title: Beyond average protein secondary structure content prediction using FTIR spectroscopy.
abstract: This paper demonstrates that secondary structure information beyond purely protein secondary structure content can be predicted from FTIR (Fourier transform infrared spectroscopy) spectra of proteins with a high degree of accuracy. Both neural networks and adaptive neuro-fuzzy inference systems (ANFISs) were employed to predict helix/sheet segment information. The best results were achieved using ANFISs with fuzzy subtractive clustering based on normalised, compressed amide I data with an average SEP (standard error of prediction, root mean of squared errors) of 1.51. Predictions for average helix/sheet length based merely on the amide I band maximum position in combination with the full-width at half-height resulted in a comparable average SEP of 1.62. This suggests the importance of information on the position and width of the amide I band maximum for the prediction of helix/sheet segment information. Finally, the most promising pattern recognition approaches found in this study were applied to a protein with an as yet unknown x-ray structure: native a1-antichymotrypsin (a1-ACT).
authors.affiliation: Leicester School of Pharmacy, Faculty of Health and Life Sciences, De Montfort University, Leicester, UK.
authors.forename: Joachim A
authors.initials: JA
authors.lastname: Hering
authors.forename: Peter R
authors.initials: PR
authors.lastname: Innocent
authors.forename: Parvez I
authors.initials: PI
authors.lastname: Haris
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D017143:Fuzzy Logic; D010363:Pattern Recognition, Automated / Q000379:methods*; D017433:Protein Structure, Secondary; D011506:Proteins / Q000032:analysis* / Q000737:chemistry* / Q000145:classification; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*
chemical_list: D011506:Proteins