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Spectroscopy
Volume 16, Issue 2, Pages 53-69
http://dx.doi.org/10.1155/2002/503989

An alternative method for rapid quantification of protein secondary structure from FTIR spectra using neural networks

Joachim A. Hering,1 Peter R. Innocent,2 and Parvez I. Haris1

1Department of Biological Sciences, De Montfort University, The Gateway, Leicester, LE1 9BH, UK
2Department of Computer Science, De Montfort University, The Gateway, Leicester, LE1 9BH, UK

Copyright © 2002 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Lack of reliable methods for accurate estimation of protein secondary structure from infrared spectra of proteins is a major barrier in its widespread use in protein secondary structure characterisation. Here we report a method for protein secondary structure estimation, from FTIR spectra of proteins, based on a multi‒layer feed‒forward neural network approach using an enhanced “resilient backpropagation” learning algorithm. The method utilises a database consisting of infrared spectra of 18 proteins, with known X‒ray structure, as the reference set. Our study revealed that providing the neural network analysis with only part of the amide I region from empirically determined structure sensitive regions in combination with appropriate pre‒processing of the spectral data produced the best overall results. This lead to a standard error of prediction (SEP) of 4.47% for α‒helix, an SEP of 6.16% for β‒sheet, and an SEP of 4.61% for turns. Compared to a previous factor analysis study by Lee et al., using the same set of 18 FTIR spectra of proteins, the error in prediction of α‒helix and β‒sheet was improved by 3.33% and 3.54% respectively, with minor increase for turns by 0.31%. Generally, our neural network analysis achieved comparable, in most cases even better prediction accuracy than most of the alternative pattern recognition based methods that were previously reported indicating the significant potential of this approach.