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- W4308552989 abstract "Background: Reflecting on the difficulty of finding the evidence of latent fingerprints on wet and rough surfaces, scientists need to visualise those fingermarks without any background interference and stable adhesion of visualising material over the fingermark residues.Objective: To stabilize the interaction with the fingermarks, the synthesized nanoparticles were conjugated with a highly adhesive biopolymer, Mussel Adhesive Protein (MAP) which can effectively interact with fingerprint deposits.Material and Methods: Rare earth metal, europium oxide and nanoparticles were used as a visualisation material to get high contrast and reduced background interference-based fingerprints. To stabilise the interaction with the fingermarks, the synthesised nanoparticles were conjugated with highly adhesive biopolymer, Mussel Adhesive Protein (MAP) which can effectively interacts with fingerprint deposits. A molecular docking study was done using Auto-Dock to find the binding affinity between the metal nanoparticle and the protein. Further, the stability of the bioconjugated with fingerprint residues was analysed by protein-protein interaction study through patch dock and PDB Sum.Results: The docking analysis between europium and nanoparticles with MAP was found to be -8.77 kcal/mol and -47.49 kcal/mol respectively. Protein-protein interaction studies showed a highest affinity for dermcidin and keratin with a binding affinity of -16.76 kcal/mol and -24.76 kcal/mol respectively.Conclusions: The docking studies showed an efficient interaction between the synthesised molecules and the fingermark residues. Results of these interaction studies proved that this bio-conjugated complex can be explored for efficient visualisation of low intensified fingermarks." @default.
- W4308552989 created "2022-11-12" @default.
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- W4308552989 date "2022-11-15" @default.
- W4308552989 modified "2023-10-14" @default.
- W4308552989 title "Molecular docking study on europium nanoparticles and mussel adhesive protein for effective detection of latent fingerprints" @default.
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- W4308552989 doi "https://doi.org/10.1080/1354750x.2022.2145495" @default.
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