LogP Prediction for Blocked Tripeptides with Amino Acids Descriptors (HMLP) by Multiple Linear Regression and Support Vector Regression

被引：2
|
作者：
Yin, Jiajian [1 ]
机构：
[1] Sichuan Agr Univ, Coll Life & Sci, Yaan 625014, Peoples R China
来源：
2011 INTERNATIONAL CONFERENCE ON ENVIRONMENT SCIENCE AND BIOTECHNOLOGY (ICESB 2011) | 2011年 / 8卷
关键词：
HMLP parameters; peptides; logP; QSAR; cross validation; support vector regression; UNIONIZABLE SIDE-CHAINS; N-ACETYL-DIPEPTIDE; QUANTITATIVE-ANALYSES; HYDROPHOBICITY; SUBSTITUENT; PEPTIDES;
D O I：
10.1016/j.proenv.2011.10.028
中图分类号：
Q81 [生物工程学（生物技术）]; Q93 [微生物学];
学科分类号：
071005 ; 0836 ; 090102 ; 100705 ;
摘要：
The hydrophilicity/ lipophilicity of peptides are very important for rational design and drug discovery of bioactive peptides. In this study, each amino acid side chain was characterized by using three structure parameters (heuristic molecular lipophilicity potential, HMLP). Based on HMLP descriptors, prediction QSAR models of the logP were constructed for blocked tripeptides by multiple linear regression (MLR) and support vector regression (SVR). All the results showed that the logP relates to the total surface area(S) and hydrophilic indices (H), and the prediction results of SVR are better than that of MLR. The result shows HMLP parameters (S, L, H) could preferably describe the structure features of the peptides responsible for their octanol to water partition behavior. (c) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Asia-Pacific Chemical, Biological & Environmental Engineering Society (APCBEES)
引用
收藏
页码：173 / 178
页数：6
相关论文
共 50 条
  • [21] Local prediction of non-linear time series using support vector regression
    Lau, K. W.
    Wu, Q. H.
    PATTERN RECOGNITION, 2008, 41 (05) : 1539 - 1547
  • [22] Localized support vector regression for time series prediction
    Yang, Haiqin
    Huang, Kaizhu
    King, Irwin
    Lyu, Michael R.
    NEUROCOMPUTING, 2009, 72 (10-12) : 2659 - 2669
  • [23] A Longitudinal Support Vector Regression for Prediction of ALS Score
    Du, Wei
    Cheung, Huey
    Johnson, Calvin A.
    Goldberg, Ilya
    Thambisetty, Madhav
    Becker, Kevin
    PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, 2015, : 1586 - 1590
  • [24] Smart Growth Prediction Based on Support Vector Regression
    Li, Feiyang
    Chen, Wenjie
    Chen, Weijian
    Cai, Nian
    PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON MODELLING, SIMULATION AND APPLIED MATHEMATICS (MSAM2017), 2017, 132 : 152 - 155
  • [25] An optimized support vector regression for prediction of bearing degradation
    Zhang, Chenglong
    Ding, Shifei
    Sun, Yuting
    Zhang, Zichen
    APPLIED SOFT COMPUTING, 2021, 113
  • [26] Wind Speed Prediction Using Support Vector Regression
    Zhao, Pan
    Xia, Junrong
    Dai, Yiping
    He, Jiaxing
    ICIEA 2010: PROCEEDINGS OF THE 5TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOL 2, 2010, : 317 - +
  • [27] Traffic flow prediction using support vector regression
    Nidhi N.
    Lobiyal D.K.
    International Journal of Information Technology, 2022, 14 (2) : 619 - 626
  • [28] Stock Price Prediction Using Support Vector Regression
    Abirami, R.
    Vijaya, M. S.
    GLOBAL TRENDS IN COMPUTING AND COMMUNICATION SYSTEMS, PT 1, 2012, 269 : 588 - 597
  • [29] Prediction of components degradation using support vector regression with optimized parameters
    Yang, Chunzhen
    Liu, Jingquan
    Zeng, Yuyun
    Xie, Guangyao
    INTERNATIONAL YOUTH NUCLEAR CONGRESS 2016, IYNC2016, 2017, 127 : 284 - 290
  • [30] Comparing Support Vector Regression and Statistical Linear Regression for Predicting Poverty Incidence in Vietnam
    Senf, Cornelius
    Lakes, Tobia
    BRIDGING THE GEOGRAPHIC INFORMATION SCIENCES, 2012, : 251 - 265
12345