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.
Hering
Joachim A
JA
Department of Biological Sciences, De Montfort University, Leicester, UK.
Innocent
Peter R
PR
Haris
Parvez I
PI
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