Statistical method for predicting protein absorption peaks in terahertz region
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Statistical method for predicting protein absorption peaks in terahertz region
Nuclear Science and TechniquesVol. 24, Issue 3, Article number: 030201(2013)
Affiliations:
1.Institute of Modern Communication Technology, School of Physics and Electronics Engineering, Shanxi University, Taiyuan 030006, China
2.Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Jiading Campus, Shanghai 201800, China
3.Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
Yuting WU, Wenmei ZHANG, Hongwei ZHAO, et al. Statistical method for predicting protein absorption peaks in terahertz region. [J]. Nuclear Science and Techniques 24(3):030201(2013)
DOI:
Yuting WU, Wenmei ZHANG, Hongwei ZHAO, et al. Statistical method for predicting protein absorption peaks in terahertz region. [J]. Nuclear Science and Techniques 24(3):030201(2013) DOI: 10.13538/j.1001-8042/nst.2013.03.005.
Statistical method for predicting protein absorption peaks in terahertz region
Terahertz vibrational spectroscopy has recently been demonstrated as a novel noninvasive technique for the characterization of biological molecules. But the interpretation of the experimentally measured terahertz absorption bands requires robust computational method. In this paper, we present a statistical method for predicting the absorption peak positions of a macromolecule in the terahertz region. The essence of this method is to calculate the absorption spectra of a biological molecule based on multiple short scale molecular dynamics trajectories instead of using a long time scale trajectory. The method was employed to calculate the absorption peak positions of the protein, thioredoxin from Escherichia coli (,E.coli,), in the range of 10–25 cm,–1, to verify the reliability of this statistical method. The predicted absorption peak positions of thioredoxin show good correlation with measured results demonstrating that the proposed method is effective in terahertz absorption spectra modeling. Such approach can be applied to predict characteristic spectral features of biomolecules in the terahertz region.