Dataset Information

A centrifugation-based physicochemical characterization method for the interaction between proteins and nanoparticles.

ABSTRACT: Nanomedicine requires in-depth knowledge of nanoparticle-protein interactions. These interactions are studied with methods limited to large or fluorescently labelled nanoparticles as they rely on scattering or fluorescence-correlation signals. Here, we have developed a method based on analytical ultracentrifugation (AUC) as an absorbance-based, label-free tool to determine dissociation constants (K_D), stoichiometry (N_max), and Hill coefficient (n), for the association of bovine serum albumin (BSA) with gold nanoparticles. Absorption at 520?nm in AUC renders the measurements insensitive to unbound and aggregated proteins. Measurements remain accurate and do not become more challenging for small (sub-10?nm) nanoparticles. In AUC, frictional ratio analysis allows for the qualitative assessment of the shape of the analyte. Data suggests that small-nanoparticles/protein complexes significantly deviate from a spherical shape even at maximum coverage. We believe that this method could become one of the established approaches for the characterization of the interaction of (small) nanoparticles with proteins.

SUBMITTER: Bekdemir A

PROVIDER: S-EPMC5080432 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A centrifugation-based physicochemical characterization method for the interaction between proteins and nanoparticles.

Bekdemir Ahmet A Stellacci Francesco F

Nature communications 20161020

Nanomedicine requires in-depth knowledge of nanoparticle-protein interactions. These interactions are studied with methods limited to large or fluorescently labelled nanoparticles as they rely on scattering or fluorescence-correlation signals. Here, we have developed a method based on analytical ultracentrifugation (AUC) as an absorbance-based, label-free tool to determine dissociation constants (K<sub>D</sub>), stoichiometry (N<sub>max</sub>), and Hill coefficient (n), for the association of bo ...[more]

PMID: 27762263

Similar Datasets

Project description:BackgroundIdentification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged.ResultsBy considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E. coli K-12.ConclusionsThe accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks.

Project description:Mayaro (MAYV), Saint Louis encephalitis (SLEV), and Oropouche (OROV) viruses are neglected members of the three main families of arboviruses with medical relevance that circulate in the Amazon region as etiological agents of outbreaks of febrile illnesses in humans. As enveloped viruses, MAYV, SLEV, and OROV largely depend on their class II fusion proteins (E1, E, and Gc, respectively) for entry into the host cell. Since many aspects of the structural biology of such proteins remain unclear, the present study aimed at physicochemically characterizing them by an in silico approach. The complete amino acid sequences of MAYV E1, SLEV E, and OROV Gc proteins derived by conceptual translation from annotated coding regions in the reference sequence genome of the respective viruses were obtained from the NCBI Protein database in the FASTA format and then submitted to the ClustalO, Protcalc, Pepstats, Predator, Proscan, PCprof, Phyre2, and 3Drefine web servers for the determination of sequence identities, the estimation of residual properties, the prediction of secondary structures, the identification of potential post-translational modifications, the recognition of antigenic propensities, and the modeling/refinement of three-dimensional structures. Sequence identities were 20.44%, 18.82%, and 13.70% between MAYV/SLEV, SLEV/OROV, and MAYV/OROV fusion proteins, respectively. As for the residual properties, MAYV E1 and SLEV E proteins showed a predominance of the non-polar profile (56% and 55% of the residues, respectively), whereas the OROV Gc protein showed a predominance of the polar profile (52% of the residues). Regarding predicted secondary structures, MAYV E1 and SLEV E proteins showed fewer alpha-helices (16.51% and 15.17%, respectively) than beta-sheets (21.79% and 25.15%, respectively), while the opposite was observed in the OROV Gc protein (20.39% alpha-helices and 12.14% beta-sheets). Regarding post-translational modifications, MAYV E1, SLEV E, and OROV Gc proteins showed greater relative potential for protein kinase C phosphorylation, N-myristoylation, and casein kinase II phosphorylation, respectively. Finally, antigenic propensities were higher in the N-terminus half than in the C-terminus half of these three proteins, whose three-dimensional structures revealed three distinctive domains. In conclusion, MAYV E1 and SLEV E proteins were found to share more physicochemical characteristics with each other than the OROV Gc protein, although they are all grouped under the same class of viral fusion proteins.

Project description:Curcumin is an extensively studied natural compound due to its extensive biological applications. However, there are some drawbacks linked to this compound such as poor absorption, low water-solubility, quick systemic elimination, fast metabolism, poor pharmacokinetics, low bioavailability, low penetration targeting efficacy and low stability. To overcome these drawbacks, curcumin is encapsulated in nano-carriers. In the current studies, we synthesized nanoparticles of curcumin without using nanocarriers by different methods such as nano-suspension (Cur-NSM), sonication (Cur-SM) and anti-solvent precipitation (Cur-ASP) to enhance the solubility of curcumin in water. The prepared nanoparticles were characterized by FTIR, SEM and XRD analysis. These curcumin nanoparticles were screened for their solubilities in water, DPPH scavenging, amylase, α-glucosidase and β-glucosidase enzymatic activities. The particle size of nano-curcumin was found to be in the 47.4-98.7 nm range. The reduction in particle size of curcumin dramatically increases its solubility in water to 79.2 μg mL-1, whereas the solubility of curcumin is just 0.98 μg mL-1. Cur-ASP showed the highest free radical scavenging potential (48.84 ± 0.98%) which was comparable with standard BHT (50.48 ± 1.11%) at 75.0 μg mL-1. As well, Cur-ASP showed the highest inhibition of α-amylase (68.67 ± 1.02%), α-glucosidase (58.30 ± 0.52%), and β-glucosidase (64.80 ± 0.43%) at 100 μg mL-1 which is comparable with standard drug acarbose. The greater surface area of nanoparticles exposes the various groups of curcumin for blocking the binding sites of enzymes. This strategy may be helpful in designing curcumin as a potent therapeutic agent against diabetes mellitus. Further, the molecular interactions of curcumin with α-amylase, α-glucosidase, β-glucosidase, and polyphenol oxidase were assessed by analyzing the plausible binding modes of curcumin in the binding pocket of each receptor. The best binding mode of curcumin was used to make complexes with the target proteins and their stability was confirmed by 50 ns MD simulation.

Dataset Information

A centrifugation-based physicochemical characterization method for the interaction between proteins and nanoparticles.

Publications

A centrifugation-based physicochemical characterization method for the interaction between proteins and nanoparticles.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets