RSC Advances
Structural biology articles published in the last
6 months
ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction
Predict SLC6 mutation clinical pathogenicity by calculating the amino acid descriptors in different ranges with rationalization analysis of the prediction.
RSC Adv., 2024,14, 13083-13094
https://doi.org/10.1039/D4RA00748D
Investigation of chain-length selection by the tenellin iterative highly-reducing polyketide synthase
Engineering the substrate-binding-helix of the keto-reductase domain of TENS controls chain-length selectivity of the products.
RSC Adv., 2024,14, 8963-8970
https://doi.org/10.1039/D3RA08463A
Analysing the effect caused by increasing the molecular volume in M1-AChR receptor agonists and antagonists: a structural and computational study
Two QSAR models which correlates the interaction energy and structural features of agonists and antagonists of M1-AChR.
RSC Adv., 2024,14, 8615-8640
https://doi.org/10.1039/D3RA07380G
Aqueous alkaline phosphate facilitates the non-exchangeable deuteration of peptides and proteins
The incorporation of deuterium into peptides and proteins holds broad applications across various fields, such as drug development and structural characterization.
RSC Adv., 2024,14, 8075-8080
https://doi.org/10.1039/D3RA08636D
Single molecule technique unveils the role of electrostatic interactions in ssDNA–gp32 molecular complex stability
A single-molecule electrophysiology assay was employed using an α-HL nanopore to test the stability of ssDNA and gp32 SSB–protein, in different salt conditions. Low salt and the presence of Zn2+ increases ssDNA–gp32 complex stability.
RSC Adv., 2024,14, 5449-5460
https://doi.org/10.1039/D3RA07746B
Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning
A deep learning approach centered on electron density is suggested for predicting the binding affility between proteins and ligands. The approach is thoroughly assessed using various pertinent benchmarks.
RSC Adv., 2024,14, 4492-4502
https://doi.org/10.1039/D3RA08650J
De novo designed aliphatic and aromatic peptides assemble into amyloid-like cytotoxic supramolecular nanofibrils
De novo designed aliphatic and aromatic peptides form biomimetic supramolecular nanofibrils illuminating the intricacies of the pathogenic amyloid assemblies.
RSC Adv., 2024,14, 4382-4388
https://doi.org/10.1039/D3RA07869H
Improving the thermostability and modulating the inulin profile of inulosucrase through rational glycine-to-proline substitution
MD simulations pinpointed temperature-sensitive glycine residues in LrInu. Substituting these glycines with proline significantly improved stability and levan yield.
RSC Adv., 2024,14, 2346-2353
https://doi.org/10.1039/D3RA06896J
Elucidating arsenic-bound proteins in the protein data bank: data mining and amino acid cross-validation through Raman spectroscopy
Decoding arsenic's impact: data mining protein structures in the protein data bank through amino acid mapping.
RSC Adv., 2023,13, 36261-36279
https://doi.org/10.1039/D3RA05987A
Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry
In this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.
RSC Adv., 2023,13, 35947-35963
https://doi.org/10.1039/D3RA06476J
Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein–ligand interactions in SARS-CoV-2 Mpro
Using SARS-CoV-2 Mpro as a case study, Wasserstein distance and dimension reduction are applied to the analysis of MD data of flexible complexes. The resulting embedding map correlates ligand-induced conformational differences and binding affinity.
RSC Adv., 2023,13, 34249-34261
https://doi.org/10.1039/D3RA06375E
Binding interactions of hydrophobically-modified flavonols with β-glucosidase: fluorescence spectroscopy and molecular modelling study
Binding interactions of fluorescent flavonols with the β-glucosidase enzyme are studied using fluorescence spectroscopy in combination with molecular docking and molecular dynamics simulations.
RSC Adv., 2023,13, 34107-34121
https://doi.org/10.1039/D3RA06276G
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