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Automatic amide I frequency selection for rapid quantification of protein secondary structure from Fourier transform infrared spectra of proteins
Author(s) -
Hering Joachim A.,
Innocent Peter R.,
Haris Parvez I.
Publication year - 2002
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/1615-9861(200207)2:7<839::aid-prot839>3.0.co;2-l
Subject(s) - protein secondary structure , fourier transform infrared spectroscopy , fourier transform , absorbance , amide , biological system , spectral line , chemistry , infrared spectroscopy , infrared , analytical chemistry (journal) , selection (genetic algorithm) , spectroscopy , protein structure , pattern recognition (psychology) , computer science , artificial intelligence , chromatography , mathematics , physics , optics , biology , organic chemistry , biochemistry , mathematical analysis , quantum mechanics , astronomy
Here we report the development of a new neural network based approach for rapid quantification of protein secondary structure from Fourier transform infrared (FTIR) spectra of proteins. A technique for efficiently reducing the amount of spectral data by almost 90% is suggested to facilitate faster neural network analysis. Additionally, an automatic procedure is introduced for selecting only those regions within the amide I band of protein FTIR spectra, which can be best related to secondary structure contents by subsequent neural network analysis. Based on a given reference set of FTIR spectra from proteins with known secondary structure, a subset of merely 29 out of 101 amide I absorbance values could be identified, which lead to an improved prediction accuracy. The average prediction accuracy achieved for helix, sheet, turn, bend, and other is 4.96% which is better than that achieved by alternative methods that have been previously reported indicating the significant potential of this approach. Our suggested automatic amide I frequency selection procedure may be easily extended to identify promising regions from spectral data recorded by other spectroscopic techniques, like for example circular dichroism spectroscopy.