Exploiting Vector Pattern Diversity of Molecular Scaffolds for Cheminformatics Tasks in Drug Discovery

Chemical diversity is challenging to describe objectively. Despite this, various notions of chemical diversity are used throughout the medicinal chemistry optimization process in drug discovery. In this work, we show the usefulness of considering exploited vectors during different phases of the drug design process to provide a quantitative and objective description of chemical diversity. We have developed a concise and fast approach to enumerate and analyze the exploited vector patterns (EVPs) of molecular compound series, which can then be used in archetypal compound selection tasks, from hit matter identification to hit expansion and lead optimization. We first show that EVPs can be used to assess the progressibility of compounds in a fragment library design exercise. By considering EVPs, we then show how a set of compounds can be prioritized for hit expansion using EVP-based, customizable diversity sampling approaches, reducing the time taken and mitigating human biases. We also show that EVPs are a useful tool to analyze SAR data, offering the chance to uncover correlations between different vectors without predetermining the molecular scaffold structures. The codes used to perform these tasks are presented as easy-to-use Jupyter notebooks, which can be readily adapted for further related tasks.

Therapeutic Peptides Are Preferentially Solubilized in Specific Microenvironments within PEG-PLGA Polymer Nanoparticles

Polymeric nanoparticles are a highly promising drug delivery formulation. However, a lack of understanding of the molecular mechanisms that underlie their drug solubilization and controlled release capabilities has hindered the efficient clinical translation of such technologies. Polyethylene glycol-poly(lactic-co-glycolic) acid (PEG-PLGA) nanoparticles have been widely studied as cancer drug delivery vehicles. In this letter, we use unbiased coarse-grained molecular dynamics simulations to model the self-assembly of a PEG-PLGA nanoparticle and its solubulization of the anticancer peptide, EEK, with good agreement with previously reported experimental structural data. We applied unsupervised machine learning techniques to quantify the conformations that polymers adopt at various locations within the nanoparticle. We find that the local microenvironments formed by the various polymer conformations promote preferential EEK solubilization within specific regions of the NP. This demonstrates that these microenvironments are key in controlling drug storage locations within nanoparticles, supporting the rational design of nanoparticles for therapeutic applications.

Topology-controlled self-assembly of amphiphilic block copolymers

Contemporary synthetic chemistry approaches can be used to yield a range of distinct polymer topologies with precise control. The topology of a polymer strongly influences its self-assembly into complex nanostructures however a clear mechanistic understanding of the relationship between polymer topology and self-assembly has not yet been developed. In this work, we use atomistic molecular dynamics simulations to provide a nanoscale picture of the self-assembly of three poly(ethylene oxide)-poly(methyl acrylate) block copolymers with different topologies into micelles. We find that the topology affects the ability of the micelle to form a compact hydrophobic core, which directly affects its stability. Also, we apply unsupervised machine learning techniques to show that the topology of a polymer affects its ability to take a conformation in response to the local environment within the micelles. This work provides foundations for the rational design of polymer nanostructures based on their underlying topology.

Modular Software for Generating and Modeling Diverse Polymer Databases

Machine learning methods offer the opportunity to design new functional materials on an unprecedented scale; however, building the large, diverse databases of molecules on which to train such methods remains a daunting task. Automated computational chemistry modeling workflows are therefore becoming essential tools in this data-driven hunt for new materials with novel properties, since they offer a means by which to create and curate molecular databases without requiring significant levels of user input. This ensures that well-founded concerns regarding data provenance, reproducibility, and replicability are mitigated. We have developed a versatile and flexible software package, PySoftK (Python Soft Matter at King's College London) that provides flexible, automated computational workflows to create, model, and curate libraries of polymers with minimal user intervention. PySoftK is available as an efficient, fully tested, and easily installable Python package. Key features of the software include the wide range of different polymer topologies that can be automatically generated and its fully parallelized library generation tools. It is anticipated that PySoftK will support the generation, modeling, and curation of large polymer libraries to support functional materials discovery in the nanotechnology and biotechnology arenas.

Constraints on Knot Insertion, Not Internal Jamming, Control Polycatenane Translocation Dynamics through Crystalline Pores

The translocation of polymers through pores and channels is an archetypal process in biology and is widely studied and exploited for applications in bio- and nanotechnology. In recent times, the translocation of polymers of various different topologies has been studied both experimentally and by computer simulation. However, in some cases, a clear understanding of the precise mechanisms that drive their translocation dynamics can be challenging to derive. Experimental methods are able to provide statistical details of polymer translocation, but computer simulations are uniquely placed to uncover a finer level of mechanistic understanding. In this work, we use high-throughput molecular simulations to reveal the importance that knot insertion rates play in controlling translocation dynamics in the small pore limit, where unexpected nonpower law behavior emerges. This work both provides new predictive understanding of polycatenane translocation and shows the importance of carefully considering the role of the definition of translocation itself.

Conformational Heterogeneity and Interchain Percolation Revealed in an Amorphous Conjugated Polymer

Conjugated polymers are employed in a variety of application areas due to their bright fluorescence and strong biocompatibility. However, understanding the structure of amorphous conjugated polymers on the nanoscale is extremely challenging compared to their related crystalline phases. Using a bespoke classical force field, we study amorphous poly(9,9-di-n-octylfluorene-alt-benzothiadiazole) (F8BT) with molecular dynamics simulations to investigate the role that its nanoscale structure plays in controlling its emergent (and all-important) optical properties. Notably, we show that a giant percolating cluster exists within amorphous F8BT, which has ramifications in understanding the nature of interchain species that drive the quantum yield reduction and bathochromic shift observed in conjugated polymer-based devices and nanostructures. We also show that distinct conformations can be unravelled from within the disordered structure of amorphous F8BT using a two-stage machine learning protocol, highlighting a link between molecular conformation and ring stacking propensity. This work provides predictive understanding by which to enhance the optical properties of next-generation conjugated polymer-based devices and materials by rational, simulation-led design principles.

Interplay of lipid and surfactant: Impact on nanoparticle structure

Liquid lipid nanoparticles (LLN) are oil-in-water nanoemulsions of great interest in the delivery of hydrophobic drug molecules. They consist of a surfactant shell and a liquid lipid core. The small size of LLNs makes them difficult to study, yet a detailed understanding of their internal structure is vital in developing stable drug delivery vehicles (DDVs). Here, we implement machine learning techniques alongside small angle neutron scattering experiments and molecular dynamics simulations to provide critical insight into the conformations and distributions of the lipid and surfactant throughout the LLN. We simulate the assembly of a single LLN composed of the lipid, triolein (GTO), and the surfactant, Brij O10. Our work shows that the addition of surfactant is pivotal in the formation of a disordered lipid core; the even coverage of Brij O10 across the LLN shields the GTO from water and so the lipids adopt conformations that reduce crystallisation. We demonstrate the superior ability of unsupervised artificial neural networks in characterising the internal structure of DDVs, when compared to more conventional geometric methods. We have identified, clustered, classified and averaged the dominant conformations of lipid and surfactant molecules within the LLN, providing a multi-scale picture of the internal structure of LLNs.

Morphology of bile salts micelles and mixed micelles with lipolysis products, from scattering techniques and atomistic simulations

HYPOTHESES

Bile salts (BS) are biosurfactants released into the small intestine, which play key and contrasting roles in lipid digestion: they adsorb at interfaces and promote the adsorption of digestive enzymes onto fat droplets, while they also remove lipolysis products from that interface, solubilising them into mixed micelles. Small architectural variations on their chemical structure, specifically their bile acid moiety, are hypothesised to underlie these conflicting functionalities, which should be reflected in different aggregation and solubilisation behaviour.

EXPERIMENTS

The micellisation of two BS, sodium taurocholate (NaTC) and sodium taurodeoxycholate (NaTDC), which differ by one hydroxyl group on the bile acid moiety, was assessed by pyrene fluorescence spectroscopy, and the morphology of aggregates formed in the absence and presence of fatty acids (FA) and monoacylglycerols (MAG) - typical lipolysis products - was resolved by small-angle X-ray/neutron scattering (SAXS, SANS) and molecular dynamics simulations. The solubilisation by BS of triacylglycerol-incorporating liposomes - mimicking ingested lipids - was studied by neutron reflectometry and SANS.

FINDINGS

Our results demonstrate that BS micelles exhibit an ellipsoidal shape. NaTDC displays a lower critical micellar concentration and forms larger and more spherical aggregates than NaTC. Similar observations were made for BS micelles mixed with FA and MAG. Structural studies with liposomes show that the addition of BS induces their solubilisation into mixed micelles, with NaTDC displaying a higher solubilising capacity.

Time-Resolved Fluorescence Anisotropy of a Molecular Rotor Resolves Microscopic Viscosity Parameters in Complex Environments

Understanding viscosity in complex environments remains a largely unanswered question despite its importance in determining reaction rates in vivo. Here, time-resolved fluorescence anisotropy imaging (TR-FAIM) is combined with fluorescent molecular rotors (FMRs) to simultaneously determine two non-equivalent viscosity-related parameters in complex heterogeneous environments. The parameters, FMR rotational correlation time and lifetime, are extracted from fluorescence anisotropy decays, which in heterogeneous environments show dip-and-rise behavior due to multiple dye populations. Decays of this kind are found both in artificially constructed adiposomes and in live cell lipid droplet organelles. Molecular dynamics simulations are used to assign each population to nano-environments within the lipid systems. The less viscous population corresponds to the state showing an average 25° tilt to the lipid membrane normal, and the more viscous population to the state showing an average 55° tilt. This combined experimental and simulation approach enables a comprehensive description of the FMR probe behavior within viscous nano-environments in complex, biological systems.

Structure and Dynamics of Nanoconfined Water Between Surfactant Monolayers

The properties of nanoconfined water arise in direct response to the properties of the interfaces that confine it. A great deal of research has focused on understanding how and why the physical properties of confined water differ greatly from the bulk. In this work, we have used all-atom molecular dynamics (MD) simulations to provide a detailed description of the structural and dynamical properties of nanoconfined water between two monolayers consisting of an archetypal ionic surfactant, cetrimonium bromide (CTAB, [CH₃(CH₂)₁₅N(CH₃)₃]⁺Br^-). Small differences in the area per surfactant of the monolayers impart a clear effect on the intrinsic density, mobility, and ordering of the interfacial water layer confined by the monolayers. We find that as the area per surfactant within a monolayer decreases, the mobility of the interfacial water molecules decreases in response. As the monolayer packing density decreases, we find that each individual CTAB molecule has a greater effect on the ordering of water molecules in its first hydration shell. In a denser monolayer, we observe that the effect of individual CTAB molecules on the ordering of water molecules is hindered by increased competition between headgroups. Therefore, when two monolayers with different areas per surfactant are used to confine a nanoscale water layer, we observe the emergence of noncentrosymmetry.

On the Structure of Solid Lipid Nanoparticles

Solid lipid nanoparticles (SLNs) have a crystalline lipid core which is stabilized by interfacial surfactants. SLNs are considered favorable candidates for drug delivery vehicles since their ability to store and release organic molecules can be tailored through the identity of the lipids and surfactants used. When stored, polymorphic transitions in the core of drug-loaded SLNs lead to the premature release of drug molecules. Significant experimental studies have been conducted with the aim of investigating the physicochemical properties of SLNs, however, no molecular scale investigations have been reported on the behaviors that drive SLN formation and their polymorphic transitions. A combination of small angle neutron scattering and all-atom molecular dynamics simulations is therefore used to yield a detailed atomistic description of the internal structure of an SLN comprising triglyceride, tripalmitin, and the nonionic surfactant, Brij O10 (C_18:1 E₁₀ ). The molecular scale mechanisms by which the surfactants stabilize the crystalline structure of the SLN lipid core are uncovered. By comparing these results to simulated liquid and solid aggregates of tripalmitin lipids, how the morphology of the lipids vary between these systems is demonstrated providing further insight into the mechanisms that control drug encapsulation and release from SLNs.

On the interaction of hyaluronic acid with synovial fluid lipid membranes

All-atom molecular dynamics simulations have been used to investigate the adsorption of low molecular weight hyaluronic acid to lipid membranes. We have determined the interactions that govern the adsorption of three different molecular weight hyaluronic acid molecules (0.4, 3.8 & 15.2 kDa) to lipid bilayers that are representative of the surface-active phospholipid bilayers found in synovial joints. We have found that both direct hydrogen bonds and water-mediated interactions with the lipid headgroups play a key role in the binding of hyaluronic acid to the lipid bilayer. The water-mediated interactions become increasingly important in stabilising the adsorbed hyaluronic acid molecules as the molecular weight of hyaluronic acid increases. We also observe a redistribution of ions around bound hyaluronic acid molecules and the associated lipid headgroups, and that the degree of redistribution increases with the molecular weight of hyaluronic acid. By comparing this behaviour to that observed in simulations of the charge-neutral polysaccharide dextran (MW ∼ 15 kDa), we show that this charge redistribution leads to an increased alignment of the lipid headgroups with the membrane normal, and therefore to more direct and water-mediated interactions between hyaluronic acid and the lipid membrane. These findings provide a detailed understanding of the general structure of hyaluronic acid-lipid complexes that have recently been presented experimentally, as well as a potential mechanism for their enhanced tribological properties.