• 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

iHyd-PseAAC（EPSV）：通过提取增强的位置和序列变异特征、周氏五步法则和广义伪氨基酸组成来识别蛋白质中的羟基化位点。

iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature Chou's 5-Step Rule and General Pseudo Amino Acid Composition.

作者信息

Ehsan Asma, Mahmood Muhammad K, Khan Yaser D, Barukab Omar M, Khan Sher A, Chou Kuo-Chen

机构信息

1Department of Mathematics, University of the Punjab, Lahore, Pakistan; 2Faculty of Information Technology, University of Management and Technology, Lahore, Pakistan; 3King Abdul Aziz University, Faculty of Computing and Information Technology in Rabigh, Jeddah, KSA; 4Gordon Life Science Institute, Boston, MA 02478, USA.

出版信息

Curr Genomics. 2019 Feb;20(2):124-133. doi: 10.2174/1389202920666190325162307.

DOI:10.2174/1389202920666190325162307
PMID:31555063
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6728902/
Abstract

BACKGROUND

In various biological processes and cell functions, Post Translational Modifications (PTMs) bear critical significance. Hydroxylation of proline residue is one kind of PTM, which occurs following protein synthesis. The experimental determination of hydroxyproline sites in an uncharacterized protein sequence requires extensive, time-consuming and expensive tests.

METHODS

With the torrential slide of protein sequences produced in the post-genomic age, certain remarkable computational strategies are desired to overwhelm the issue. Keeping in view the composition and sequence order effect within polypeptide chains, an innovative predictor a mathematical model is proposed.

RESULTS

Later, it was stringently verified using self-consistency, cross-validation and jackknife tests on benchmark datasets. It was established after a rigorous jackknife test that the new predictor values are superior to the values predicted by previous methodologies.

CONCLUSION

This new mathematical technique is the most appropriate and encouraging as compared with the existing models.

摘要

背景

在各种生物过程和细胞功能中，翻译后修饰（PTM）具有至关重要的意义。脯氨酸残基的羟基化是一种翻译后修饰，它在蛋白质合成后发生。在未表征的蛋白质序列中实验确定羟脯氨酸位点需要进行广泛、耗时且昂贵的测试。

方法

随着后基因组时代产生的蛋白质序列大量涌现，需要某些显著的计算策略来解决这个问题。考虑到多肽链内的组成和序列顺序效应，提出了一种创新的预测器——一种数学模型。

结果

随后，在基准数据集上使用自一致性、交叉验证和留一法测试对其进行了严格验证。经过严格的留一法测试后确定，新预测器的值优于先前方法预测的值。

结论

与现有模型相比，这种新的数学技术是最合适且令人鼓舞的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/6728902/d781b764cc45/CG-20-124_F5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/6728902/94598a5ab772/CG-20-124_F1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/6728902/e5e98f48e3c3/CG-20-124_F2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/6728902/825e509b0620/CG-20-124_F3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/6728902/45dd6d82afea/CG-20-124_F4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/6728902/d781b764cc45/CG-20-124_F5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/6728902/94598a5ab772/CG-20-124_F1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/6728902/e5e98f48e3c3/CG-20-124_F2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/6728902/825e509b0620/CG-20-124_F3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/6728902/45dd6d82afea/CG-20-124_F4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/6728902/d781b764cc45/CG-20-124_F5.jpg

相似文献

1
iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature Chou's 5-Step Rule and General Pseudo Amino Acid Composition.iHyd-PseAAC（EPSV）：通过提取增强的位置和序列变异特征、周氏五步法则和广义伪氨基酸组成来识别蛋白质中的羟基化位点。
Curr Genomics. 2019 Feb;20(2):124-133. doi: 10.2174/1389202920666190325162307.
2
iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition.iHyd-PseAAC：通过将二肽位置特异性倾向纳入伪氨基酸组成来预测蛋白质中的羟脯氨酸和羟赖氨酸
Int J Mol Sci. 2014 May 5;15(5):7594-610. doi: 10.3390/ijms15057594.
3
iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique.iHyd-LysSite（EPSV）：通过提取增强位置和序列变异特征技术，使用统计公式识别蛋白质中的羟赖氨酸位点。
Curr Genomics. 2020 Nov;21(7):536-545. doi: 10.2174/1389202921999200831142629.
4
iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC.iHyd-PseCp：通过将序列耦合效应纳入通用伪氨基酸组成来鉴定蛋白质中的羟脯氨酸和羟赖氨酸。
Oncotarget. 2016 Jul 12;7(28):44310-44321. doi: 10.18632/oncotarget.10027.
5
A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites.一种用于预测蛋白质羟基化位点的混合深度学习模型。
Int J Mol Sci. 2018 Sep 18;19(9):2817. doi: 10.3390/ijms19092817.
6
iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments Chou's 5-steps Rule and Pseudo Components.iSulfoTyr-PseAAC：通过结合统计矩、周氏五步法则和伪组分来识别酪氨酸硫酸化位点
Curr Genomics. 2019 May;20(4):306-320. doi: 10.2174/1389202920666190819091609.
7
SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins.SPrenylC-PseAAC：一种基于序列的模型，通过 Chou 的 5 步规则和广义 PseAAC 开发，用于识别蛋白质中的 S- prenylation 位点。
J Theor Biol. 2019 May 7;468:1-11. doi: 10.1016/j.jtbi.2019.02.007. Epub 2019 Feb 12.
8
iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC Chou's 5-steps Rule.iMethylK_pseAAC：通过将统计矩和位置相关特征纳入通用伪氨基酸组成的周氏五步法则来提高赖氨酸甲基化位点识别的准确性
Curr Genomics. 2019 May;20(4):275-292. doi: 10.2174/1389202920666190809095206.
9
iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition.iPhosH-PseAAC：根据周的五步法则和广义伪氨基酸组成，通过融合统计矩和位置相对特征来识别蛋白质中的磷酸组氨酸位点。
IEEE/ACM Trans Comput Biol Bioinform. 2021 Mar-Apr;18(2):596-610. doi: 10.1109/TCBB.2019.2919025. Epub 2021 Apr 6.
10
SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins.SPalmitoylC-PseAAC：一种基于序列的模型，通过 Chou 的 5 步规则和通用 PseAAC 开发，用于识别蛋白质中的 S-棕榈酰化位点。
Anal Biochem. 2019 Mar 1;568:14-23. doi: 10.1016/j.ab.2018.12.019. Epub 2018 Dec 26.

引用本文的文献

1
ESM-BBB-Pred: a fine-tuned ESM 2.0 and deep neural networks for the identification of blood-brain barrier peptides.ESM-BBB-Pred：一种用于识别血脑屏障肽的微调ESM 2.0和深度神经网络。
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbaf066.
2
BBB-PEP-prediction: improved computational model for identification of blood-brain barrier peptides using blending position relative composition specific features and ensemble modeling.血脑屏障肽预测：利用混合位置相对组成特异性特征和集成建模改进的血脑屏障肽识别计算模型。
J Cheminform. 2023 Nov 18;15(1):110. doi: 10.1186/s13321-023-00773-1.
3
IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach.

本文引用的文献

1
pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments.pSSbond-PseAAC：通过 PseAAC 和统计矩的集成预测二硫键结合位点。
J Theor Biol. 2019 Feb 21;463:47-55. doi: 10.1016/j.jtbi.2018.12.015. Epub 2018 Dec 12.
2
Metabolic Modeling of Microbial Community Interactions for Health, Environmental and Biotechnological Applications.用于健康、环境和生物技术应用的微生物群落相互作用的代谢建模
Curr Genomics. 2018 Dec;19(8):712-722. doi: 10.2174/1389202919666180911144055.
3
Bastion3: a two-layer ensemble predictor of type III secreted effectors.
IGPred-HDnet：基于图特征和层次深度学习的免疫球蛋白蛋白预测方法。
Comput Intell Neurosci. 2023 Jan 25;2023:2465414. doi: 10.1155/2023/2465414. eCollection 2023.
4
Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.基于深度学习的蛋白质翻译后修饰位点和蛋白质切割预测的进展。
Methods Mol Biol. 2022;2499:285-322. doi: 10.1007/978-1-0716-2317-6_15.
5
Evaluating machine learning methodologies for identification of cancer driver genes.评估用于识别癌症驱动基因的机器学习方法。
Sci Rep. 2021 Jun 10;11(1):12281. doi: 10.1038/s41598-021-91656-8.
6
iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique.iHyd-LysSite（EPSV）：通过提取增强位置和序列变异特征技术，使用统计公式识别蛋白质中的羟赖氨酸位点。
Curr Genomics. 2020 Nov;21(7):536-545. doi: 10.2174/1389202921999200831142629.
7
PPAI: a web server for predicting protein-aptamer interactions.PPAI：一个用于预测蛋白质-适体相互作用的网络服务器。
BMC Bioinformatics. 2020 Jun 9;21(1):236. doi: 10.1186/s12859-020-03574-7.
8
Some illuminating remarks on molecular genetics and genomics as well as drug development.关于分子遗传学和基因组学以及药物开发的一些有启发性的观点。
Mol Genet Genomics. 2020 Mar;295(2):261-274. doi: 10.1007/s00438-019-01634-z. Epub 2020 Jan 1.
9
HeteroDualNet: A Dual Convolutional Neural Network With Heterogeneous Layers for Drug-Disease Association Prediction Chou's Five-Step Rule.异构双网络：一种具有异构层的双卷积神经网络用于药物-疾病关联预测。周氏五步规则。
Front Pharmacol. 2019 Nov 8;10:1301. doi: 10.3389/fphar.2019.01301. eCollection 2019.
10
iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou's 5-step rule.iCrotoK-PseAAC：根据周的五步规则，通过混合位置相对统计特征来识别赖氨酸巴豆酰化位点。
PLoS One. 2019 Nov 21;14(11):e0223993. doi: 10.1371/journal.pone.0223993. eCollection 2019.
堡垒 3：III 型分泌效应物的双层集成预测器。
Bioinformatics. 2019 Jun 1;35(12):2017-2028. doi: 10.1093/bioinformatics/bty914.
4
iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC.iPPI-PseAAC(CGR)：通过将混沌游戏表示法纳入 PseAAC 来识别蛋白质-蛋白质相互作用。
J Theor Biol. 2019 Jan 7;460:195-203. doi: 10.1016/j.jtbi.2018.10.021. Epub 2018 Oct 9.
5
iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.iPhosY-PseAAC：通过将序列统计矩纳入伪氨基酸组成来识别磷酸酪氨酸位点。
Mol Biol Rep. 2018 Dec;45(6):2501-2509. doi: 10.1007/s11033-018-4417-z. Epub 2018 Oct 11.
6
pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset.pLoc_bal-mHum：通过 PseAAC 和准平衡训练数据集预测人类蛋白质的亚细胞定位。
Genomics. 2019 Dec;111(6):1274-1282. doi: 10.1016/j.ygeno.2018.08.007. Epub 2018 Sep 1.
7
pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC.pLoc_bal-mGpos：通过准平衡训练数据集和 PseAAC 预测革兰氏阳性菌蛋白质的亚细胞定位
Genomics. 2019 Jul;111(4):886-892. doi: 10.1016/j.ygeno.2018.05.017. Epub 2018 May 26.
8
iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC.iPhosT-PseAAC：通过将序列统计矩纳入伪氨基酸组成来识别磷酸苏氨酸位点。
Anal Biochem. 2018 Jun 1;550:109-116. doi: 10.1016/j.ab.2018.04.021. Epub 2018 Apr 25.
9
iDNA6mA-PseKNC: Identifying DNA N-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC.iDNA6mA-PseKNC：通过将核苷酸理化性质纳入 PseKNC 来鉴定 DNA N6-甲基腺苷位点。
Genomics. 2019 Jan;111(1):96-102. doi: 10.1016/j.ygeno.2018.01.005. Epub 2018 Jan 31.
10
A Novel Modeling in Mathematical Biology for Classification of Signal Peptides.一种用于信号肽分类的数学生物学新模型。
Sci Rep. 2018 Jan 18;8(1):1039. doi: 10.1038/s41598-018-19491-y.