Predicting contact map using Radial Basis Function Neural Network with Conformational Energy Function

Peng Chen, De Shuang Huang, Xing Ming Zhao, Xueling Li

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Contact map, which is important to understand and reconstruct protein's three-dimensional (3D) structure, may be helpful to solve the protein's 3D structure. This paper presents a novel approach to predict the contact map using Radial Basis Function Neural Network (RBFNN) optimised by Conformational Energy Function (CEF) based on chemico-physical knowledge of amino acids. Finally, the results are trimmed by Short-Range Contact Function (SRCF). Consequently, it can be found that our proposed method is better than the existing methods such as PROFcon and the PE-based method. Particularly, this method can accurately predict 35% of contacts at a distance cutoff of 8 Å.

Original languageEnglish (US)
Pages (from-to)123-136
Number of pages14
JournalInternational Journal of Bioinformatics Research and Applications
Volume4
Issue number2
DOIs
StatePublished - May 2008

Keywords

  • Bioinformatics
  • CEF
  • Conformational energy function
  • Contact map
  • PCA
  • Principal component analysis
  • RBFNN
  • Radial basis function neural network
  • SRCF
  • Short-range contact function

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Clinical Biochemistry
  • Health Information Management

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