Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles

The reshuffling mobility of molecular building blocks in self-assembled micelles is a key determinant of many their interesting properties, from emerging morphologies and surface compartmentalization, to dynamic reconfigurability and stimuli-responsiveness. However, the microscopic details of such complex structural dynamics are typically nontrivial to elucidate, especially in multicomponent assemblies. Here we show a machine-learning approach that allows us to reconstruct the structural and dynamic complexity of mono- and bicomponent surfactant micelles from high-dimensional data extracted from equilibrium molecular dynamics simulations. Unsupervised clustering of smooth overlap of atomic position (SOAP) data enables us to identify, in a set of multicomponent surfactant micelles, the dominant local molecular environments that emerge within them and to retrace their dynamics, in terms of exchange probabilities and transition pathways of the constituent building blocks. Tested on a variety of micelles differing in size and in the chemical nature of the constitutive self-assembling units, this approach effectively recognizes the molecular motifs populating them in an exquisitely agnostic and unsupervised way, and allows correlating them to their composition in terms of constitutive surfactant species

Non-Equilibrium Generation of Catalytic Supramolecular Polymers of Pre-RNA Nucleobases

Bioenergetics played critical roles for the chemical emergence of life where available energy resources drove the generation of primitive polymers and fueled early metabolism. Further, apart from information storage, the catalytic roles of primitive nucleic acid fragments have also been argued to be important for biopolymer evolution. Herein, we have demonstrated the non-equilibrium generation of catalytic supramolecular polymers of a possible proto-RNA building block (melamine) driven by a thermodynamically activated ester of low molecular weight. We utilized reversible covalent linkage to install a catalytic imidazole moiety in the polymer backbone. This resulted in energy dissipation via hydrolysis of the substrate predominantly from the assembled state and subsequent disassembly, thus installing kinetic asymmetry in the energy consumption cycle. Non-catalytic analogues led to kinetically stable polymers while inactivated substrates were unable to drive the polymerization. The non-equilibrium polymers of the pre-RNA bases were capable to spatiotemporally bind to a model cofactor. Notably, presence of an exogenous aromatic base augmented the stability of the polymers, reminiscent to what the molecular midwives did during early evolution. </p

Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles

The reshuffling mobility of molecular building blocks in self-assembled micelles is a determinant key of many interesting properties, from emerging morphologies and surface compartmentalizations to dynamic reconfigurability and stimuli-responsiveness of these supramolecular soft particles. However, such complex structural dynamics is typically non-trivial to be elucidated, especially for multi-component assemblies. Here we show a machine-learning approach that allows to reconstruct the structural and dynamic complexity of mono- and bi-component surfactant micelles from high-dimensional data extracted from equilibrium molecular dynamics simulations. Unsupervised clustering of smooth overlap of atomic position (SOAP) data enables to identify the main local molecular environments in a micelle, and to retrace their composition and dynamics, in terms of the exchange of surfactants among micelle clusters. Provided that there is sufficient difference between surfactants that are mixed in a multi-component micelle, this approach can effectively recognize diverse surfactant types even in an exquisitely agnostic, unsupervised way: solely based on their relative displacements and dynamic motions, and without prior information on the molecular species present in the system