Marianne Seijo

Dielectric discontinuity effects on the adsorption of a linear polyelectrolyte at the surface of a neutral nanoparticle

The formation of complexes between nanoparticles and polyelectrolytes is a key process for the control of the reactivity of manufactured nanoparticles and rational design of core shell nanostructures. In this work, we investigate the influence of the nanoparticle dielectric constant on the adsorption of a linear charged polymer (polyelectrolyte) at the surface of a neutral nanoparticle. The polyelectrolyte linear charge density, as well as the image charges in the nanoparticle due to the dielectric discontinuity, is taken into account. Monte Carlo simulations are used to predict the adsorption/desorption limits and system properties. Effects of the nanoparticle size and polyelectrolyte length are also investigated. The polyelectrolyte is found adsorbed on the nanoparticle when the dielectric constant of the nanoparticle is greater than the dielectric constant of the medium. Attractive interactions induced by the presence of opposite sign image charges are found strong enough to adsorb the polyelectrolyte showing that the reaction field contribution has to be considered. The affinity between the polyelectrolyte and the nanoparticle is found to increase in magnitude by increasing the nanoparticle size and dielectric constant. The reaction field magnitude is also found to depend in a nonlinear way from the polyelectrolyte length.

Effects of surface site distribution and dielectric discontinuity on the charging behavior of nanoparticles. A grand canonical Monte Carlo study

The surface site distribution and the dielectric discontinuity effects on the charging process of a spherical nanoparticle (NP) have been investigated. It is well known that electrostatic repulsion between charges on neighbouring sites tends to decrease the effective charge of a NP. The situation is more complicated close to a dielectric breakdown, since here a charged site is not only interacting with its neighbours but also with its own image charge and the image charges of all its neighbours. Coexistence of opposite charges, titration sites positions, and pH dependence are systematically studied using a grand canonical Monte Carlo method. A Tanford and Kirkwood approach has been applied to describe the interaction potentials between explicit discrete ampholytic charging sites. Homogeneous, heterogeneous and patch site distributions were considered to reproduce the titration site distribution at the solid/solution interface of natural NPs. Results show that the charging process is controlled by the balance between Coulomb interactions and the reaction field through the solid-liquid interface. They also show that the site distribution plays a crucial role in the charging process. In patch distributions, charges accumulate at the perimeter of each patch due to finite size effects. When homogeneous and heterogeneous distributions are compared, three different charging regimes are obtained. In homogeneous and heterogeneous (with quite low polydispersity indexes) distributions, the effects of the NP dielectric constant on Coulomb interactions are counterbalanced by the reaction field and in this case, the dielectric breakdown has no significant effect on the charging process. This is not the case in patch distributions, where the dielectric breakdown plays a crucial role in the charging process.

Modeling the surface charge evolution of spherical nanoparticles by considering dielectric discontinuity effects at the solid/electrolyte solution interface

It is well known that the electrostatic repulsions between charges on neighboring sites decrease the effective charge at the surface of a charged nanoparticle (NP). However, the situation is more complex close to a dielectric discontinuity, since charged sites are interacting not only with their neighbors but also with their own image charges and the image charges of all neighbors. Titrating site positions, solution ionic concentration, dielectric discontinuity effects, and surface charge variations with pH are investigated here using a grand canonical Monte Carlo method. A Tanford and Kirkwood approach is used to calculate the interaction potentials between the discrete charged sites. Homogeneous, heterogeneous, and patch site distributions are considered to reproduce the various titrating site distributions at the solid/solution interface of spherical NPs. By considering Coulomb, salt, and image charges effects, results show that for different ionic concentrations, modifications of the dielectric constant of NPs having homogeneous and heterogeneous site distributions have little effect on their charging process. Thus, the reaction field, due to the presence of image charges, fully counterbalances the Coulomb interactions. This is not the case for patch distributions, where Coulomb interactions are not completely counterbalanced by the reaction field. Application of the present model to pyrogenic silica is also performed and comparison is made with published experimental data of titration curves at various ionic concentrations.

Knowledge of the Neighborhood of the Reactive Site up to Three Atoms Can Predict Biochemistry and Protein Sequences

Thousands of biochemical reactions with characterized activities are orphan, meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose a new method, BridgIT, to identify candidate genes and protein sequences for these reactions, and this method introduces, for the first time, information about the enzyme binding pocket into reaction similarity comparisons. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from KEGG 2011 that became non-orphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 out of 379 novel reactions in 2014 that were later catalogued in KEGG 2018, BridgIT predicted the exact or highly similar enzyme sequences. BridgIT requires knowledge about only three connecting bonds around the atoms of the reactive sites to correctly identify protein sequences for 93% of analyzed enzymatic reactions.Thousands of biochemical reactions with characterized biochemical activities are still orphan. Novel reactions predicted by pathway generation tools also lack associated protein sequences and genes. Mapping orphan and novel reactions back to the known biochemistry and proposing genes for their catalytic functions is a daunting problem. We propose a new method, BridgIT, to identify candidate genes and protein sequences for orphan and novel enzymatic reactions. BridgIT introduces, for the first time, the information of the enzyme binding pocket into reaction similarity comparisons. It ascertains the similarity of two reactions by comparing the reactive sites of their substrates and their surrounding structures, along with the structures of the generated products. BridgIT compares orphan and novel reactions to enzymatic reactions with known protein sequences, and then, it proposes protein sequences and genes of the most similar non-orphan reactions as candidates for catalyzing the novel or orphan reactions. We performed BridgIT analysis of orphan reactions from KEGG 2011 (Kyoto Encyclopedia of Genes and Genomes, published in 2011) that became non-orphan in KEGG 2016, and BridgIT correctly predicted enzymes with identical third- and fourth-level EC numbers for 91% and 56% of these reactions, respectively. BridgIT results revealed that it is sufficient to know information about six atoms together with their connecting bonds around the reactive sites of the substrates to match a protein sequence to the catalytic activity of enzymatic reactions with maximal accuracy. Moreover, the same information about only three atoms around the reactive site allowed us to correctly match 87% of the analyzed enzymatic reactions. Finally, we used BridgIT to provide candidate protein sequences for 137000 novel enzymatic reactions from the recently introduced ATLAS of Biochemistry. A web-tool of BridgIT can be consulted at http://lcsb-databases.epfl.ch/BridgIT/. AUTHORS SUMMARY The recent advances in pathway generation tools have resulted in a wealth of de novo hypothetical enzymatic reactions, which lack knowledge of the protein-encoding genes associated with their functionality. Moreover, nearly half of known metabolic enzymes are orphan, i.e., they also lack an associated gene or protein sequence. Proposing genes for catalytic functions of de novo and orphan reactions is critical for their utility in various applications ranging from biotechnology to medicine. In this work, we propose a novel computational method that will bridge the knowledge gap and provide candidate genes for both de novo and orphan reactions. We demonstrate that information about a small chemical structure around the reactive sites of substrates is sufficient to correctly assign genes to the functionality of enzymatic reactions.