• 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

蛋白质社交行为在伴侣识别方面比表面几何形状发出更强的信号。

Protein social behavior makes a stronger signal for partner identification than surface geometry.

作者信息

Laine Elodie, Carbone Alessandra

机构信息

Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Paris, 75005, France.

Institut Universitaire de France, Paris, 75005, France.

出版信息

Proteins. 2017 Jan;85(1):137-154. doi: 10.1002/prot.25206. Epub 2016 Nov 20.

DOI:10.1002/prot.25206
PMID:27802579
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5242317/
Abstract

Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico-chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross-docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S-index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface-based (ranking) score to discriminate partners from non-interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137-154. © 2016 Wiley Periodicals, Inc.

摘要

细胞是相互作用的生命系统,其中蛋白质的运动、相互作用和调控基本上不受集中管理。蛋白质的物理化学和几何特性如何决定其与谁相互作用,目前仍远未完全清楚。我们表明,在完整的交叉对接研究中,表征一种蛋白质与许多潜在相互作用分子的行为方式,能够明确识别其细胞内/真实/天然的伙伴。我们定义了一个社交指数,即S指数,以反映一种蛋白质是否倾向于与其他蛋白质配对。形式上,我们提出了一个合适的归一化函数来衡量蛋白质的社交性,并将其与基于简单界面的(排序)分数相结合,以区分相互作用的伙伴和非相互作用分子。我们表明,社交性是一个重要因素,并且这种归一化方法比基于形状互补的对接分数具有更高的区分能力。在更复杂的对接算法中也观察到了这种社交效应。对接构象使用实验性结合位点进行评估。后者尽可能最佳地近似结合位点预测,近年来结合位点预测已达到高精度。这使得我们的分析有助于全面理解伙伴识别,并有助于提出区分策略。这些结果与之前声称仅通过几何对接就能解决伙伴识别问题的研究结果相矛盾。《蛋白质》2016年;85:137 - 154。© 2016威利期刊公司。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/61540f4ab1ae/PROT-85-137-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/c78da751fa40/PROT-85-137-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/c0eefd450959/PROT-85-137-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/fe02bbf40ca0/PROT-85-137-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/c4b20a3e3c03/PROT-85-137-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/51f03ed8b448/PROT-85-137-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/f4149c7fadcb/PROT-85-137-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/61540f4ab1ae/PROT-85-137-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/c78da751fa40/PROT-85-137-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/c0eefd450959/PROT-85-137-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/fe02bbf40ca0/PROT-85-137-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/c4b20a3e3c03/PROT-85-137-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/51f03ed8b448/PROT-85-137-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/f4149c7fadcb/PROT-85-137-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/954f/5242317/61540f4ab1ae/PROT-85-137-g007.jpg

相似文献

1
Protein social behavior makes a stronger signal for partner identification than surface geometry.蛋白质社交行为在伴侣识别方面比表面几何形状发出更强的信号。
Proteins. 2017 Jan;85(1):137-154. doi: 10.1002/prot.25206. Epub 2016 Nov 20.
2
Great interactions: How binding incorrect partners can teach us about protein recognition and function.精彩的相互作用:结合错误的伴侣如何让我们了解蛋白质识别与功能。
Proteins. 2016 Oct;84(10):1408-21. doi: 10.1002/prot.25086. Epub 2016 Jun 24.
3
CLUB-MARTINI: Selecting Favourable Interactions amongst Available Candidates, a Coarse-Grained Simulation Approach to Scoring Docking Decoys.CLUB-MARTINI:在现有候选物中选择有利相互作用,一种用于对接诱饵评分的粗粒度模拟方法。
PLoS One. 2016 May 11;11(5):e0155251. doi: 10.1371/journal.pone.0155251. eCollection 2016.
4
Decrypting protein surfaces by combining evolution, geometry, and molecular docking.通过结合进化、几何和分子对接来解析蛋白质表面。
Proteins. 2019 Nov;87(11):952-965. doi: 10.1002/prot.25757. Epub 2019 Jun 26.
5
Scoring optimisation of unbound protein-protein docking including protein binding site predictions.无蛋白-蛋白对接中包括蛋白结合位点预测的打分优化。
J Mol Recognit. 2012 Jan;25(1):15-23. doi: 10.1002/jmr.1165.
6
Protein-protein structure prediction by scoring molecular dynamics trajectories of putative poses.通过对假定构象的分子动力学轨迹进行评分来预测蛋白质-蛋白质结构。
Proteins. 2016 Sep;84(9):1312-20. doi: 10.1002/prot.25079. Epub 2016 Jun 23.
7
ClusPro: an automated docking and discrimination method for the prediction of protein complexes.ClusPro:一种用于预测蛋白质复合物的自动对接与区分方法。
Bioinformatics. 2004 Jan 1;20(1):45-50. doi: 10.1093/bioinformatics/btg371.
8
Protein-protein docking with binding site patch prediction and network-based terms enhanced combinatorial scoring.蛋白质-蛋白质对接,结合位点贴片预测和基于网络术语增强的组合评分。
Proteins. 2010 Nov 15;78(15):3150-5. doi: 10.1002/prot.22831.
9
Rigid-Docking Approaches to Explore Protein-Protein Interaction Space.探索蛋白质-蛋白质相互作用空间的刚性对接方法。
Adv Biochem Eng Biotechnol. 2017;160:33-55. doi: 10.1007/10_2016_41.
10
Rescore protein-protein docked ensembles with an interface contact statistics.使用界面接触统计对蛋白质-蛋白质对接集合进行重新评分。
Proteins. 2017 Feb;85(2):235-241. doi: 10.1002/prot.25209. Epub 2016 Dec 5.

引用本文的文献

1
SENSE-PPI reconstructs interactomes within, across, and between species at the genome scale.SENSE-PPI在基因组规模上重建物种内部、物种间以及物种之间的相互作用组。
iScience. 2024 Jun 25;27(7):110371. doi: 10.1016/j.isci.2024.110371. eCollection 2024 Jul 19.
2
Deep Local Analysis evaluates protein docking conformations with locally oriented cubes.深度局部分析使用局部定向的立方块评估蛋白质对接构象。
Bioinformatics. 2022 Sep 30;38(19):4505-4512. doi: 10.1093/bioinformatics/btac551.
3
From complete cross-docking to partners identification and binding sites predictions.

本文引用的文献

1
Great interactions: How binding incorrect partners can teach us about protein recognition and function.精彩的相互作用:结合错误的伴侣如何让我们了解蛋白质识别与功能。
Proteins. 2016 Oct;84(10):1408-21. doi: 10.1002/prot.25086. Epub 2016 Jun 24.
2
Local Geometry and Evolutionary Conservation of Protein Surfaces Reveal the Multiple Recognition Patches in Protein-Protein Interactions.蛋白质表面的局部几何结构与进化保守性揭示了蛋白质-蛋白质相互作用中的多个识别位点
PLoS Comput Biol. 2015 Dec 21;11(12):e1004580. doi: 10.1371/journal.pcbi.1004580. eCollection 2015 Dec.
3
Computational prediction of protein interfaces: A review of data driven methods.
从完全交叉对接,到合作伙伴识别和结合位点预测。
PLoS Comput Biol. 2022 Jan 28;18(1):e1009825. doi: 10.1371/journal.pcbi.1009825. eCollection 2022 Jan.
4
Decrypting protein surfaces by combining evolution, geometry, and molecular docking.通过结合进化、几何和分子对接来解析蛋白质表面。
Proteins. 2019 Nov;87(11):952-965. doi: 10.1002/prot.25757. Epub 2019 Jun 26.
5
Identification and visualization of protein binding regions with the ArDock server.利用 ArDock 服务器鉴定和可视化蛋白质结合区域。
Nucleic Acids Res. 2018 Jul 2;46(W1):W417-W422. doi: 10.1093/nar/gky472.
蛋白质界面的计算预测:数据驱动方法综述
FEBS Lett. 2015 Nov 30;589(23):3516-26. doi: 10.1016/j.febslet.2015.10.003. Epub 2015 Oct 13.
4
Contacts-based prediction of binding affinity in protein-protein complexes.基于接触的蛋白质-蛋白质复合物结合亲和力预测
Elife. 2015 Jul 20;4:e07454. doi: 10.7554/eLife.07454.
5
Protein-protein interactions in a crowded environment: an analysis via cross-docking simulations and evolutionary information.拥挤环境中的蛋白质-蛋白质相互作用:通过对接模拟和进化信息进行分析。
PLoS Comput Biol. 2013;9(12):e1003369. doi: 10.1371/journal.pcbi.1003369. Epub 2013 Dec 5.
6
PAIRpred: partner-specific prediction of interacting residues from sequence and structure.PAIRpred:基于序列和结构的相互作用残基的特定伙伴预测。
Proteins. 2014 Jul;82(7):1142-55. doi: 10.1002/prot.24479. Epub 2013 Dec 6.
7
Arbitrary protein-protein docking targets biologically relevant interfaces.任意蛋白质-蛋白质对接针对的是生物学上相关的界面。
BMC Biophys. 2012 May 6;5:7. doi: 10.1186/2046-1682-5-7.
8
Predicting protein-protein interface residues using local surface structural similarity.利用局部表面结构相似性预测蛋白质-蛋白质界面残基。
BMC Bioinformatics. 2012 Mar 18;13:41. doi: 10.1186/1471-2105-13-41.
9
Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.基于序列数据的蛋白质-蛋白质复合物中相互作用残基的伙伴感知预测。
PLoS One. 2011;6(12):e29104. doi: 10.1371/journal.pone.0029104. Epub 2011 Dec 14.
10
InterEvol database: exploring the structure and evolution of protein complex interfaces.InterEvol 数据库:探索蛋白质复合物界面的结构和演化。
Nucleic Acids Res. 2012 Jan;40(Database issue):D847-56. doi: 10.1093/nar/gkr845. Epub 2011 Nov 3.