.
5
Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature.从生物医学文献中自动构建大规模且准确的药物-副作用关联知识库。
J Biomed Inform. 2014 Oct;51:191-9. doi: 10.1016/j.jbi.2014.05.013. Epub 2014 Jun 10.
6
Polypharmacology rescored: protein-ligand interaction profiles for remote binding site similarity assessment.多药理学重新评分:用于远程结合位点相似性评估的蛋白质-配体相互作用图谱。
Prog Biophys Mol Biol. 2014 Nov-Dec;116(2-3):174-86. doi: 10.1016/j.pbiomolbio.2014.05.006. Epub 2014 Jun 9.
7
Probabilistic drug connectivity mapping.概率药物关联映射。
BMC Bioinformatics. 2014 Apr 17;15:113. doi: 10.1186/1471-2105-15-113.
8
Repositioning: the fast track to new anti-malarial medicines?重新定位:新型抗疟药物的快速通道?
Malar J. 2014 Apr 14;13:143. doi: 10.1186/1475-2875-13-143.
9
Cystic fibrosis: an inherited disease affecting mucin-producing organs.囊性纤维化:一种影响产生粘蛋白器官的遗传性疾病。
Int J Biochem Cell Biol. 2014 Jul;52:136-45. doi: 10.1016/j.biocel.2014.03.011. Epub 2014 Mar 28.
10
Construction of drug network based on side effects and its application for drug repositioning.基于副作用构建药物网络及其在药物重新定位中的应用。
PLoS One. 2014 Feb 4;9(2):e87864. doi: 10.1371/journal.pone.0087864. eCollection 2014.

蛋白质中数量有限的独特配体结合口袋对药物发现、进化和生化功能的影响。

Implications of the small number of distinct ligand binding pockets in proteins for drug discovery, evolution and biochemical function.

作者信息

Skolnick Jeffrey, Gao Mu, Roy Ambrish, Srinivasan Bharath, Zhou Hongyi

机构信息

Center for the Study of Systems Biology, Georgia Institute of Technology, 250 14th St NW, Atlanta, GA 30318, USA.

Center for the Study of Systems Biology, Georgia Institute of Technology, 250 14th St NW, Atlanta, GA 30318, USA.

出版信息

Bioorg Med Chem Lett. 2015 Mar 15;25(6):1163-70. doi: 10.1016/j.bmcl.2015.01.059. Epub 2015 Feb 3.

DOI:10.1016/j.bmcl.2015.01.059
PMID:25690787
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4593502/
Abstract

Coincidence of the properties of ligand binding pockets in native proteins with those in proteins generated by computer simulations without selection for function shows that pockets are a generic protein feature and the number of distinct pockets is small. Similar pockets occur in unrelated protein structures, an observation successfully employed in pocket-based virtual ligand screening. The small number of pockets suggests that off-target interactions among diverse proteins are inherent; kinases, proteases and phosphatases show this prototypical behavior. The ability to repurpose FDA approved drugs is general, and minor side effects cannot be avoided. Finally, the implications to drug discovery are explored.

摘要

天然蛋白质中配体结合口袋的性质与计算机模拟生成的、未经过功能筛选的蛋白质中的配体结合口袋的性质相吻合,这表明口袋是蛋白质的一个普遍特征,且不同口袋的数量很少。相似的口袋出现在不相关的蛋白质结构中,这一观察结果已成功应用于基于口袋的虚拟配体筛选。口袋数量少表明不同蛋白质之间的脱靶相互作用是固有的;激酶、蛋白酶和磷酸酶都表现出这种典型行为。重新利用FDA批准药物的能力是普遍存在的,且无法避免轻微的副作用。最后,探讨了其对药物发现的影响。