Toward Chemotactic Supramolecular Nanoparticles: From Autonomous Surface Motion Following Specific Chemical Gradients to Multivalency-Controlled Disassembly

Abstract

Nature designs chemotactic supramolecular structures that can selectively bind specific groups present on surfaces, autonomously scan them moving along density gradients, and react once a critical concentration is encountered. Since such properties are key in many biological functions, these also offer inspirations for designing artificial systems capable of similar bioinspired autonomous behaviors. One approach is to use soft molecular units that self-assemble in an aqueous solution generating nanoparticles (NPs) that display specific chemical groups on their surface, enabling multivalent interactions with complementarily functionalized surfaces. However, a first challenge is to explore the behavior of these assemblies at sufficiently high-resolution to gain insights on the molecular factors controlling their behaviors. Here, by coupling coarse-grained molecular models and advanced simulation approaches, we show that it is possible to study the (autonomous or driven) motion of self-assembled NPs on a receptor-grafted surface at submolecular resolution. As an example, we focus on self-assembled NPs composed of facially amphiphilic oligomers. We observe how tuning the multivalent interactions between the NP and the surface allows to control of the NP binding, its diffusion along chemical surface gradients, and ultimately, the NP reactivity at determined surface group densities. In silico experiments provide physical–chemical insights on key molecular features in the self-assembling units which determine the dynamic behavior and fate of the NPs on the surface: from adhesion, to diffusion, and disassembly. This offers a privileged point of view into the chemotactic properties of supramolecular assemblies, improving our knowledge on how to design new types of materials with bioinspired autonomous behaviors.

Classifying soft self-assembled materials via unsupervised machine learning of defects

“…Such dynamical features determine a set of intriguing properties that, on the one hand are crucial for the functioning of biological tissues (e.g., self-healing, chemotacticity, molecular transport, etc.) 12 – 16 and, on the other hand, are promising features for the design of new functional materials and nano-technologies 1 , 17 – 23 .…”

“…However, analysing and interpreting the high-dimensional, high-detail data produced by computer simulations can be challenging, particularly for supramolecular systems. In such complex structures, irregularities and defects play a crucial role 6 , 8 , 10 , 11 , 16 , demanding for molecular descriptors capable to translate the simulation data into more reliable classifications of the molecular structure and dynamics characteristic of these systems.…”

Molecular communications in complex systems of dynamic supramolecular polymers

“…Coarse-grained MD (CG-MD) simulations have been also employed to study complex supramolecular polymer systems, reaching relevant space and time-scales to capture their dynamics 36 – 44 . In particular, combined with enhanced sampling methods, such models allow to study the molecular exchange between the supramolecular structures and their surroundings 22 , 32 , 45 – 48 , the exchange pathways 49 , and the response of these materials to external stimuli 50 , 51 . Such advanced computational approaches are mostly employed to study isolated assemblies in solution, whereas the dynamics of a system involving multiple assemblies (e.g., monomers/oligomers exchange between fibres, or fibres fragmentation and recombination) remains typically overlooked.…”

Classifying soft self-assembled materials via unsupervised machine learning of defects

“…11 Such dynamical features resulted in a set of intriguing properties that, on the one hand, mimic natural ones, crucial for the functioning of biological tissues (e.g., self-healing, chemotacticity, molecular transport, etc.) [12][13][14][15][16] and, on the other hand, are promising features for the design of new functional materials and nano-technologies 1,[17][18][19][20][21][22][23] A major goal in the study of self-assembled architectures is understanding how changes in the structure of the selfassembling building blocks (input) affect the overall properties of the supramolecular assembled structure (output) (Fig. 1).…”

“…6,7,11,[28][29][30][31][32] However, analysing and interpreting the high-dimensional, high-detail data produced by molecular simulations can be challenging, particularly in supramolecular systems. In such complex dynamical structures, irregularities and defects play a crucial role, 6,8,10,11,16 demanding for system descriptors capable to translate the simulation data into a reliable classification of the molecular structure and dynamics charactristic of these systems.…”