Homology modeling and molecular docking study of biogenic Muga silk nanoparticles as putative drug-binding system.
Prithvi C AsapurPurushottam D SahareSantosh Kumar MahapatraIndrani BanerjeePublished in: Biotechnology and applied biochemistry (2020)
The recent emergence of natural biopolymers as drug delivery vehicles is attributed to their biodegradability and less systemic toxicity. Muga silk nanoparticles were prepared using microwave radiolysis method and were characterized by Fourier transform infrared spectroscopy, circular dichroism, X-ray diffraction and transmission electron microscopy. To find the applicability in the drug delivery system of these nanoparticle and to know the binding site(s), a computational study was carried out. The structure of the Muga protein is predicted using homology modeling, which is further used for molecular docking. The in silico molecular docking between the Muga silk nanoparticles and three United States Food and Drug Administration-approved model drugs of doxorubicin, remdesivir and dexamethasone was performed. The binding capabilities and binding energy of the Muga silk proteins with these drugs are determined. The basic idea of the active site and the residues involved in the binding of the drugs/ligands is also studied. Doxorubicin showed the highest binding affinity of -8.7 kcal/mol and that of the remdesivir and dexamethasone are found to be -7.2 and -7.9 kcal/mol, respectively. Such high binding affinity(ies) would help for slow drug release kinetics and the other two drugs can be loaded when the requirement is for sustained drug release. The data were also validated using the UV-vis. spectroscopy.
Keyphrases
- molecular docking
- drug delivery
- drug release
- molecular dynamics simulations
- cancer therapy
- electron microscopy
- binding protein
- dna binding
- drug administration
- high resolution
- wound healing
- low dose
- oxidative stress
- drug induced
- tissue engineering
- magnetic resonance
- small molecule
- machine learning
- protein protein
- magnetic resonance imaging
- computed tomography
- single molecule
- artificial intelligence
- climate change
- transcription factor