| Literature DB >> 30734871 |
Bamdad Riahi-Zanjani1, Mahdi Balali-Mood2, Zarrin Es'haghi3, Ahmad Asoodeh4, Adel Ghorani-Azam5.
Abstract
Antimicrobial peptides (AMPs) are best known for their bactericidal properties; however, due to their unique and flexible structures, they have also been proposed as potential selective sorbents for specific molecules. In the present study, we aimed to design and produce a new peptide-based microextraction fiber for preconcentrating morphine in urine samples. The binding of morphine to the peptide was first evaluated by computational simulation using the Molecular Operating Environment (MOE) 2015.10 software. A similar study was then performed using DS BIOVIA Materials Studio 2017 v17.1.0.48, which confirmed the results of the simulation carried out with MOE. Afterwards, those results were also confirmed by experimental research. In the experimental evaluation, carbon nanotubes (CNTs) were initially carboxylated with H2SO4/HNO3 (3:1) and then functionalized with the peptide. FTIR analysis, Raman measurements, and SEM imaging were used to confirm that CNT functionalization was successful as well as to check the nanostructure of the fiber. To evaluate the functionality of the fiber, it was inserted into a microtube containing a urine sample that included morphine and then sonicated for 5 min at 40 °C. Afterwards, the fiber was washed with methanol 20% (H2O/methanol) and the resulting sample was analyzed by HPLC. This procedure was repeated for different concentrations of morphine in the urine sample. The computational and experimental results showed that a morphine concentration as low as 0.25 ppb in urine could be adsorbed and detected using the peptide fiber. Therefore, given its semi-selective binding affinity for morphine, this peptide-based fiber can be considered a new approach to the detection of small amounts of morphine in biological samples.Entities:
Keywords: Antimicrobial peptides; Chemical structure; Computational simulation; Microextraction fiber; Molecular docking
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Year: 2019 PMID: 30734871 DOI: 10.1007/s00894-019-3925-7
Source DB: PubMed Journal: J Mol Model ISSN: 0948-5023 Impact factor: 1.810