Using artificially generated spectral data to improve protein secondary structure prediction from Fourier transform infrared spectra of proteins

Severcan M., Haris P., Severcan F.

ANALYTICAL BIOCHEMISTRY, vol.332, no.2, pp.238-244, 2004 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 332 Issue: 2
  • Publication Date: 2004
  • Doi Number: 10.1016/j.ab.2004.06.030
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.238-244
  • Keywords: protein, secondary structure prediction, neural networks, FTIR, spectroscopy, FTIR SPECTRA, RAPID QUANTIFICATION, NEURAL NETWORKS, AMIDE-I
  • Middle East Technical University Affiliated: No


Secondary structures of proteins have been predicted using neural networks from their Fourier transform infrared spectra. To improve the generalization ability of the neural networks, the training data set has been artificially increased by linear interpolation. The leave-one-out approach has been used to demonstrate the applicability of the method. Bayesian regularization has been used to train the neural networks and the predictions have been further improved by the maximum-likelihood estimation method. The networks have been tested and standard error of prediction (SEP) of 4.19% for alpha helix, 3.49% for beta sheet, and 3.15% for turns have been achieved. The results indicate that there is a significant decrease in the SEP for each type of structure parameter compared to previous works. (C) 2004 Elsevier Inc. All rights reserved.