Enhancement Predicting Accuracy for Elastin-Like Polypeptides Temperature Transition by Back Propagation Neural Network

ISSN: 1875-5305 (Online)
ISSN: 0929-8665 (Print)


Volume 21, 12 Issues, 2014


Download PDF Flyer




Protein & Peptide Letters

Aims & ScopeAbstracted/Indexed in


Submit Abstracts Online Submit Manuscripts Online

Editor-in-Chief:
Prof. Ben M. Dunn
Department of Biochemistry and Molecular Biology
University of Florida
College of Medicine
P.O. Box 100245
Gainesville, FL
USA
Email: bdunn@ufl.edu

View Full Editorial Board

Subscribe Purchase Articles Order Reprints

Current: 1.735
5 - Year: 1.493

Enhancement Predicting Accuracy for Elastin-Like Polypeptides Temperature Transition by Back Propagation Neural Network

Author(s): Kai-Zong Huang, Xing-Kui Xiong, Chun-Mei Zhang, Yue-Yang Lai, Cheng-Nan Zou, Guang-Ya Zhang and Zi-Chun Hua

Affiliation: College of Life Sciences, Nanjing University, 22 Hankou Road, Nanjing 210093, Jiangsu, People’s Republic of China.

Abstract

Abstract: Elastin-like polypeptides (ELPs) have been widely used to promote the development of a variety of smart biomaterials. Transition temperature is a key attribute of ELPs central to ELPs researches. Therefore, it is essential to establish predictive models of transition temperature that are both computationally efficient and reliable by employing simple parameters. Back propagation neural network (BPNN), a powerful feed-forward algorithm designed to solve problems with overwhelming complexity, has been successfully used in non-linear predictor model. In this study, two pH-sensitive ELPs were expressed, purified and determined for temperature transition across a range of pH. The pH, concentration and molecular weight (MW) as well as isoelectric point (PI) and pseudo amino acid (PseAA) of these two ELPs were adopted as input parameters. Support vector machine (SVM) and back propagation neural network (BPNN) were performed respectively. We selected Lamda (λ) value by training set and evaluated predictor model by jackknife test that combined with Uniform Design (UD). According to the results of BPNN and SVM, whose mean absolute error (MAE) of BPNN model jackknife test were 4.80 and 4.95 respectively, the predictive ability of BPNN is a minor improvement over SVM. Applying Mackay’s data, MAE of BPNN jackknife test was 2.02, while the MAE between experimental and predicted transition temperature was 2.30 in Mackay’s predictor model. Compared with Mackay predictor model, the enhancement in the accuracy indicates that the proposed BPNN method could play a complementary role to predict ELPs transition temperature.

Keywords: BP neural network, elastin-like polypentapetides, support vector machines, transition temperature, Uniform Design.

Purchase Online Order Reprints Order Eprints Rights and Permissions

  
  



Article Details

Volume: 21
Issue Number: 10
First Page: 1065
Last Page: 1072
Page Count: 8
DOI: 10.2174/0929866521666140421113700
Advertisement

Related Journals




Webmaster Contact: urooj@benthamscience.org Copyright © 2014 Bentham Science