Molecular simulation of self-assembly

Multiscale Molecular Dynamics Simulations of an Active Self-Assembling Material

Inspired by the adaptability observed in biological materials, self-assembly processes have attracted significant interest for their potential to yield novel materials with unique properties. However, experimental methods have often fallen short in capturing the molecular details of the assembly process. In this study, we employ a multiscale molecular dynamics simulation approach, complemented by NMR quantification, to investigate the mechanism of self-assembly in a redox-fueled bioinspired system. Contrary to conventional assumptions, we have uncovered a significant role played by the monomer precursor in the assembly process, with its presence varying with concentration and the extent of conversion of the monomer to the dimer. Experimental confirmation through NMR quantification underscores the concentration-dependent incorporation of monomers into the fibrous structures. Furthermore, our simulations also shed light on the diverse intermolecular interactions, including T-shaped and parallel π stacking, as well as hydrogen bonds, in stabilizing the aggregates. Overall, the open conformation of the dimer is preferred within these aggregates. However, inside the aggregates, the distribution of conformations shifts slightly to the closed conformation compared to on the surface. These findings contribute to the growing field of bioinspired materials science by providing valuable mechanistic and structural insights to guide the design and development of self-assembling materials with biomimetic functionalities.

Molecular mechanisms underlying nanowire formation in pristine phthalocyanine

Understanding the molecular processes of nanowire self-assembly is crucial for designing and controlling nanoscale structures that could lead to breakthroughs in functional materials. In this work, we focus on pristine phthalocyanines as a representative example of mesogenic supramolecular assemblies and have analyzed the formation of nanowires using classical molecular dynamics simulations. In the simulations, the molecules spontaneously form multi-columnar structures resembling supramolecular polymers that subsequently grow into more ordered aggregates. These self-assemblies are concentration dependent, leading to the formation of multi-columnar, dynamic aggregates at higher concentrations and nanowires at lower concentrations. The multi-columnar assemblies on a whole are more disordered than the nanowires, but have locally ordered domains of parallel facing molecules that can fluctuate while maintaining their overall shape. The nanowire formation at lower concentrations involves the initial interaction and clustering of randomly oriented phthalocyanine molecules, followed by the merging of small clusters into elongated segments and the eventual formation of a stable nanowire. We observe three main conformers in these self-assemblies, the parallel, T-shaped and edge-to-edge stacking of the phthalocyanine dimers. We calculate the underlying free energy landscape and show that the parallel conformers form the most stable configuration which is followed by the T-shaped and edge-to-edge dimer configurations. The findings provide insights into the mechanisms and pathways of nanowire formation and a step towards the understanding of self-assembly processes in supramolecular mesogens.

Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic

Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex data sets that capture the 3D spatial positions, dynamics, and interactions of thousands of molecules. Analyzing MD data sets is key for understanding and predicting emergent phenomena and in identifying key drivers and tuning design knobs of such phenomena. In this work, we show that the Euler characteristic (EC) provides an effective topological descriptor that facilitates MD analysis. The EC is a versatile, low-dimensional, and easy-to-interpret descriptor that can be used to reduce, analyze, and quantify complex data objects that are represented as graphs/networks, manifolds/functions, and point clouds. Specifically, we show that the EC is an informative descriptor that can be used for machine learning and data analysis tasks such as classification, visualization, and regression. We demonstrate the benefits of the proposed approach through case studies that aim to understand and predict the hydrophobicity of self-assembled monolayers and the reactivity of complex solvent environments.

Molecular modelling and computational studies of peptide diphenylalanine nanotubes, containing waters: structural and interactions analysis

The work is devoted to computer studies of the structural and physical properties of such self-organizing structures as peptide nanotubes (PNT) based on diphenylalanine (FF) dipeptide with different initial isomers of the left (L-FF) and right (D-FF) chiralities of these dipeptides. The structures under study are considered both with empty anhydrous and with internal cavities filled with water molecules. Molecular models of both chiralities are investigated using quantum-chemical DFT and semi-empirical methods, which are in consistent with the known experimental data. To study the effect of nano-sized clusters of water molecules embedded in the inner hydrophilic cavity on the properties of nanotubes (including the changes in their dipole moments and polarizations), as well as the changes in the structure and properties of water clusters themselves (their own dipole moments and polarizations), the surfaces of internal cavities of nanotubes and outer surfaces of water cluster structures for both types of chirality are analyzed. A specially developed method of visual differential analysis of structural features of (bio)macromolecular structures is applied for these studies. The results obtained of a number of physical properties (interacting energies, dipole moments, polarization values) are given for various cases and analyzed in comparison with the known data. These data are necessary for analyzing the interactions of water molecules with hydrophilic parts of nanotube molecules based on FF, such as COO- and NH3 + , since they determine many properties of the structures under study. The data obtained are useful for further analysis of the possible adhesion and capture of medical molecular components by active layers of FF-based PNT, which can be designed for creating capsules for targeted delivery of pharmaceuticals and drugs on their basis.

Predicting Spontaneous Orientational Self-Assembly: In Silico Design of Materials with Quantum Mechanically Derived Force Fields

De novo design of self-assembled materials hinges upon our ability to relate macroscopic properties to individual building blocks, thus characterizing in such supramolecular architectures a wide range of observables at varied time/length scales. This work demonstrates that quantum mechanical derived force fields (QMD-FFs) do satisfy this requisite and, most importantly, do so in a predictive manner. To this end, a specific FF, built solely based on the knowledge of the target molecular structure, is employed to reproduce the spontaneous transition to an ordered liquid crystal phase. The simulations deliver a multiscale portrait of such self-assembly processes, where conformational changes within the individual building blocks are intertwined with a 200 ns ensemble reorganization. The extensive characterization provided not only is in quantitative agreement with the experiment but also connects the time/length scales at which it was performed. Realizing QMD-FF predictive power and unmatched accuracy stands as an important leap forward for the bottom-up design of advanced supramolecular materials.

A Close Look at Molecular Self-Assembly with the Transmission Electron Microscope

Molecular self-assembly is pervasive in the formation of living and synthetic materials. Knowledge gained from research into the principles of molecular self-assembly drives innovation in the biological, chemical, and materials sciences. Self-assembly processes span a wide range of temporal and spatial domains and are often unintuitive and complex. Studying such complex processes requires an arsenal of analytical and computational tools. Within this arsenal, the transmission electron microscope stands out for its unique ability to visualize and quantify self-assembly structures and processes. This review describes the contribution that the transmission electron microscope has made to the field of molecular self-assembly. An emphasis is placed on which TEM methods are applicable to different structures and processes and how TEM can be used in combination with other experimental or computational methods. Finally, we provide an outlook on the current challenges to, and opportunities for, increasing the impact that the transmission electron microscope can have on molecular self-assembly.

Automated Parameterization of Quantum Mechanically Derived Force Fields for Soft Materials and Complex Fluids: Development and Validation

The reliability of molecular dynamics (MD) simulations in predicting macroscopic properties of complex fluids and soft materials, such as liquid crystals, colloidal suspensions, or polymers, relies on the accuracy of the adopted force field (FF). We present an automated protocol to derive specific and accurate FFs, fully based on ab initio quantum mechanical (QM) data. The integration of the Joyce and Picky procedures, recently proposed by our group to provide an accurate description of simple liquids, is here extended to larger molecules, capable of exhibiting more complex fluid phases. While the standard Joyce protocol is employed to parameterize the intramolecular FF term, a new automated procedure is here proposed to handle the computational cost of the QM calculations required for the parameterization of the intermolecular FF term. The latter is thus obtained by integrating the old Picky procedure with a fragmentation reconstruction method (FRM) that allows for a reliable, yet computationally feasible sampling of the intermolecular energy surface at the QM level. The whole FF parameterization protocol is tested on a benchmark liquid crystal, and the performances of the resulting quantum mechanically derived (QMD) FF were compared with those delivered by a general-purpose, transferable one, and by the third, "hybrid" FF, where only the bonded terms were refined against QM data. Lengthy atomistic MD simulations are carried out with each FF on extended 5CB systems in both isotropic and nematic phases, eventually validating the proposed protocol by comparing the resulting macroscopic properties with other computational models and with experiments. The QMD-FF yields the best performances, reproducing both phases in the correct range of temperatures and well describing their structure, dynamics, and thermodynamic properties, thus providing a clear protocol that may be explored to predict such properties on other complex fluids or soft materials.

Revealing Thermodynamics and Kinetics of Lipid Self-Assembly by Markov State Model Analysis

Self-assembly is ubiquitous in the realm of biology and has become an elegant bottom-up approach to fabricate new materials. Although molecular dynamics (MD) simulations can complement experiments by providing the missing atomic details, it still remains a grand challenge to reveal the thermodynamic and kinetic information on a self-assembly system. In this work, we demonstrate for the first time that the Markov state model analysis can be used to delineate the variation of free energy during the self-assembly process of a typical amphiphilic lipid dipalmitoyl-phosphatidylcholine (DPPC). Free energy profiles against the solvent-accessible surface area and the root-mean-square deviation have been derived from extensive MD results of more than five hundred trajectories, which identified a metastable crossing-cylinder (CC) state and a transition state of the distorted bilayer with a free energy barrier of ∼0.02 kJ mol^-1 per DPPC lipid, clarifying a long-standing speculation for 20 years that there exists a free energy barrier during lipid self-assembly. Our simulations also unearth two mesophase structures at the early stage of self-assembly, discovering two assembling pathways to the CC state that have never been reported before. Further thermodynamic analysis derives the contributions from the enthalpy and the entropy terms to the free energy, demonstrating the critical role played by the enthalpy-entropy compensation. Our strategy opens the door to quantitatively understand the self-assembly processes in general and provides new opportunities for identifying common thermodynamic and kinetic patterns in different self-assembly systems and inspiring new ideas for experiments. It may also contribute to the refinement of force field parameters of various self-assembly systems.

Molecular modeling and computational study of the chiral-dependent structures and properties of the self-assembling diphenylalanine peptide nanotubes, containing water molecules

DFT (VASP) and semi-empirical (HyperChem) calculations for the L- and D-chiral diphenylalanine (L-FF and D-FF) nanotube (PNT) structures, empty and filled with water/ice clusters, are presented and analyzed. The results obtained show that after optimization, the dipole moment and polarization of both chiral type L-FF and D-FF PNT and embedded water/ice cluster are enhanced; the water/ice cluster acquire the helix-like structure similar as L-FF and D-FF PNT. Ferroelectric properties of tubular water/ice helix-like-cluster obtained after optimization inside L-FF and D-FF PNT and total L-FF and D-FF PNT with embedded water/ice cluster are discussed.

In Vitro-In Silico Modeling Approach to Rationally Designed Simple and Versatile Drug Delivery Systems

Rational design and development of a nanosystem usually relies on empirical approaches as well as a fair degree of serendipity. Understanding how nanosystems behave at the molecular level is of great importance for potential biomedical applications. In this work, we describe a nanosystem composed of two natural compounds, vitamin E and sphingomyelin, prepared by spontaneous emulsification (vitamin E-sphingomyelin nanosystems (VSNs)). Extensive characterization revealed suitable physicochemical properties, very high biocompatibility in vitro and in vivo, and colloidal stability during storage and in biological media, all relevant properties for clinical translation. We have additionally pursued a computational approach to gain an improved understanding of the assembling, structure, dynamics, and drug-loading capacity of VSNs, using both small molecules and biomolecules (resveratrol, curcumin, gemcitabine, and two peptides). The spontaneous formation of compartmentalized VSNs starting from completely disassembled molecules, observed here for the first time, was accurately assessed from the computational molecular dynamics trajectories. We describe here a synergistic in silico/in vitro approach showing the predictive power of computational simulations for VSNs' structural characterization and description of internal interaction mechanisms responsible for the association of bioactive molecules, representing a paradigm shift in the rational design of nanotechnologies as drug delivery systems for advanced personalized medicine.

Computational Nanoscopy of Tight Junctions at the Blood-Brain Barrier Interface

The selectivity of the blood-brain barrier (BBB) is primarily maintained by tight junctions (TJs), which act as gatekeepers of the paracellular space by blocking blood-borne toxins, drugs, and pathogens from entering the brain. The BBB presents a significant challenge in designing neurotherapeutics, so a comprehensive understanding of the TJ architecture can aid in the design of novel therapeutics. Unraveling the intricacies of TJs with conventional experimental techniques alone is challenging, but recently developed computational tools can provide a valuable molecular-level understanding of TJ architecture. We employed the computational methods toolkit to investigate claudin-5, a highly expressed TJ protein at the BBB interface. Our approach started with the prediction of claudin-5 structure, evaluation of stable dimer conformations and nanoscale assemblies, followed by the impact of lipid environments, and posttranslational modifications on these claudin-5 assemblies. These led to the study of TJ pores and barriers and finally understanding of ion and small molecule transport through the TJs. Some of these in silico, molecular-level findings, will need to be corroborated by future experiments. The resulting understanding can be advantageous towards the eventual goal of drug delivery across the BBB. This review provides key insights gleaned from a series of state-of-the-art nanoscale simulations (or computational nanoscopy studies) performed on the TJ architecture.