arXiv subjects

Peter Košovan

Publications and source records attributed to Peter Košovan.

4 recordsLinked to original sources

arXiv2026

Short Peptide Tails Modulate DNA Association and Condensation by PAMAM Dendrimers

Poly(amidoamine) (PAMAM) dendrimers are promising candidates for nucleic acid delivery; however, biocompatibility and transfection efficiency remain a challenge. Here, we investigated how the composition of short peptide tails conjugated to generation 2 PAMAM (G2) dendrimers influence DNA association and condensation across a range of pH values. Using a combination of potentiometric titrations, DNA precipitation assays, and coarse-grained molecular simulations with charge regulation, we show that the ionization of G2 dendrimers is strongly affected by both pH and proximity to DNA. Although charge regulation enhances dendrimer protonation and strengthens DNA association at low pH, DNA condensation by unmodified G2 remains largely insensitive to pH within the studied range. In contrast, conjugation of a single peptide tail introduces a pronounced pH dependence to DNA condensation. Histidine-containing conjugates exhibit the strongest response, with condensation efficiency decreasing markedly as the pH increases. Simulations reveal that the interaction strength between conjugates and DNA depends on both peptide composition and pH and that histidine-containing peptide tails become nearly neutral at physiological pH, contributing little to DNA binding. While single-conjugate simulations explain the trends in DNA association, they do not fully account for the observed condensation behavior, highlighting the importance of collective effects involving multiple conjugates. Overall, peptide conjugation transforms G2 PAMAM dendrimers from relatively pH-insensitive DNA condensing agents into pH-responsive DNA-binding systems. These findings provide molecular-level insight into the interplay between charge regulation, peptide composition, and DNA condensation.

cond-mat.soft↗

arXiv2024

The explicit bonding Reaction ensemble Monte Carlo method

We present the explicit bonding Reaction ensemble Monte Carlo (eb-RxMC) method, designed to sample reversible bonding reactions in macromolecular systems in thermodynamic equilibrium. Our eb-RxMC method is based on the Reaction ensemble method, however, its implementation differs from the latter by the representation of the reaction. In the eb-RxMC implementation, we are adding or deleting bonds between existing particles, instead of inserting or deleting particles with different chemical identities. This new implementation makes the eb-RxMC method suitable for simulating the formation of reversible linkages between macromolecules, which would not be feasible with the original implementation. To enable coupling of our eb-RxMC algorithm with Molecular Dynamics algorithm for sampling of the configuration space, we biased the sampling of reactions only within a certain inclusion radius. We validated our algorithm using a set of ideally behaving systems undergoing dimerization and polycondensation reactions, for which analytical results are available. For dimerization reactions with various equilibrium constants and initial compositions, the degree of conversion measured in our simulations perfectly matched the reference values given by the analytical equations. We also showed that this agreement is not affected by the arbitrary choice of the inclusion radius or the stiffness of the harmonic bond potential. Next, we showed that our simulations can correctly match the analytical results for the distribution of the degree of polymerization and end-to-end distance of ideal chains in polycondensation reactions. Altogether, we demonstrated that our eb-RxMC simulations correctly sample both reaction and configuration space of these reference systems, opening the door to future simulations of more complex interacting macromolecular systems.

cond-mat.soft↗

arXiv2024

pyMBE: the Python-based Molecule Builder for ESPResSo

We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software to design custom Coarse-Grained (CG) models, as well as pre-defined models of polyelectrolytes, peptides and globular proteins in the Extensible Simulation Package for Research on Soft Matter (ESPResSo). The Python interface of \espresso offers a flexible framework, capable of building custom CG models from scratch. As a downside, building CG models from scratch is error-prone, especially for newcomers in the field of CG modeling, or for molecules with complex architectures. The pyMBE module builds CG models in \espresso using a hierarchical bottom-up approach, providing a robust tool to automate the setup of CG models and helping new users prevent common mistakes. ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which have been designed to study chemical reaction equilibria in macromolecular systems with many reactive species. However, setting up these methods for systems which contain several types of reactive groups is an error-prone task, especially for beginners. The pyMBE module enables the automatic setup of cpH and G-RxMC simulations in \espresso, lowering the barrier for newcomers and opening the door to investigate complex systems not studied with these methods yet. To demonstrate some of the applications of pyMBE, we showcase several case studies where we successfully reproduce previously published simulations of charge-regulating peptides and globular proteins in bulk solution and weak polyelectrolytes in dialysis. The pyMBE module is publicly available as a GitHub repository (https://github.com/pyMBE-dev/pyMBE) which includes its source code and various sample and test scripts, including the ones that we used to generate the data presented in this article.

cond-mat.soft↗

arXiv2023

Explaining Giant Apparent $\mathrm{p}K_\mathrm{A}$ Shifts in Weak Polyelectrolyte Brushes

Recent experiments on weak polyelectrolyte brushes found marked shifts in the effective p$K_\mathrm{A}$ that are linear in the logarithm of the salt concentration. Comparing explicit-particle simulations with mean-field calculations we show that for high grafting densities the salt concentration effect can be explained using the ideal Donnan theory, but for low grafting densities the full shift is due to a combination of the Donnan effect and the polyelectrolyte effect. The latter originates from electrostatic correlations which are neglected in the Donnan picture and which are only approximately included in the mean-field theory. Moreover, we demonstrate that the magnitude of the polyelectrolyte effect is almost invariant with respect to salt concentration but depends on the grafting density of the brush. This invariance is due to a complex cancellation of multiple effects. Based on our results, we show how the experimentally determined p$K_\mathrm{A}$ shifts may be used to infer the grafting density of brushes, a parameter that is difficult to measure directly.

cond-mat.soft↗