Titanium dioxide nanoparticles preferentially bind in subdomains IB, IIA of HSA and minor groove of DNA.
Khursheed AliFaizan Abul QaisSourabh DwivediEslam M Abdel-SalamSabiha M AnsariQuaiser SaquibMohammad FaisalAbdulaziz A Al-KhedhairyMajed Al-ShaeriJaved MusarratPublished in: Journal of biomolecular structure & dynamics (2017)
Titanium dioxide nanoparticles (TiO2-NPs) interaction with human serum albumin (HSA) and DNA was studied by UV-visible spectroscopy, spectrofluorescence, circular dichroism (CD), and transmission electron microscopy (TEM) to analyze the binding parameters and protein corona formation. TEM revealed protein corona formation on TiO2-NPs surface due to adsorption of HSA. Intrinsic fluorescence quenching data suggested significant binding of TiO2-NPs (avg. size 14.0 nm) with HSA. The Stern-Volmer constant (Ksv) was determined to be 7.6 × 102 M-1 (r2 = 0.98), whereas the binding constant (Ka) and number of binding sites (n) were assessed to be 5.82 × 102 M-1 and 0.97, respectively. Synchronous fluorescence revealed an apparent decrease in fluorescence intensity with a red shift of 2 nm at Δλ = 15 nm and Δλ = 60 nm. UV-visible analysis also provided the binding constant values for TiO2-NPs-HSA and TiO2-NPs-DNA complexes as 2.8 × 102 M-1 and 5.4 × 103 M-1. The CD data demonstrated loss in α-helicity of HSA and transformation into β-sheet, suggesting structural alterations by TiO2-NPs. The docking analysis of TiO2-NPs with HSA revealed its preferential binding with aromatic and non-aromatic amino acids in subdomain IIA and IB hydrophobic cavity of HSA. Also, the TiO2-NPs docking revealed the selective binding with A-T bases in minor groove of DNA.
Keyphrases
- single molecule
- quantum dots
- visible light
- amino acid
- circulating tumor
- binding protein
- oxide nanoparticles
- photodynamic therapy
- protein protein
- cell free
- dna binding
- single cell
- energy transfer
- molecular dynamics
- magnetic resonance imaging
- aqueous solution
- magnetic resonance
- computed tomography
- nucleic acid
- deep learning