Automatic amide I frequency selection for rapid quantification of protein secondary structure from Fourier transform infrared spectra of proteins.
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.
Department of Biological Sciences, De Montfort University, Leicester, UK.
Joachim A
JA
Hering
Peter R
PR
Innocent
Parvez I
PI
Haris
D000465:Algorithms; D000577:Amides / Q000737:chemistry*; D000818:Animals; D002875:Chromosomes / Q000737:chemistry / Q000235:genetics; D018360:Crystallography, X-Ray; D016208:Databases, Factual; D006801:Humans; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D017550:Spectroscopy, Fourier Transform Infrared; D013223:Statistics as Topic / Q000379:methods
D000577:Amides; D011506:Proteins