• 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
Prediction of RNA binding sites in proteins from amino acid sequence.从氨基酸序列预测蛋白质中的RNA结合位点。
RNA. 2006 Aug;12(8):1450-62. doi: 10.1261/rna.2197306. Epub 2006 Jun 21.
2
RNABindR: a server for analyzing and predicting RNA-binding sites in proteins.RNABindR:一个用于分析和预测蛋白质中RNA结合位点的服务器。
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W578-84. doi: 10.1093/nar/gkm294. Epub 2007 May 5.
3
FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues.FastRNABindR:蛋白质-RNA 界面残基的快速准确预测
PLoS One. 2016 Jul 6;11(7):e0158445. doi: 10.1371/journal.pone.0158445. eCollection 2016.
4
Predicting DNA-binding sites of proteins from amino acid sequence.从氨基酸序列预测蛋白质的DNA结合位点。
BMC Bioinformatics. 2006 May 19;7:262. doi: 10.1186/1471-2105-7-262.
5
Prediction of protein-RNA binding sites by a random forest method with combined features.基于组合特征的随机森林方法预测蛋白质-RNA 结合位点。
Bioinformatics. 2010 Jul 1;26(13):1616-22. doi: 10.1093/bioinformatics/btq253. Epub 2010 May 18.
6
Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.基于机器学习的蛋白质-RNA 界面残基预测:现状评估。
BMC Bioinformatics. 2012 May 10;13:89. doi: 10.1186/1471-2105-13-89.
7
A MOTIF-BASED METHOD FOR PREDICTING INTERFACIAL RESIDUES IN BOTH THE RNA AND PROTEIN COMPONENTS OF PROTEIN-RNA COMPLEXES.一种基于基序的方法,用于预测蛋白质-RNA复合物的RNA和蛋白质组分中的界面残基。
Pac Symp Biocomput. 2016;21:445-455.
8
PRIDB: a Protein-RNA interface database.PRIDB:一个蛋白质-核糖核酸相互作用界面数据库。
Nucleic Acids Res. 2011 Jan;39(Database issue):D277-82. doi: 10.1093/nar/gkq1108. Epub 2010 Nov 11.
9
Predicting RNA-binding sites in proteins using the interaction propensity of amino acid triplets.利用氨基酸三联体的相互作用倾向预测蛋白质中的RNA结合位点。
Protein Pept Lett. 2010 Sep;17(9):1102-10. doi: 10.2174/092986610791760388.
10
Efficient mapping of RNA-binding residues in RNA-binding proteins using local sequence features of binding site residues in protein-RNA complexes.利用蛋白质-RNA 复合物中结合位点残基的局部序列特征,高效绘制 RNA 结合蛋白中的 RNA 结合残基。
Proteins. 2023 Sep;91(9):1361-1379. doi: 10.1002/prot.26528. Epub 2023 May 31.

引用本文的文献

1
Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.蛋白质序列中核酸结合残基预测二十年进展
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbaf016.
2
A high-throughput search for intracellular factors that affect RNA folding identifies proteins PepA and YagL as RNA chaperones that promote RNA remodelling.高通量筛选影响 RNA 折叠的细胞内因子,鉴定出 PepA 和 YagL 两种蛋白为 RNA 分子伴侣,能促进 RNA 重塑。
RNA Biol. 2024 Jan;21(1):13-30. doi: 10.1080/15476286.2024.2429956. Epub 2024 Nov 22.
3
Predicting nuclear G-quadruplex RNA-binding proteins with roles in transcription and phase separation.预测具有转录和相分离作用的核 G-四链体 RNA 结合蛋白。
Nat Commun. 2024 Mar 22;15(1):2585. doi: 10.1038/s41467-024-46731-9.
4
PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences.PRIP:一种基于序列语义的蛋白质-核糖核酸界面预测工具
Life (Basel). 2022 Feb 18;12(2):307. doi: 10.3390/life12020307.
5
Mapping of Functional Subdomains in the ALKBH9B mA-Demethylase Required for Its Binding to the Viral RNA and to the Coat Protein of Alfalfa Mosaic Virus.ALKBH9B mA去甲基化酶中与病毒RNA及苜蓿花叶病毒外壳蛋白结合所需的功能亚结构域的定位
Front Plant Sci. 2021 Jul 5;12:701683. doi: 10.3389/fpls.2021.701683. eCollection 2021.
6
A Tale of Loops and Tails: The Role of Intrinsically Disordered Protein Regions in R-Loop Recognition and Phase Separation.环与尾的故事:内在无序蛋白区域在R环识别和相分离中的作用
Front Mol Biosci. 2021 Jun 10;8:691694. doi: 10.3389/fmolb.2021.691694. eCollection 2021.
7
Amino Acid Composition in Various Types of Nucleic Acid-Binding Proteins.各种核酸结合蛋白中的氨基酸组成。
Int J Mol Sci. 2021 Jan 18;22(2):922. doi: 10.3390/ijms22020922.
8
Concurrent binding to DNA and RNA facilitates the pluripotency reprogramming activity of Sox2.同时结合 DNA 和 RNA 有助于 Sox2 进行多能性重编程活性。
Nucleic Acids Res. 2020 Apr 17;48(7):3869-3887. doi: 10.1093/nar/gkaa067.
9
Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins.从不完整训练数据中发现功能位点:以核酸结合蛋白为例的研究
Front Genet. 2019 Aug 30;10:729. doi: 10.3389/fgene.2019.00729. eCollection 2019.
10
Deciphering the three-domain architecture in schlafens and the structures and roles of human schlafen12 and serpinB12 in transcriptional regulation.解析睡眠相关蛋白的三结构域架构以及人类睡眠相关蛋白12和丝氨酸蛋白酶抑制剂B12在转录调控中的结构与作用。
J Mol Graph Model. 2019 Jul;90:59-76. doi: 10.1016/j.jmgm.2019.04.003. Epub 2019 Apr 9.

本文引用的文献

1
Identification of interface residues in protease-inhibitor and antigen-antibody complexes: a support vector machine approach.蛋白酶抑制剂与抗原-抗体复合物中界面残基的鉴定:一种支持向量机方法。
Neural Comput Appl. 2004 Jun 1;13(2):123-129. doi: 10.1007/s00521-004-0414-3.
2
Identifying interaction sites in "recalcitrant" proteins: predicted protein and RNA binding sites in rev proteins of HIV-1 and EIAV agree with experimental data.识别“难处理”蛋白质中的相互作用位点:HIV-1和EIAV的Rev蛋白中预测的蛋白质和RNA结合位点与实验数据相符。
Pac Symp Biocomput. 2006:415-26.
3
The structure and function of telomerase reverse transcriptase.端粒酶逆转录酶的结构与功能。
Annu Rev Biochem. 2006;75:493-517. doi: 10.1146/annurev.biochem.75.103004.142412.
4
Predicting DNA-binding sites of proteins from amino acid sequence.从氨基酸序列预测蛋白质的DNA结合位点。
BMC Bioinformatics. 2006 May 19;7:262. doi: 10.1186/1471-2105-7-262.
5
An algorithm for predicting protein-protein interaction sites: Abnormally exposed amino acid residues and secondary structure elements.一种预测蛋白质-蛋白质相互作用位点的算法:异常暴露的氨基酸残基和二级结构元件。
Protein Sci. 2006 May;15(5):1017-29. doi: 10.1110/ps.051589106.
6
Crystal structure of the essential N-terminal domain of telomerase reverse transcriptase.端粒酶逆转录酶必需的N端结构域的晶体结构
Nat Struct Mol Biol. 2006 Mar;13(3):218-25. doi: 10.1038/nsmb1054. Epub 2006 Feb 5.
7
WHISCY: what information does surface conservation yield? Application to data-driven docking.WHISCY:表面保守性能产生什么信息?在数据驱动对接中的应用。
Proteins. 2006 May 15;63(3):479-89. doi: 10.1002/prot.20842.
8
Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines.利用支持向量机从一级结构预测核糖体RNA、RNA和DNA结合蛋白。
J Theor Biol. 2006 May 21;240(2):175-84. doi: 10.1016/j.jtbi.2005.09.018. Epub 2005 Nov 7.
9
Soluble domains of telomerase reverse transcriptase identified by high-throughput screening.通过高通量筛选鉴定的端粒酶逆转录酶的可溶性结构域。
Protein Sci. 2005 Aug;14(8):2051-8. doi: 10.1110/ps.051532105.
10
An anchor site-type defect in human telomerase that disrupts telomere length maintenance and cellular immortalization.人类端粒酶中一种锚定位点型缺陷,该缺陷会破坏端粒长度维持和细胞永生化。
Mol Biol Cell. 2005 Jul;16(7):3152-61. doi: 10.1091/mbc.e05-02-0148. Epub 2005 Apr 27.

从氨基酸序列预测蛋白质中的RNA结合位点。

Prediction of RNA binding sites in proteins from amino acid sequence.

作者信息

Terribilini Michael, Lee Jae-Hyung, Yan Changhui, Jernigan Robert L, Honavar Vasant, Dobbs Drena

机构信息

Bioinformatics and Computationa Biology, Graduate Program, Iowa State University, Ames, Iowa 50010, USA.

出版信息

RNA. 2006 Aug;12(8):1450-62. doi: 10.1261/rna.2197306. Epub 2006 Jun 21.

DOI:10.1261/rna.2197306
PMID:16790841
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1524891/
Abstract

RNA-protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA-protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA-protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA-protein interface. In leave-one-out cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA-protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.).

摘要

RNA与蛋白质的相互作用在广泛的生物过程中至关重要,包括基因表达调控、蛋白质合成以及许多病毒的复制和组装。我们开发了一种计算工具,仅使用蛋白质序列作为输入,来预测RNA结合蛋白的哪些氨基酸参与RNA与蛋白质的相互作用。RNABindR是利用机器学习,基于蛋白质数据库中已知RNA-蛋白质复合物的经过验证的非冗余界面数据集开发的。它生成一个分类器,该分类器捕获足以预测给定蛋白质中哪些氨基酸位于RNA-蛋白质界面的一级序列信号。在留一法交叉验证实验中,RNABindR识别界面残基的总体准确率超过85%。用户可以对其进行校准,以获得对界面残基的高特异性或高敏感性。RNABindR采用朴素贝叶斯分类器,其性能与更复杂的神经网络分类器相当(据我们所知,这是之前唯一发表的基于序列的RNA结合位点预测方法),并且具有速度快、简单以及结果可解释的优点。对人类端粒酶蛋白hTERT的RNABindR预测结果与实验数据高度吻合。能够预测RNA结合蛋白中哪些残基可能与RNA接触的计算工具的出现,应有助于设计直接测试RNA结合功能的实验,并有助于我们理解生物系统中RNA-蛋白质复合物的多样性、机制和调控。(可从http://bindr.gdcb.iastate.edu.作为网络工具获取RNABindR。)