Christopher R. Iacovella

The stratum corneum (SC), the outermost layer of human skin, owes its barrier function to highly ordered lipid lamellae composed primarily of ceramides (CERs), cholesterol (CHOL), and free fatty acids (FFAs). Molecular dynamics simulations offer the opportunity to gain valuable insights into the structural organization of lipids, complementing experimental approaches. However, simulations using atomistic models are computationally expensive when studying the large, multilamellar structures characteristic of the SC. Coarse-grained (CG) models of SC lipids provide an efficient alternative but have largely been limited to CER NS. In this work, a previously developed Multistate Iterative Boltzmann Inversion (MS-IBI) CG model for CER NS has been extended to three additional CER subclassesCERs NP, AP, and ASwhich differ from CER NS in headgroup hydroxylation. By leveraging structural similarity and transferring nonbonded interaction parameters for CER NS hydroxyl groups, we have developed new models with minimal reparameterization. The models have been validated against atomistic simulations of both pure and mixed bilayers containing CHOL and FFA. To capture the multilamellar organization, six-leaflet multilayers were self-assembled. The resulting CG systems exhibited lamellar organization, chain order, and repeat distances consistent with the available experimental data. Comparisons across CER subclasses revealed that headgroup hydroxylation influences lipid packing, chain tilt, and whether the CER tails exhibit a hairpin or extended conformation. This work demonstrates the flexibility and transferability of the MS-IBI approach and provides CG models for key CER subclasses in human SC, enabling large-scale simulations of realistic SC lipid compositions beyond the reach of atomistic models.

Functionalized alkylsilane films exhibit switching interfacial behavior in response to different solvents. Image shows exposed oxygen (red) and carbon atoms (green). Hydroxyl films switch more favorably than carboxyl counterparts.

A generalized and extensible machine-learned molecular mechanics force field trained on over 1.1 million QC data applicable for drug discovery applications. Figure reproduced from the arXiv:201001196 preprint under the arXiv non-exclusive license.

The lipids located in the outermost layer of the skin, the stratum corneum (SC), play a crucial role in maintaining the skin barrier function. The primary components of the SC lipid matrix are ceramides (CERs), cholesterol (CHOL), and free fatty acids (FFAs). They form two crystalline lamellar phases: the long periodicity phase (LPP) and the short periodicity phase (SPP). In inflammatory skin conditions like atopic dermatitis and psoriasis, there are changes in the SC CER composition, such as an increased concentration of a sphingosine-based CER (CER NS) and a reduced concentration of a phytosphingosine-based CER (CER NP). In the present study, a lipid model was created exclusively forming the SPP, to examine whether alterations in the CER NS:CER NP molar ratio would affect the lipid organization. Experimental data were combined with molecular dynamics simulations of lipid models containing CER NS:CER NP at ratios of 1:2 (mimicking a healthy SC ratio) and 2:1 (observed in inflammatory skin diseases), mixed with CHOL and lignoceric acid as the FFA. The experimental findings show that the acyl chains of CER NS and CER NP and the FFA are in close proximity within the SPP unit cell, indicating that CER NS and CER NP adopt a linear conformation, similarly as observed for the LPP. Both the experiments and simulations indicate that the lamellar organization is the same for the two CER NS:CER NP ratios while the SPP NS:NP 1:2 model had a slightly denser hydrogen bonding network than the SPP NS:NP 2:1 model. The simulations show that this might be attributed to intermolecular hydrogen bonding with the additional hydroxide group on the headgroup of CER NP compared with CER NS.

Molecular dynamics simulations of mixtures of the ceramide N-(tetracosanoyl)-sphingosine (NS), cholesterol, and a free fatty acid are performed to gain a molecular-level understanding of the structure of the lipids found in the stratum corneum layer of skin. A new coarse-grained force field for cholesterol was developed using the multistate iterative Boltzmann inversion method (MS-IBI). The coarse-grained cholesterol force field is compatible with previously developed coarse-grained force fields for ceramide NS, free fatty acids, and water, and validated against atomistic simulations of these lipids using the CHARMM force field. Self-assembly simulations of multilayer structures using these coarse-grained force fields are performed, revealing that a large fraction of the ceramides adopt extended conformations, which cannot occur in the single bilayer in water structures typically studied using molecular simulation. Cholesterol fluidizes the membrane by promoting packing defects and an increase in cholesterol content is found to reduce the bilayer thickness, due to an increase in interdigitation of the C₂₄ lipid tails, consistent with experimental observations. Using a reverse-mapping procedure, a self-assembled coarse-grained multilayer system is used to construct an equivalent structure with atomistic resolution. Simulations of this atomistic structure are found to closely agree with experimentally derived neutron scattering length density profiles. Significant interlayer hydrogen bonding is observed in the inner layers of the atomistic multilayer structure that are not found in the outer layers in contact with water or in equivalent bilayer structures. This work highlights the importance of simulating multilayer structures, as compared to the more commonly studied bilayer systems, to enable more appropriate comparisons with multilayer experimental membranes. These results also provide validation of the efficacy of the coarse-grained force fields and the framework for multiscale simulation.

Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116 964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193 131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion.

In this work, molecular dynamics simulations are used to examine the self-assembly of anisotropically coated “patchy” nanoparticles. Specifically, we use a coarse-grained model to examine silica nanoparticles coated with alkane chains, where the poles of the grafted nanoparticle are bare, resulting in strongly attractive patches. Through a systematic screening process, the patchy nanoparticles are found to form dispersed, string-like, and aggregated phases, dependent on the combination of alkane chain length, coating chain density, and the fractional coated surface area. Correlation analysis is used to identify the ability of various particle descriptors to predict bulk phase behavior from more computationally efficient single grafted nanoparticle simulations and demonstrates that the solvent-accessible surface area of the nanoparticle core is a key predictor of bulk phase behavior. The results of this work enhance our knowledge of the phase space of patchy nanoparticles and provide a powerful approach for future screening of these materials.