Sara Fortuna

Polymer vesicles with a colloidal armor of nanoparticles

The fabrication of polymer vesicles with a colloidal armor made from a variety of nanoparticles is demonstrated. In addition, it is shown that the armored supracolloidal structure can be postmodified through film-formation of soft polymer latex particles on the surface of the polymersome, hereby effectively wrapping the polymersome in a plastic bag, as well as through formation of a hydrogel by disintegrating an assembled polymer latex made from poly(ethyl acrylate-co-methacrylic acid) upon increasing the pH. Furthermore, ordering and packing patterns are briefly addressed with the aid of Monte Carlo simulations, including patterns observed when polymersomes are exposed to a binary mixture of colloids of different size.

Packing patterns of silica nanoparticles on surfaces of armored polystyrene latex particles.

Fascinating packing patterns of identical spherical and discotic objects on curved surfaces occur readily in nature and science. Examples include C(60) fullerenes, (1, 2) 13-atom cuboctahedral metal clusters, (3) and S-layer proteins on outer cell membranes. (4) Numerous situations with surface-arranged objects of variable size also exist, such as the lenses on insect eyes, biomineralized shells on coccolithophorids, (5) and solid-stabilized emulsion droplets (6) and bubbles. (7) The influence of size variations on these packing patterns, however, is studied sparsely. Here we investigate the packing of nanosized silica particles on the surface of polystyrene latex particles fabricated by Pickering miniemulsion polymerization of submicrometer-sized armored monomer droplets. We are able to rationalize the experimental morphology and the nearest-neighbor distribution with the help of Monte Carlo simulations. We show that broadening of the nanoparticle size distribution has pronounced effects on the self-assembled equilibrium packing structures, with original 12-point dislocations or grain-boundary scars gradually fading out.

Agent-based modeling for the 2D molecular self-organization of realistic molecules.

We extend our previously developed agent-based (AB) algorithm to the study of the self-assembly of a fully atomistic model of experimental interest. We study the 2D self-assembly of a rigid organic molecule (1,4-benzene-dicarboxylic acid or TPA), comparing the AB results with Monte Carlo (MC) and MC simulated annealing, a technique traditionally used to solve the global minimization problem. The AB algorithm gives a lower energy configuration in the same simulation time than both of the MC simulation techniques. We also show how the AB algorithm can be used as a part of the protocol to calculate the phase diagram with less computational effort than standard techniques.

Distance-Based Configurational Entropy of Proteins from Molecular Dynamics Simulations

Estimation of configurational entropy from molecular dynamics trajectories is a difficult task which is often performed using quasi-harmonic or histogram analysis. An entirely different approach, proposed recently, estimates local density distribution around each conformational sample by measuring the distance from its nearest neighbors. In this work we show this theoretically well grounded the method can be easily applied to estimate the entropy from conformational sampling. We consider a set of systems that are representative of important biomolecular processes. In particular: reference entropies for amino acids in unfolded proteins are obtained from a database of residues not participating in secondary structure elements; the conformational entropy of folding of β2-microglobulin is computed from molecular dynamics simulations using reference entropies for the unfolded state; backbone conformational entropy is computed from molecular dynamics simulations of four different states of the EPAC protein and compared with order parameters (often used as a measure of entropy); the conformational and rototranslational entropy of binding is computed from simulations of 20 tripeptides bound to the peptide binding protein OppA and of β2-microglobulin bound to a citrate coated gold surface. This work shows the potential of the method in the most representative biological processes involving proteins, and provides a valuable alternative, principally in the shown cases, where other approaches are problematic.

Hexagonal Lattice Model of the Patterns Formed by Hydrogen-Bonded Molecules on the Surface

We model the two-dimensional self-assembly of planar molecules capable of complementary interactions (like hydrogen bonding) as a set of hexagonal tiles on a hexagonal lattice. We use Monte Carlo simulations to study the phase diagrams of three model systems. The phases are characterized using a variety of order parameters, and they are studied as a function of the strength of the complementary interaction energy. This simplified model is proven to be capable of reproducing the phases encountered in real systems, unifying within the same framework most of the structures encountered experimentally.

Accurate Estimation of the Entropy of Rotation–Translation Probability Distributions

The estimation of rotational and translational entropies in the context of ligand binding has been the subject of long-time investigations. The high dimensionality (six) of the problem and the limited amount of sampling often prevent the required resolution to provide accurate estimates by the histogram method. Recently, the nearest-neighbor distance method has been applied to the problem, but the solutions provided either address rotation and translation separately, therefore lacking correlations, or use a heuristic approach. Here we address rotational-translational entropy estimation in the context of nearest-neighbor-based entropy estimation, solve the problem numerically, and provide an exact and an approximate method to estimate the full rotational-translational entropy.

In Silico Generation of Peptides by Replica Exchange Monte Carlo: Docking-Based Optimization of Maltose-Binding-Protein Ligands.

Short peptides can be designed in silico and synthesized through automated techniques, making them advantageous and versatile protein binders. A number of docking-based algorithms allow for a computational screening of peptides as binders. Here we developed ex-novo peptides targeting the maltose site of the Maltose Binding Protein, the prototypical system for the study of protein ligand recognition. We used a Monte Carlo based protocol, to computationally evolve a set of octapeptides starting from a polialanine sequence. We screened in silico the candidate peptides and characterized their binding abilities by surface plasmon resonance, fluorescence and electrospray ionization mass spectrometry assays. These experiments showed the designed binders to recognize their target with micromolar affinity. We finally discuss the obtained results in the light of further improvement in the ex-novo optimization of peptide based binders.

An artificial intelligence approach for modeling molecular self-assembly : agent-based simulations of rigid molecules

Agent-based simulations are rule-based models traditionally used for the simulations of complex systems. In this paper, an algorithm based on the concept of agent-based simulations is developed to predict the lowest energy packing of a set of identical rigid molecules. The agents are identified with rigid portions of the system under investigation, and they evolve following a set of rules designed to drive the system toward the lowest energy minimum. The algorithm is compared with a conventional Metropolis Monte Carlo algorithm, and it is applied on a large set of representative models of molecules. For all the systems studied, the agent-based method consistently finds a significantly lower energy minima than the Monte Carlo algorithm because the system evolution includes elements of adaptation (new configurations induce new types of moves) and learning (past successful choices are repeated).

Molecular dynamics simulations and docking enable to explore the biophysical factors controlling the yields of engineered nanobodies

Nanobodies (VHHs) have proved to be valuable substitutes of conventional antibodies for molecular recognition. Their small size represents a precious advantage for rational mutagenesis based on modelling. Here we address the problem of predicting how Camelidae nanobody sequences can tolerate mutations by developing a simulation protocol based on all-atom molecular dynamics and whole-molecule docking. The method was tested on two sets of nanobodies characterized experimentally for their biophysical features. One set contained point mutations introduced to humanize a wild type sequence, in the second the CDRs were swapped between single-domain frameworks with Camelidae and human hallmarks. The method resulted in accurate scoring approaches to predict experimental yields and enabled to identify the structural modifications induced by mutations. This work is a promising tool for the in silico development of single-domain antibodies and opens the opportunity to customize single functional domains of larger macromolecules.

Phase behaviour of self-assembled monolayers controlled by tuning physisorbed and chemisorbed states: A lattice-model view

The self-assembly of molecules on surfaces into 2D structures is important for the bottom-up fabrication of functional nanomaterials, and the self-assembled structure depends on the interplay between molecule-molecule interactions and molecule-surface interactions. Halogenated benzene derivatives on platinum have been shown to have two distinct adsorption states: a physisorbed state and a chemisorbed state, and the interplay between the two can be expected to have a profound effect on the self-assembly and phase behaviour of these systems. We developed a lattice model that explicitly includes both adsorption states, with representative interactions parameterised using density functional theory calculations. This model was used in Monte Carlo simulations to investigate pattern formation of hexahalogenated benzene molecules on the platinum surface. Molecules that prefer the physisorbed state were found to self-assemble with ease, depending on the interactions between physisorbed molecules. In contrast, molecules that preferentially chemisorb tend to get arrested in disordered phases. However, changing the interactions between chemisorbed and physisorbed molecules affects the phase behaviour. We propose functionalising molecules in order to tune their adsorption states, as an innovative way to control monolayer structure, leading to a promising avenue for directed assembly of novel 2D structures.