基于序列与结构特征结合的蛋白质与DNA绑定位点预测

doi:10.3969/j.issn.1006-2475.2016.01.005

计算机与现代化

基于序列与结构特征结合的蛋白质与DNA绑定位点预测

南京理工大学计算机科学与工程学院,江苏南京210094

收稿日期:2015-10-19 出版日期:2016-01-22 发布日期:2016-01-26
作者简介:杨骥(1990-),男,安徽合肥人,南京理工大学计算机科学与工程学院硕士研究生,研究方向:模式识别与生物信息学。

Prediction of DNA-protein Binding Sites Based on Combining Sequence with Structure Information

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received:2015-10-19 Online:2016-01-22 Published:2016-01-26

摘要/Abstract

摘要： 目前国内外对于DNA-蛋白质绑定位点预测的研究大多集中在仅以蛋白质序列信息或仅以蛋白质结构信息为基础进行计算，而二者结合所实现的预测效果较差。本文提出一种在蛋白质位置特异性得分矩阵序列特征的基础上，结合蛋白质残基的溶剂可及表面积、相对表面积、深度和突出指数这几个结合效果良好的结构特征的DNA与蛋白质绑定位点预测方法，并使用随机下采样方法解决训练集样本不平衡问题，最后使用支持向量机算法进行预测。实验结果表明，本文方法具有较好的预测能力。


关键词: 位置特异性得分矩阵, 可及表面积, 相对表面积, 深度与突出指数, 随机下采样, 支持向量机

Abstract: Most of the research of DNA-protein binding sites are focusing on just computing protein sequence information or structure information, while the results are terrible if combing this two information, no matter what at home or abroad. To solve this problem, we combine protein structure information of accessible surface area, relative solvent accessibility, depth index and protrusion index with protein sequence information of position specific scoring matrix to predict DNA-Protein binding sites. Then we use under sampling to solve the unbalance problem of training dataset. Finally, we use support vector machine to make prediction. The result of experiment shows the method that we proposed can achieve better performance in prediction.


Key words: position specific scoring matrix, accessible surface area, relative solvent accessibility, depth index and protrusion index, under sampling, support vector machine

中图分类号:

TP181

杨骥. 基于序列与结构特征结合的蛋白质与DNA绑定位点预测[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2016.01.005.

YANG Ji. Prediction of DNA-protein Binding Sites Based on Combining Sequence with Structure Information[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.01.005.

参考文献

［1］ Ptashne M. Regulation of transcription: From lambda to eukaryotes［J］. Trends in Biochemical Sciences, 2005，30(6):275-279.
［2］ Ahmad S, Sarai A. PSSM-based prediction of DNA binding sites in proteins［J］. BMC Bioinformatics, 2005,6:33.
［3］ Hwang S, Gou Z, Kuznetsov I B. DP-Bind: A Web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins［J］. Bioinformatics, 2007,23(5):634-636.
［4］ Wang L, Huang C, Yang M Q. BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features［J］. BMC Systems Biology, 2010,4(Suppl1):S3.
［5］ Wang Liangjiang, Yang M Q, Yang J Y. Prediction of DNA-binding residues from protein sequence information using random forests［J］. BMC Genomics， 2009,10(Suppl 1):S1.
［6］ Yu Dong-jun, Hu Jun, Wu Xiao-Wei, et al. Learning protein multi-view features in complex space［J］. Amino Acids, 2013，44(5):1365-1379.
［7］ Lee B, Richards F M. The interpretation of protein structures: Estimation of static accessibility［J］. Journal of Molecular Biology, 1971,55(3):379-400,IN3-IN4.
［8］ Ahmad S, Gromiha M M, Sarai A. Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information［J］. Bioinformatics, 2004,20(4):477-486.
［9］ Meshkin A， Ghafuri H. Prediction of relative solvent accessibility by support vector regression and best-first method［J］. EXCLI Journal, 2010,9:29-38.
［10］Rost B, Sander C. Conservation and prediction of solvent accessibility in protein families［J］. Proteins, 1994,20(3):216-226.
［11］Pintar A, Carugo O, Pongor S C X. An algorithm that identifies protruding atoms in proteins［J］. Bioinformatics, 2002,18(7):980-984.
［12］Pintar A, Carugo O, Pongor S C X. DPX: For the analysis of the protein core［J］. Bioinformatics, 2003,19(2):313-314.
［13］Cortes C, Vapnik V. Support-vector networks［J］. Machine Learning, 1995,20(3):273-297.
［14］Li Tao, Li Qianzhong, Shuai Liu, et al. PreDNA: Accurate prediction of DNA-binding sites in proteins by integrating sequence and geometric structure information［J］. Bioinformatics, 2013,29(6):678-685.
［15］Laurikkala J. Improving identification of difficult small classes by balancing class distribution［C］// Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine. 2001:63-66.
［16］Weiss G M, Provost F. The effect of class distribution on classifier learning: An empirical study［EB/OL］. http://www.inf.ed.ac.uk/teaching/courses/dme/studpres/leegon.pdf, 2001-09-10.
［17］Estabrooks A, Jo T, Japkowicz N. A multiple resampling method for learning from imbalanced data sets［J］. Computational Intelligence, 2004,20(1):18-36.

[1]	杨博, 庄毅. 基于AOA-MSVM的控制集群故障检测方法[J]. 计算机与现代化, 2023, 0(12): 112-116.
[2]	杨孙哲, 孙爱珍. 基于HSV颜色与LBP纹理特征的水稻氮素营养诊断[J]. 计算机与现代化, 2023, 0(07): 86-92.
[3]	申志, 李元 . 基于KPCA和SSA优化SVM的非线性过程故障检测#br#[J]. 计算机与现代化, 2023, 0(06): 15-20.
[4]	尹建丰, 卫鑫, 顾雄伟, 黄凯, 魏敏捷. 基于图像阈值优化及改进SVM的电表数字识别[J]. 计算机与现代化, 2023, 0(05): 106-110.
[5]	赵延平, 王芳, 夏杨. 基于支持向量机的短文本分类方法[J]. 计算机与现代化, 2022, 0(02): 92-96.
[6]	赵聪慧, 冯庆胜. 基于DT-CWT和SVM的踏面旋转不变纹理提取算法[J]. 计算机与现代化, 2021, 0(12): 85-90.
[7]	陈翔, 邹庆年, 谢绍宇, 陈翠琼. 基于迁移学习策略的压板开关状态识别[J]. 计算机与现代化, 2021, 0(05): 120-126.
[8]	王文蔚, 肖军弼, 程鹏, 张悦. 基于SDN的DDoS攻击防御系统[J]. 计算机与现代化, 2021, 0(02): 117-121.
[9]	张文华，张志俊. 基于SVM的新能源公交车运营里程核查方法[J]. 计算机与现代化, 2020, 0(05): 39-.
[10]	郑沫利1,赵艳轲1,闫敏2,孙俊2,刘雍容1. 基于RDPSO-SVM的粮食产后储藏环节损耗智能评估方法[J]. 计算机与现代化, 2020, 0(03): 72-.
[11]	刘树艺，李静，胡春，王伟. 基于卷积神经网络与集成学习的交通标志识别[J]. 计算机与现代化, 2019, 0(12): 67-.
[12]	张开延1,2，潘杨3，娄季朝4. 基于ANP-SVM算法的智能变电站过程层网络故障分类[J]. 计算机与现代化, 2019, 0(07): 72-.
[13]	周国华,商俊燕. 一种抗噪的钢印打码字符识别方法[J]. 计算机与现代化, 2018, 0(12): 106-.
[14]	王芮1，韩锐2，贾玉祥1. 基于Spark的分布式大数据机器学习算法[J]. 计算机与现代化, 2018, 0(11): 119-.
[15]	高学金1,2,3,4,付龙晓1,2,3,4,武翠霞1,2,3,4,王普1,2,3,4. 基于ISOA的LS-SVM地铁站空调系统能耗预测模型[J]. 计算机与现代化, 2018, 0(10): 36-.

基于序列与结构特征结合的蛋白质与DNA绑定位点预测

Prediction of DNA-protein Binding Sites Based on Combining Sequence with Structure Information

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价