IR Spectra of Different O2-Content Hemoglobin from Computational Study: Promising Detector of Hemoglobin Variant in Medical Diagnosis
- 66 Downloads
Abstract
IR spectra of heme and different O2-content hemoglobin were studied by the quantum computation method at the molecule level. IR spectra of heme and different O2-content hemoglobin were quantificationally characterized from 0 to 100 THz. The IR spectra of oxy-heme and de-oxy-heme are obviously different at the frequency regions of 9.08–9.48, 38.38–39.78, 50.46–50.82, and 89.04–91.00 THz. At 24.72 THz, there exists the absorption peak for oxy-heme, whereas there is not the absorption peak for de-oxy-heme. Whether the heme contains Fe–O–O bond or not has the great influence on its IR spectra and vibration intensities of functional groups in the mid-infrared area. The IR adsorption peak shape changes hardly for different O2-content hemoglobin. However, there exist three frequency regions corresponding to the large change of IR adsorption intensities for containing-O2 hemoglobin in comparison with de-oxy-hemoglobin, which are 11.08–15.93, 44.70–50.22, and 88.00–96.68 THz regions, respectively. The most differential values with IR intensity of different O2-content hemoglobin all exceed 1.0 × 104 L mol−1 cm−1. With the increase of oxygen content, the absorption peak appears in the high-frequency region for the containing-O2 hemoglobin in comparison with de-oxy-hemoglobin. The more the O2-content is, the greater the absorption peak is at the high-frequency region. The IR spectra of different O2-content hemoglobin are so obviously different in the mid-infrared region that it is very easy to distinguish the hemoglobin variant by means of IR spectra detector. IR spectra of hemoglobin from quantum computation can provide scientific basis and specific identification of hemoglobin variant resulting from different O2 contents in medical diagnosis.
Keywords
IR spectra Hemoglobin Different O2 contents Computational system biology Quantum calculationNotes
Acknowledgements
This work was supported by Cross laboratory incubation fund (Grant Number 2014S04).
References
- 1.Xie LJ, YAO Y, Ying YB (2014) The application of terahertz spectroscopy to protein detection: a review. Appl Spectrosc Rev 49:448–461CrossRefGoogle Scholar
- 2.Ding T, Middelberg APJ, Huber T, et al (2012) Far–infrared spectroscopy analysis of linear and cyclic peptides, and lysozyme. Vib Spectros 61:144–150CrossRefGoogle Scholar
- 3.Schmuttenmaer CA (2004) Exploring dynamics in the far-infrared with terahertz spectroscopy. Chem Rev 104:1759–1780CrossRefPubMedGoogle Scholar
- 4.Parrott EPJ, Sun YW, Pickwell–Macpherson E (2011) Terahertz spectroscopy: its future role in medical diagnoses. J Mol Struct 1006:66–76CrossRefGoogle Scholar
- 5.Xu J, Plaxco KW, Allen SJ (2006) Probing the collective vibrational dynamics of a protein in liquid water by terahertz absorption spectroscopy. Protein Sci 15:1175–1181CrossRefPubMedPubMedCentralGoogle Scholar
- 6.Chen JY, Knab JR, Cerne J et al (2005) Large oxidation dependence observed in terahertz dielectric response for cytochrome c. Phys Rev E 72(4):04090-1-04090-4CrossRefGoogle Scholar
- 7.Murakami H, Toyota Y, Nishi T et al (2012) Terahertz absorption spectroscopy of protein–containing reverse micellar solution. Chem Phys Lett 519–520:105–109CrossRefGoogle Scholar
- 8.Roberts GCK (ed.) (2013) Target and Hit Validation Techniques, Encyclopedia of Biophysics. European Biophysical Societies’ Association (EBSA), pp. 2573–2579. doi: 10.1007/978-3-642-16712-6
- 9.Anthea M, Hopkins J, McLaughlin CW et al (1993) Human Biology and Health. Prentice Hall, Englewood Cliffs (ISBN 0-13-981176–1)Google Scholar
- 10.LaManna JC (2007) In situ measurements of brain tissue hemoglobin saturation and blood volume by reflectance spectrophotometry in the visible spectrum. J Biomed Opt 12(6):062103CrossRefPubMedGoogle Scholar
- 11.Thomas A, Basham K (1993) Essentials of Oxygenation: Implication for Clinical Practice. Jones & Bartlett Learning, ISBN 0867203323Google Scholar
- 12.Pin S, Alpert B, Michalowicz A (1982) Oxygen bonding in human hemoglobin and its isolated subunits: a XANES study. FEBS Lett 147(1):106–110CrossRefPubMedGoogle Scholar
- 13.Madsen PL, Secher NH (1999) Near–infrared oximetry of the brain. Prog Neurobiol 58(6):541–560CrossRefPubMedGoogle Scholar
- 14.McCully KK, Hamaoka T (2000) Near–infrared spectroscopy: What can it tell us about oxygen saturation in skeletal muscle? Exerc Sport Sci Rev 28(3):123–127PubMedGoogle Scholar
- 15.Perrey SP (2008) Non–invasive NIR spectroscopy of human brain function during exercise. Methods 45(4):289–299CrossRefPubMedGoogle Scholar
- 16.Rolfe P (2000) Invivonear–Infrared spectroscopy. Annu Rev Biomed Eng 2:715–754CrossRefPubMedGoogle Scholar
- 17.Chen P, Fernald B, Lin W (2011) Estimation of regional hemoglobin concentration in biological tissues using diffuse reflectance spectroscopy with a novel spectral interpretation algorithm. Phys Med Biol 56:3985–4000CrossRefPubMedGoogle Scholar
- 18.Spiro TG, Soldatova AV, Balakrishnan G (2013) CO, NO and O2 as vibrational probes of heme protein interactions. Coord. Chem Rev 257:511–527Google Scholar
- 19.Ohtaa T, Liua JG, Naruta Y (2013) Resonance Raman characterization of mononuclear heme–peroxo intermediate models. Coord. Chem Rev 257:407–413Google Scholar
- 20.Hemoglobin (2009) School of Chemistry, Bristol University. UKGoogle Scholar
- 21.Mohamed AK, Walid G (2015) Comparative assessment of machine–learning scoring functions on PDB bind 2013. Eng Appl Artif Intel 45: 136–151CrossRefGoogle Scholar
- 22.Mohamed AK, Walid G, Walaa FA (2015) Machine learning in computational docking. Artif Intel Med 63: 135–152CrossRefGoogle Scholar
- 23.Mayo SL, Olafson BD, Goddard WA III (1990) DREIDING: A generic force field for molecular simulations. J Phys Chem 94:8897–8909CrossRefGoogle Scholar
- 24.Frisch MJ, Trucks GW, Schlegel HB et al (2009) Gaussian, Inc., Wallingford CTGoogle Scholar
- 25.Petibois C, Gionnet K, Gonçalves M et al (2006) Analytical performances of FT–IR spectrometry and imaging for concentration measurements within biological fluids, cells, and tissues. Analyst 131:640–647CrossRefPubMedGoogle Scholar
- 26.Zelig U, Kapelushnik J, Moreh R et al (2009) Diagnosis of cell death by means of infrared spectroscopy. Biophys J 97:2107–2114CrossRefPubMedPubMedCentralGoogle Scholar
- 27.Petibois C, Déléris G (2005) Evidence that erythrocytes are highly susceptible to exercise oxidative stress: FT–IR spectrometric studies at the molecular level. Cell Biol Int 29:709–716CrossRefPubMedGoogle Scholar
- 28.Bhargava R, Fernandez DC, Hewitt SM, et al (2006) High throughput assessment of cells and tissues: Bayesian classification of spectral metrics from infrared vibrational spectroscopic imaging data. Biochim Biophys Acta 1758: 830–845Google Scholar