Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations

Mendes, Maria; Cova, Tânia F.G.G.; Ramos, M. Luísa; Vitorino, Rui; Sousa, João; Pais, Alberto A. C. C.; Vitorino, Carla

doi:10.1016/j.molliq.2020.113774

Cited by 10 publications

(5 citation statements)

References 46 publications

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“…The optimized simulation protocols had been intensively tested 73,74 and extensively used. [75][76][77][78] Molecular visualizations were done using the Visual Molecular Dynamics (VMD) soware. 79 Translocation free energy.…”

Section: Methodsmentioning

confidence: 99%

In silico and in vitro design of cordycepin encapsulation in liposomes for colon cancer treatment

et al. 2021

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Section: Methodsmentioning

confidence: 99%

In silico and in vitro design of cordycepin encapsulation in liposomes for colon cancer treatment

et al. 2021

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“…However, under enzymatic hydrolysis conditions, neither the carboxyl group nor the amide group was cleaved. On the other hand, it can be seen from the terminal carbon signal of NMR spectra that the chemical shift of the terminal carbon of the monosaccharide on the sugar chain was around 107 ppm, and the chemical shift of the terminal carbon signal of the monosaccharide generally appeared around 100 ppm [ 36 , 37 ]. Under acid degradation conditions, no signal was found around 109 ppm, while the signal was more abundant around 100 ppm, while in the LMWHA–E spectra, a carbon signal appeared around 107 ppm, which was the signal of the terminal carbon of the unbroken disaccharide unit.…”

Section: Resultsmentioning

confidence: 99%

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning

Tian

Zang

et al. 2023

Molecules

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Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA–A and LMWHA–E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural differences between LMWHA–A and LMWHA–E, and then achieve a fast and accurate classification based on near-infrared (NIR) spectroscopy and machine learning. First, we combined nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlated NIR spectroscopy (2DCOS), and aquaphotomics to analyze the structural differences between LMWHA–A and LMWHA–E. Second, we compared the dimensionality reduction methods including principal component analysis (PCA), kernel PCA (KPCA), and t-distributed stochastic neighbor embedding (t-SNE). Finally, the differences in classification effect of traditional machine learning methods including partial least squares–discriminant analysis (PLS-DA), support vector classification (SVC), and random forest (RF) as well as deep learning methods including one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were compared. The results showed that genetic algorithm (GA)–SVC and RF were the best performers in traditional machine learning, but their highest accuracy in the test dataset was 90%, while the accuracy of 1D-CNN and LSTM models in the training dataset and test dataset classification was 100%. The results of this study show that compared with traditional machine learning, the deep learning models were better for the classification of LMWHA–A and LMWHA–E. Our research provides a new methodological reference for the rapid and accurate classification of biological macromolecules.

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“…In particular, in the range of 3.3-4.5 ppm, NMR signals of the H1-H5 and H1-H6' protons of glucuronic acid and acetylglucosamine were evident. At 1.98 ppm, the signal of methyl groups of acetylglucosamine were found [34,35]. The chemical shift and the signal width of the HA proton suggest the presence of HA on the liposome surface, which highlights that the coating process occurred properly and that HA was homogeneously distributed on the liposome surface.…”

Section: Nuclear Magnetic Resonance (Nmr)mentioning

confidence: 90%

Physicochemical Characterization of Hyaluronic Acid and Chitosan Liposome Coatings

et al. 2021

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Hyaluronic acid (HA) and chitosan (CH) are biopolymers that are widely used in many biomedical applications and for cosmetic purposes. Their chemical properties are fundamental to them working as drug delivery systems and improving their synergistic effects. In this work, two different protocols were used to obtain zwitterionic liposomes coated with either hyaluronic acid or chitosan. Specifically, the methodologies used to perform vesicle preparation were chosen by taking into account the specific chemical properties of these two polysaccharides. In the case of chitosan, liposomes were first synthesized and then coated, whereas the coating of hyaluronic acid was achieved through lipidic film hydration in an HA aqueous solution. The size and the zeta-potential of the polysaccharide-coated liposomes were determined by dynamic light scattering (DLS). This approach allowed coated liposomes to be obtained with hydrodynamic diameters of 264.4 ± 12.5 and 450.3 ± 16.7 nm for HA- and CH-coated liposomes, respectively. The chemical characterization of the coated liposomal systems was obtained by surface infrared (ATR-FTIR) and nuclear magnetic resonance (NMR) spectroscopies. In particular, the presence of polysaccharides was confirmed by the bands assigned to amides and saccharides being in the 1500–1700 cm−1 and 800–1100 cm−1 regions, respectively. This approach allowed confirmation of the efficiency of the coating processes, evidencing the presence of HA or CH at the liposomal surface. These data were also supported by time-of-flight secondary ion mass spectrometry (ToF-SIMS), which provided specific assessments of surface (3–5 nm deep) composition and structure of the polysaccharide-coated liposomes. In this work, the synthesis and the physical chemistry characterization of coated liposomes with HA or CH represent an important step in improving the pharmacological properties of drug delivery systems.

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Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations

Cited by 10 publications

References 46 publications

In silico and in vitro design of cordycepin encapsulation in liposomes for colon cancer treatment

In silico and in vitro design of cordycepin encapsulation in liposomes for colon cancer treatment

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning

Physicochemical Characterization of Hyaluronic Acid and Chitosan Liposome Coatings

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