The control of assembly and crystallization of molecules is becoming increasingly important in chemistry, engineering, and materials sciences. Crystallization is also central to understand natural processes that include the formation of atmospheric ice and biomineralization. Organic surfaces, biomolecules, and even liquid/vapor interfaces can promote the nucleation of crystals. These soft surfaces present significant structural fluctuations, which have been shown to strongly impact the rate of crystallization. This raises the question of whether degrees of freedom of soft surfaces play a role in the reaction coordinate for crystal nucleation. Here we use molecular simulations to investigate the mechanism of ice nucleation promoted by an alcohol monolayer. Our analysis indicates that while the flexibility of the surface strongly depresses its ice nucleation ability, it does not play a role in the coordinate that controls the transformation from liquid to ice. We find that the variable that drives the transformation is the size of the crystalline cluster, the same as that for the homogeneous crystallization. We argue that this is a general result that arises from the separation of time scales between surface fluctuations and the crossing of the transition state barrier for crystallization.

We employ a generalized variational principle to improve the stability, reliability, and precision of fully excited-state-specific complete active space self-consistent field theory. Compared to previous approaches that similarly seek to tailor this ansatz’s orbitals and configuration interaction expansion for an individual excited state, we find the present approach to be more resistant to root flipping and better at achieving tight convergence to an energy stationary point. Unlike state-averaging, this approach allows orbital shapes to be optimal for individual excited states, which is especially important for charge-transfer states and some doubly excited states. We demonstrate the convergence and state-targeting abilities of this method in LiH, ozone, and MgO, showing in the latter that it is capable of finding three excited-state energy stationary points that no previous method has been able to locate.

Peptides coordinated to iron-sulfur clusters, referred to as maquettes, represent a synthetic strategy for constructing biomimetic models of iron-sulfur metalloproteins. These maquettes have been successfully employed as building blocks of engineered heme-containing proteins with electron-transfer functionality; however, they have yet to be explored in reactivity studies. The concept of iron-sulfur nesting in peptides is a leading hypothesis in Origins-of-Life research as a plausible path to bridge the discontinuity between prebiotic chemical transformations and extant enzyme catalysis. Based on past biomimetic and biochemical research, we put forward a mechanism of maquette reconstitution that guides our development of computational tools and methodologies. In this study, we examined a key feature of the first stage of maquette formation, which is the secondary structure of aqueous peptide models using molecular dynamics simulations based on the AMBER99SB empirical force field. We compared and contrasted S…S distances, [2Fe-2S] and [4Fe-4S] nests, and peptide conformations via Ramachandran plots for dissolved Cys and Gly amino acids, the CGGCGGC 7-mer, and the GGCGGGCGGCGGW 16-mer peptide. Analytical tools were developed for following the evolution of secondary structural features related to [Fe-S] cluster nesting along 100 ns trajectories. Simulations demonstrated the omnipresence of peptide nests for preformed [2Fe-2S] clusters; however, [4Fe-4S] cluster nests were observed only for the 16-mer peptide with lifetimes of a few nanoseconds. The origin of the [4Fe-4S] nest and its stability was linked to a "kinked-ribbon" peptide conformation. Our computational approach lays the foundation for transitioning into subsequent stages of maquette reconstitution, those being the formation of iron ion/iron-sulfur coordinated peptides.

Cysteine is the only coded amino acid in biology that contains a thiol functional group. Deprotonated thiolate is essential for anchoring iron–sulfur ([Fe–S]) clusters, as prosthetic groups to the protein matrix. [Fe–S] metalloproteins and metalloenzymes are involved in biological electron transfer, radical chemistry, small molecule activation and signalling. These are key metabolic and regulatory processes that would likely have been present in the earliest organisms. In the context of emergence of life theories, the selection and evolution of the cysteine-specific R–CH 2 –SH side chain is a fascinating question to confront. We undertook a computational [4Fe–4S]-maquette modelling approach to evaluate how side chain length can influence [Fe–S] cluster binding and stability in short 7-mer and long 16-mer peptides, which contained either thioglycine, cysteine or homocysteine. Force field-based molecular dynamics simulations for [4Fe–4S] cluster nest formation were supplemented with density functional theory calculations of a ligand-exchange reaction between a preassembled cluster and the peptide. Secondary structure analysis revealed that peptides with cysteine are found with greater frequency nested to bind preformed [4Fe–4S] clusters. Additionally, the presence of the single methylene group in cysteine ligands mitigates the steric bulk, maintains the H-bonding and dipole network, and provides covalent Fe–S(thiolate) bonds that together create the optimal electronic and geometric structural conditions for [4Fe–4S] cluster binding compared to thioglycine or homocysteine ligands. Our theoretical work forms an experimentally testable hypothesis of the natural selection of cysteine through coordination chemistry.

We employ a generalized variational principle to improve the stability, reliability, and precision of fully excited-statespecific complete active space self-consistent field theory. Compared to previous approaches that similarly seek to tailor this ansatz's orbitals and configuration interaction expansion for an individual excited state, we find the present approach to be more resistant to root flipping and better at achieving tight convergence to an energy stationary point. Unlike stateaveraging, this approach allows orbital shapes to be optimal for individual excited states, which is especially important for charge transfer states and some doubly excited states. We demonstrate the convergence and state-targeting abilities of this method in LiH, ozone, and MgO, showing in the latter that it is capable of finding three excited state energy stationary points that no previous method has been able to locate.