(A-21) Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning

Single-chain nanoparticles (SCNPs) are intriguing materials inspired by proteins that consist of a single precursor polymer chain that has collapsed into a stable structure. In many prospective applications, such as biocatalysis and nanomedicine, the utility of a single-chain nanoparticle will intricately depend on the formation of a mostly specific structure or morphology. However, it is not generally well understood how to reliably control the morphology of single-chain nanoparticles. In this study, we use molecular simulation in tandem with machine learning analyses to assess how the patterning of cross-linking moieties on precursor chains impacts the morphology and properties of SCNPs. We adopt a coarse-grained, phenomenological model to study the formation of SCNPs from precursor polymer chains. To comprehensively probe the sequence–structure space, we simulate the formation of 7,680 SCNPs from precursors chains that are predominantly distinguished by the fraction and distribution, or blockiness, of reactive cross-linking groups along the backbone. These simulations are analyzed to characterize resulting SCNPs via structural analysis and unsupervised manifold learning. Specifically, the uniform manifold approximation and projection (UMAP) algorithm is used to learn a low-dimensional numerical embedding of the SCNP morphologies and the manifold over which they are distributed. These analyses reveal not only the landscape of possible SCNP morphologies but also the dispersity of structures arising from given precursor specifications, which has not been previously well-characterized. We show how the overall fraction of functionalization and blockiness of cross-linking moieties biases the formation of certain local and global morphological characteristics. Specific archetypal morphologies, including rod-like structures, were found to be strongly associated with certain regimes of different fractions of cross-linking and blockiness. Importantly, we illustrate and quantify the dispersity of morphologies that arise due to the stochastic nature of collapse from a well-defined sequence as well as from the ensemble of sequences that correspond to a given specification of precursor parameters. Moreover, we show that specific sequences enable more precise control over morphology. We note that morphological dispersity depends on sequence patterning and that a lower fraction of cross-linking groups and a high blockiness illustrate the widest range of possible morphology and dispersity outcomes. Overall, this work critically assesses how precursor chains might be feasibly tailored to achieve given SCNP morphologies and provides a platform to pursue future sequence-based design.