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利用进化信息从蛋白质的一级序列预测 FAD 相互作用残基。

Prediction of FAD interacting residues in a protein from its primary sequence using evolutionary information.

机构信息

Institute of Microbial Technology, Sector 39A, Chandigarh, India.

出版信息

BMC Bioinformatics. 2010 Jan 18;11 Suppl 1(Suppl 1):S48. doi: 10.1186/1471-2105-11-S1-S48.

DOI:10.1186/1471-2105-11-S1-S48
PMID:20122222
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3009520/
Abstract

BACKGROUND

Flavin binding proteins (FBP) plays a critical role in several biological functions such as electron transport system (ETS). These flavoproteins contain very tightly bound, sometimes covalently, flavin adenine dinucleotide (FAD) or flavin mono nucleotide (FMN). The interaction between flavin nucleotide and amino acids of flavoprotein is essential for their functionality. Thus identification of FAD interacting residues in a FBP is an important step for understanding their function and mechanism.

RESULTS

In this study, we describe models developed for predicting FAD interacting residues using 15, 17 and 19 window pattern. Support vector machine (SVM) based models have been developed using binary pattern of amino acid sequence of protein and achieved maximum accuracy 69.65% with Mathew's Correlation Coefficient (MCC) 0.39 and Area Under Curve (AUC) 0.773. The performance of these models have been improved significantly from 69.65% to 82.86% with MCC 0.66 and AUC 0.904, when evolutionary information is used as input in SVM. The evolutionary information was generated in form of position specific score matrix (PSSM) profile by using PSI-BLAST at e-value 0.001. All models were developed on 198 non-redundant FAD binding protein chains containing 5172 FAD interacting residues and evaluated using fivefold cross-validation technique.

CONCLUSION

This study suggests that evolutionary information of 17 amino acid patterns perform best for FAD interacting residues prediction. We also developed a web server which predicts FAD interacting residues in a protein which is freely available for academics.

摘要

背景

黄素结合蛋白(FBP)在许多生物学功能中起着关键作用,例如电子传递系统(ETS)。这些黄素蛋白含有非常紧密结合的黄素腺嘌呤二核苷酸(FAD)或黄素单核苷酸(FMN),有时是共价结合的。黄素核苷酸与黄素蛋白氨基酸之间的相互作用对于它们的功能至关重要。因此,鉴定 FBP 中的 FAD 相互作用残基是理解其功能和机制的重要步骤。

结果

在这项研究中,我们描述了使用 15、17 和 19 窗口模式开发的用于预测 FAD 相互作用残基的模型。使用蛋白质氨基酸序列的二进制模式开发了支持向量机(SVM)模型,最大精度为 69.65%,马修相关系数(MCC)为 0.39,曲线下面积(AUC)为 0.773。当将进化信息用作 SVM 的输入时,这些模型的性能从 69.65%显著提高到 82.86%,MCC 为 0.66,AUC 为 0.904。进化信息通过在 e 值为 0.001 时使用 PSI-BLAST 以位置特异性评分矩阵(PSSM)形式生成。所有模型均在包含 5172 个 FAD 相互作用残基的 198 个非冗余 FAD 结合蛋白链上开发,并使用五重交叉验证技术进行评估。

结论

本研究表明,17 个氨基酸模式的进化信息最适合 FAD 相互作用残基预测。我们还开发了一个免费提供给学术界使用的预测蛋白质中 FAD 相互作用残基的网络服务器。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e3/3009520/a7ebca1f84ba/1471-2105-11-S1-S48-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e3/3009520/8845aa556603/1471-2105-11-S1-S48-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e3/3009520/a7ebca1f84ba/1471-2105-11-S1-S48-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e3/3009520/8845aa556603/1471-2105-11-S1-S48-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3e3/3009520/a7ebca1f84ba/1471-2105-11-S1-S48-2.jpg

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本文引用的文献

1
SuperSite: dictionary of metabolite and drug binding sites in proteins.超级位点:蛋白质中代谢物和药物结合位点词典
Nucleic Acids Res. 2009 Jan;37(Database issue):D195-200. doi: 10.1093/nar/gkn618. Epub 2008 Oct 8.
2
Prediction of RNA binding sites in a protein using SVM and PSSM profile.使用支持向量机和位置特异性得分矩阵预测蛋白质中的RNA结合位点。
Proteins. 2008 Apr;71(1):189-94. doi: 10.1002/prot.21677.
3
Prediction of DNA-binding residues from sequence.从序列预测DNA结合残基。
NAGbinder:一种从蛋白质一级序列中识别 N-乙酰葡萄糖胺相互作用残基的方法。
Protein Sci. 2020 Jan;29(1):201-210. doi: 10.1002/pro.3761. Epub 2019 Nov 7.
4
Benchmarking of different molecular docking methods for protein-peptide docking.不同分子对接方法在蛋白-肽对接中的基准测试。
BMC Bioinformatics. 2019 Feb 4;19(Suppl 13):426. doi: 10.1186/s12859-018-2449-y.
5
ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank.ccPDB 2.0:从蛋白质数据库创建和编译的数据集的更新版本。
Database (Oxford). 2019 Jan 1;2019:bay142. doi: 10.1093/database/bay142.
6
Protein ligand-specific binding residue predictions by an ensemble classifier.通过集成分类器预测蛋白质配体特异性结合残基
BMC Bioinformatics. 2016 Nov 17;17(1):470. doi: 10.1186/s12859-016-1348-3.
7
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8
A web server for analysis, comparison and prediction of protein ligand binding sites.一个用于分析、比较和预测蛋白质配体结合位点的网络服务器。
Biol Direct. 2016 Mar 25;11(1):14. doi: 10.1186/s13062-016-0118-5.
9
Predicting flavin and nicotinamide adenine dinucleotide-binding sites in proteins using the fragment transformation method.使用片段转换方法预测蛋白质中的黄素和烟酰胺腺嘌呤二核苷酸结合位点。
Biomed Res Int. 2015;2015:402536. doi: 10.1155/2015/402536. Epub 2015 Apr 27.
10
Prediction of membrane transport proteins and their substrate specificities using primary sequence information.利用一级序列信息预测膜转运蛋白及其底物特异性。
PLoS One. 2014 Jun 26;9(6):e100278. doi: 10.1371/journal.pone.0100278. eCollection 2014.
Bioinformatics. 2007 Jul 1;23(13):i347-53. doi: 10.1093/bioinformatics/btm174.
4
Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions.DNA结合位点的残基水平预测及其在DNA结合蛋白预测中的应用。
FEBS Lett. 2007 Mar 6;581(5):1058-66. doi: 10.1016/j.febslet.2007.01.086. Epub 2007 Feb 7.
5
Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.Cd-hit:一个用于对大量蛋白质或核苷酸序列进行聚类和比较的快速程序。
Bioinformatics. 2006 Jul 1;22(13):1658-9. doi: 10.1093/bioinformatics/btl158. Epub 2006 May 26.
6
Using evolutionary and structural information to predict DNA-binding sites on DNA-binding proteins.利用进化和结构信息预测DNA结合蛋白上的DNA结合位点。
Proteins. 2006 Jul 1;64(1):19-27. doi: 10.1002/prot.20977.
7
Prediction of coenzyme specificity in dehydrogenases/reductases. A hidden Markov model-based method and its application on complete genomes.脱氢酶/还原酶中辅酶特异性的预测。一种基于隐马尔可夫模型的方法及其在完整基因组上的应用。
FEBS J. 2006 Mar;273(6):1177-84. doi: 10.1111/j.1742-4658.2006.05153.x.
8
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