A systematic and efficient method to estimate the vibrational frequencies of linear peptide and protein ions with any amino acid sequence for the calculation of Rice-Ramsperger-Kassel-Marcus rate constant
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- A systematic and efficient method to estimate the vibrational frequencies of linear peptide and protein ions with any amino acid sequence for the calculation of Rice-Ramsperger-Kassel-Marcus rate constant
- Jeong Hee Moon; J Y Oh; M S Kim
- Bibliographic Citation
- Journal of American Society for Mass Spectrometry, vol. 17, no. 12, pp. 1749-1757
- Publication Year
- A systematic method to automatically estimate the vibrational frequency sets of linear peptide and protein ions with any amino acid sequence, which is needed in Rice-Ramsperger-Kassel-Marcus (RRKM) calculations for dissociation of these ions, has been developed. The method starts from the frequencies of free amino acids calculated quantum chemically at the DFT/B3LYP/6-31G** level. Some of these were systematically eliminated to get fictitious sets of frequencies for each amino acid at the C-terminus, N-terminus, and inside the chain. By collecting these sets as needed for a specified amino acid sequence and adding vibrations appearing upon peptide bond formation and protonation, a complete set of vibrational frequencies was obtained. Other conditions for RRKM calculations have also been systematically specified. RRKM calculations performed under various conditions have shown that the present method can be useful for an order of magnitude estimation of a statistical rate constant even at low internal energy region. The fact that arbitrariness involved in constructing an entire frequency set simply through spectral correlation can be avoided, and that any protein ion can be handled systematically and rapidly once its sequence and the number of protons attached are specified, are the main advantages of the present method.
- Amer Soc Mass Spectr
- Appears in Collections:
- Division of Bio Technology Innovation > Core Research Facility & Analysis Center > 1. Journal Articles
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