• 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

dbMPIKT:一个关于动力学和热力学突变蛋白相互作用的数据库。

dbMPIKT: a database of kinetic and thermodynamic mutant protein interactions.

机构信息

Institute of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.

School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, 243032, Anhui, China.

出版信息

BMC Bioinformatics. 2018 Nov 27;19(1):455. doi: 10.1186/s12859-018-2493-7.

DOI:10.1186/s12859-018-2493-7
PMID:30482172
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6260753/
Abstract

BACKGROUND

Protein-protein interactions (PPIs) play important roles in biological functions. Studies of the effects of mutants on protein interactions can provide further understanding of PPIs. Currently, many databases collect experimental mutants to assess protein interactions, but most of these databases are old and have not been updated for several years.

RESULTS

To address this issue, we manually curated a kinetic and thermodynamic database of mutant protein interactions (dbMPIKT) that is freely accessible at our website. This database contains 5291 mutants in protein interactions collected from previous databases and the literature published within the last three years. Furthermore, some data analysis, such as mutation number, mutation type, protein pair source and network map construction, can be performed online.

CONCLUSION

Our work can promote the study on PPIs, and novel information can be mined from the new database. Our database is available in http://DeepLearner.ahu.edu.cn/web/dbMPIKT/ for use by all, including both academics and non-academics.

摘要

背景

蛋白质-蛋白质相互作用(PPIs)在生物功能中发挥着重要作用。研究突变体对蛋白质相互作用的影响可以进一步了解 PPIs。目前,许多数据库收集实验突变体来评估蛋白质相互作用,但这些数据库大多比较陈旧,已经有好几年没有更新了。

结果

为了解决这个问题,我们手动整理了一个突变体蛋白质相互作用的动力学和热力学数据库(dbMPIKT),该数据库可在我们的网站上免费访问。该数据库包含了从以前的数据库和过去三年发表的文献中收集的 5291 个蛋白质相互作用突变体。此外,还可以在线进行一些数据分析,如突变数量、突变类型、蛋白质对来源和网络图构建。

结论

我们的工作可以促进对 PPIs 的研究,并且可以从新数据库中挖掘新的信息。我们的数据库可在 http://DeepLearner.ahu.edu.cn/web/dbMPIKT/ 上供所有人使用,包括学术界和非学术界人士。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e13/6260753/ba7be6c2c255/12859_2018_2493_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e13/6260753/42722c279470/12859_2018_2493_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e13/6260753/ed45dc997229/12859_2018_2493_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e13/6260753/4ca9a6e1da6c/12859_2018_2493_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e13/6260753/ba7be6c2c255/12859_2018_2493_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e13/6260753/42722c279470/12859_2018_2493_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e13/6260753/ed45dc997229/12859_2018_2493_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e13/6260753/4ca9a6e1da6c/12859_2018_2493_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e13/6260753/ba7be6c2c255/12859_2018_2493_Fig4_HTML.jpg

相似文献

1
dbMPIKT: a database of kinetic and thermodynamic mutant protein interactions.dbMPIKT:一个关于动力学和热力学突变蛋白相互作用的数据库。
BMC Bioinformatics. 2018 Nov 27;19(1):455. doi: 10.1186/s12859-018-2493-7.
2
IIIDB: a database for isoform-isoform interactions and isoform network modules.IIIDB:一个用于异构体-异构体相互作用和异构体网络模块的数据库。
BMC Genomics. 2015;16 Suppl 2(Suppl 2):S10. doi: 10.1186/1471-2164-16-S2-S10. Epub 2015 Jan 21.
3
Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.蛋白质热力学数据库在理解蛋白质突变体稳定性及设计稳定突变体方面的应用。
Methods Mol Biol. 2016;1415:71-89. doi: 10.1007/978-1-4939-3572-7_4.
4
SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models.SKEMPI:突变蛋白相互作用的结构动力学和能量学数据库及其在经验模型中的应用。
Bioinformatics. 2012 Oct 15;28(20):2600-7. doi: 10.1093/bioinformatics/bts489. Epub 2012 Aug 1.
5
ProTherm, Thermodynamic Database for Proteins and Mutants: developments in version 3.0.ProTherm:蛋白质与突变体的热力学数据库——3.0 版本的进展
Nucleic Acids Res. 2002 Jan 1;30(1):301-2. doi: 10.1093/nar/30.1.301.
6
ProTherm, version 4.0: thermodynamic database for proteins and mutants.ProTherm 4.0版:蛋白质和突变体的热力学数据库。
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D120-1. doi: 10.1093/nar/gkh082.
7
PINT: Protein-protein Interactions Thermodynamic Database.PINT:蛋白质-蛋白质相互作用热力学数据库。
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D195-8. doi: 10.1093/nar/gkj017.
8
K-Pro: Kinetics Data on Proteins and Mutants.K-Pro:蛋白质和突变体的动力学数据。
J Mol Biol. 2023 Oct 15;435(20):168245. doi: 10.1016/j.jmb.2023.168245. Epub 2023 Aug 23.
9
PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics.PROXiMATE:一个突变蛋白质-蛋白质复合物热力学和动力学数据库。
Bioinformatics. 2017 Sep 1;33(17):2787-2788. doi: 10.1093/bioinformatics/btx312.
10
Thermodynamic databases for proteins and protein-nucleic acid interactions.蛋白质及蛋白质-核酸相互作用的热力学数据库。
Biopolymers. 2001;61(2):121-6. doi: 10.1002/1097-0282(2002)61:2<121::AID-BIP10077>3.0.CO;2-1.

引用本文的文献

1
Decoding the effects of mutation on protein interactions using machine learning.利用机器学习解码突变对蛋白质相互作用的影响。
Biophys Rev (Melville). 2025 Feb 21;6(1):011307. doi: 10.1063/5.0249920. eCollection 2025 Mar.
2
Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.基于图掩蔽自蒸馏学习的蛋白质-蛋白质相互作用突变影响预测。
Commun Biol. 2024 Oct 26;7(1):1400. doi: 10.1038/s42003-024-07066-9.
3
Binding Curve Viewer: Visualizing the Equilibrium and Kinetics of Protein-Ligand Binding and Competitive Binding.

本文引用的文献

1
SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation.SKEMPI 2.0:一个更新的蛋白质-蛋白质结合能、动力学和热力学突变的基准。
Bioinformatics. 2019 Feb 1;35(3):462-469. doi: 10.1093/bioinformatics/bty635.
2
Protein-protein interface hot spots prediction based on a hybrid feature selection strategy.基于混合特征选择策略的蛋白质-蛋白质界面热点预测。
BMC Bioinformatics. 2018 Jan 15;19(1):14. doi: 10.1186/s12859-018-2009-5.
3
Protein binding hot spots prediction from sequence only by a new ensemble learning method.
结合曲线查看器:可视化蛋白质-配体结合和竞争结合的平衡和动力学。
J Chem Inf Model. 2024 May 27;64(10):4180-4192. doi: 10.1021/acs.jcim.4c00130. Epub 2024 May 8.
4
Quantification of biases in predictions of protein-protein binding affinity changes upon mutations.量化预测蛋白质突变后结合亲和力变化的偏倚。
Brief Bioinform. 2023 Nov 22;25(1). doi: 10.1093/bib/bbad491.
5
Prediction of hot spots towards drug discovery by protein sequence embedding with 1D convolutional neural network.通过一维卷积神经网络的蛋白质序列嵌入预测药物发现的热点。
PLoS One. 2023 Sep 18;18(9):e0290899. doi: 10.1371/journal.pone.0290899. eCollection 2023.
6
Deep Local Analysis deconstructs protein-protein interfaces and accurately estimates binding affinity changes upon mutation.深度局部分析方法可以对蛋白质-蛋白质界面进行解构,并准确估计突变对结合亲和力的影响。
Bioinformatics. 2023 Jun 30;39(39 Suppl 1):i544-i552. doi: 10.1093/bioinformatics/btad231.
7
Predicting Biomolecular Binding Kinetics: A Review.预测生物分子结合动力学:综述。
J Chem Theory Comput. 2023 Apr 25;19(8):2135-2148. doi: 10.1021/acs.jctc.2c01085. Epub 2023 Mar 29.
8
Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants.持续的拉普拉斯投影奥密克戎 BA.4 和 BA.5 成为新的优势变体。
Comput Biol Med. 2022 Dec;151(Pt A):106262. doi: 10.1016/j.compbiomed.2022.106262. Epub 2022 Nov 2.
9
Revealing the Threat of Emerging SARS-CoV-2 Mutations to Antibody Therapies.揭示新兴 SARS-CoV-2 突变对抗体疗法的威胁。
J Mol Biol. 2021 Sep 3;433(18):167155. doi: 10.1016/j.jmb.2021.167155. Epub 2021 Jul 14.
10
A topology-based network tree for the prediction of protein-protein binding affinity changes following mutation.一种基于拓扑结构的网络树,用于预测突变后蛋白质-蛋白质结合亲和力的变化。
Nat Mach Intell. 2020;2(2):116-123. doi: 10.1038/s42256-020-0149-6. Epub 2020 Feb 14.
仅通过一种新的集成学习方法从序列预测蛋白质结合热点
Amino Acids. 2017 Oct;49(10):1773-1785. doi: 10.1007/s00726-017-2474-6. Epub 2017 Aug 1.
4
Prediction of Protein Hotspots from Whole Protein Sequences by a Random Projection Ensemble System.基于随机投影集成系统的蛋白质序列全局预测蛋白质热点。
Int J Mol Sci. 2017 Jul 18;18(7):1543. doi: 10.3390/ijms18071543.
5
Characterizing the Hot Spots Involved in RON-MSPβ Complex Formation Using Alanine Scanning Mutagenesis and Molecular Dynamics Simulation.利用丙氨酸扫描诱变和分子动力学模拟表征RON-MSPβ复合物形成过程中的热点
Adv Pharm Bull. 2017 Apr;7(1):141-150. doi: 10.15171/apb.2017.018. Epub 2017 Apr 13.
6
Protein-Protein Interface and Disease: Perspective from Biomolecular Networks.蛋白质-蛋白质相互作用界面与疾病:来自生物分子网络的视角
Adv Biochem Eng Biotechnol. 2017;160:57-74. doi: 10.1007/10_2016_40.
7
Identification of self-interacting proteins by exploring evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix.通过探索嵌入在PSI-BLAST构建的位置特异性评分矩阵中的进化信息来鉴定自相互作用蛋白。
Oncotarget. 2016 Dec 13;7(50):82440-82449. doi: 10.18632/oncotarget.12517.
8
Protein-protein interaction inhibitors: advances in anticancer drug design.蛋白质-蛋白质相互作用抑制剂:抗癌药物设计的进展。
Expert Opin Drug Discov. 2016 Oct;11(10):957-68. doi: 10.1080/17460441.2016.1223038. Epub 2016 Sep 2.
9
A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces.一种用于蛋白质-蛋白质界面热点检测的机器学习方法。
Int J Mol Sci. 2016 Jul 27;17(8):1215. doi: 10.3390/ijms17081215.
10
Co-Occurring Atomic Contacts for the Characterization of Protein Binding Hot Spots.用于表征蛋白质结合热点的共现原子接触
PLoS One. 2015 Dec 16;10(12):e0144486. doi: 10.1371/journal.pone.0144486. eCollection 2015.