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Computational Modeling of the Interaction of Silver Clusters with Carbohydrates.

ABSTRACT: Silver nanoparticles are recognized for their numerous physical, biological, and pharmaceutical applications. In the present study, the interaction of silver clusters with monosaccharide molecules is examined to identify which molecule works better as a reducing agent in the application of a green synthesis approach. Geometry optimization of clusters containing one, three, and five silver atoms is performed along with the optimization of α-d-glucose, α-d-ribose, d-erythrose, and glyceraldehyde using density functional theory. Optimized geometries allow identifying the interaction formed in the silver cluster and monosaccharide complexes. An electron localization function analysis is performed to further analyze the interaction found and explain the reduction process in the formation of silver nanoparticles. The overall results indicate that glyceraldehyde presents the best characteristics to serve as the most efficient reducing agent.

SUBMITTER: Gallegos FE

PROVIDER: S-EPMC8851645 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Computational Modeling of the Interaction of Silver Clusters with Carbohydrates.

Gallegos Felipe E FE Meneses Lorena M LM Cuesta Sebastián A SA Santos Juan C JC Arias Josefa J Carrillo Pamela P Pilaquinga Fernanda F

ACS omega 20220204 6

Silver nanoparticles are recognized for their numerous physical, biological, and pharmaceutical applications. In the present study, the interaction of silver clusters with monosaccharide molecules is examined to identify which molecule works better as a reducing agent in the application of a green synthesis approach. Geometry optimization of clusters containing one, three, and five silver atoms is performed along with the optimization of α-d-glucose, α-d-ribose, d-erythrose, and glyceraldehyde u ...[more]

PMID: 35187295

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Project description:Microtubules are multistranded polymers in eukaryotic cells that support key cellular functions such as chromosome segregation, motor-based cargo transport, and maintenance of cell polarity. Microtubules self-assemble via "dynamic instability," in which the dynamic plus ends switch stochastically between alternating phases of polymerization and depolymerization. A key question in the field is what are the atomistic origins of this switching, i.e., what is different between the GTP- and GDP-tubulin states that enables microtubule growth and shortening, respectively? More generally, a major challenge in biology is how to connect theoretical frameworks across length- and timescales, from atoms to cellular behavior. In this study, we describe a multiscale model by linking atomistic molecular dynamics (MD), molecular Brownian dynamics (BD), and cellular-level thermokinetic modeling of microtubules. Here, we investigated the underlying interaction energy when tubulin dimers associate laterally by performing all-atom MD simulations. We found that the lateral potential energy is not significantly different among three nucleotide states of tubulin, GTP, GDP, and GMPCPP and is estimated to be ≅ -11 kBT. Furthermore, using MD potential energy in our BD simulations of tubulin dimers confirms that the lateral bond is weak on its own, with a mean lifetime of ∼0.1 μs, implying that the longitudinal bond is required for microtubule assembly. We conclude that nucleotide-dependent lateral-bond strength is not the key mediator microtubule dynamic instability, implying that GTP acts elsewhere to exert its stabilizing influence on microtubule polymer. Furthermore, the estimated lateral-bond strength (ΔGlat0≅ -5 kBT) is well-aligned with earlier estimates based on thermokinetic modeling and light microscopy measurements. Thus, we have computationally connected atomistic-level structural information, obtained by cryo-electron microscopy, to cellular-scale microtubule assembly dynamics using a combination of MD, BD, and thermokinetic models to bridge from Ångstroms to micrometers and from femtoseconds to minutes.

Dataset Information

Computational Modeling of the Interaction of Silver Clusters with Carbohydrates.

Publications

Computational Modeling of the Interaction of Silver Clusters with Carbohydrates.

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