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When driven out of equilibrium, many diverse systems can form complex spatial and dynamical patterns, even in the absence of attractive interactions., When driven out of equilibrium, many diverse systems can form complex spatial and dynamical patterns, even in the absence of attractive interactions. Using kinetic Monte Carlo simulations, we investigate the phase behavior of a binary system of particles of dissimilar size confined between semiflexible planar surfaces, in which the nanoconfinement introduces a non-local coupling between particles, which we model as an activation energy barrier to diffusion that decreases with the local fraction of the larger particle. The system autonomously reaches a cyclical non-equilibrium state characterized by the formation and dissolution of metastable micelle-like clusters with the small particles in the core and the large ones in the surrounding corona. The power spectrum of the fluctuations in the aggregation number exhibits 1/f noise reminiscent of self-organized critical systems. We suggest that the dynamical metastability of the micellar structures arises from an inversion of the energy landscape, in which the relaxation dynamics of one of the species induces a metastable phase for the other species.

Characterization of proteins with intrinsic or unfolded state disorder comprises a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. We introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, that calculates the maximum log-likelihood of a protein structural ensemble by accounting for the uncertainties of a wide range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects, paramagnetic relaxation enhancements, residual dipolar couplings, and hydrodynamic radii, single molecule fluorescence F\"orster resonance energy transfer efficiencies and small angle X-ray scattering intensity curves. We apply X-EISD to the drkN SH3 unfolded state domain and show that certain experimental data types are more influential than others for both eliminating structural ensemble models, while also finding equally probable disordered ensembles that have alternative structural properties that will stimulate further experiments to discriminate between them.

Accurate prediction of NMR chemical shifts can in principle help refine aqueous solution structure of proteins to the quality of X-ray structures. We report a new machine learning algorithm for protein chemical shift prediction that outperforms existing chemical shift calculators on realistic NMR solution data. Our UCBShift predictor implements two modules: a transfer prediction module that employs both sequence and structural alignment to select reference candidates for experimental chemical shift replication, and a redesigned machine learning module based on random forest regression which utilizes more, and more carefully curated, feature extracted data. When combined together, this new predictor achieves state of the art accuracy for predicting chemical shifts on a "real-world" dataset, with root-mean-square errors of 0.31 ppm for amide hydrogens, 0.19 ppm for Halpha, 0.87 ppm for C, 0.81 ppm for Calpha, 1.01 ppm for Cbeta, and 1.83 ppm for N, exceeding current prediction accuracy of popular chemical shift predictors such as SPARTA+ and SHIFTX2.

Nanoconfined aqueous environments and the recent advent of accelerated chemistry in microdroplets are increasingly being investigated for catalysis. The mechanisms underlying the enhanced reactivity in alternate solvent environments, and whether the enhanced reactivity due to nanoconfinement is a universal phenomenon, are not fully understood. Here, we use ab initio molecular dynamics simulations to characterize the free energy of a retro-Diels-Alder reaction in bulk water at very different densities and in water nanoconfined by parallel graphene sheets. We find that the broadly different global solvation environments accelerate the reactions to a similar degree with respect to the gas phase reaction, with activation free energies that do not differ by more than kbT from each other. The reason for the same acceleration factor in the extremely different solvation environments is that it is the microsolvation of the dienophile's carbonyl group that governs the transition state stabilization and mechanism, which is not significantly disrupted by either the lower density in bulk water or the strong nanoconfinement conditions used here. Our results also suggest that significant acceleration of Diels Alder reactions in microdroplets or on-water conditions can't arise from local microsolvation when water is present, but instead must come from highly altered reaction environments that drastically change the reaction mechanisms.

To study intermolecular interactions involving radicals at the correlated level, the energy decomposition analysis scheme for second-order Mo̷ller-Plesset perturbation theory based on absolutely localized molecular orbitals (ALMO-MP2-EDA) is generalized to unrestricted and restricted open-shell MP2. The benefit of restricted open-shell MP2 is that it can provide accurate binding energies for radical complexes where density functional theory can be error-prone due to delocalization errors. As a model application, the open-shell ALMO-MP2-EDA is applied to study the first solvation step of halogenated benzene radical cations, where both halogen- and hydrogen-bonded isomers are possible. We determine that the lighter halogens favor the hydrogen-bonded form, while the iodine-substituted species prefers halogen bonding due to larger polarizability and charge transfer at the halogen. As a second application, relevant to the activation of CO2 in photoelectrocatalysis, complexes of CO2-· interacting with both pyridine and imidazole are analyzed with ALMO-MP2-EDA. The results reveal the importance of charge transfer into the π* orbital of the heterocycle in controlling the stability of the carbamate binding mode, which is favored for pyridine but not for imidazole.

We review the standard model for de novo computational design of enzymes, which primarily focuses on the development of an active-site geometry, composed of protein functional groups in orientations optimized to stabilize the transition state, for a novel chemical reaction not found in nature. Its emphasis is placed on the structure and energetics of the active site embedded in an accommodating protein that serves as a physical support that shields the reaction chemistry from solvent, which is typically improved upon by laboratory-directed evolution. We also provide a review of design strategies that move beyond the standard model, by placing more emphasis on the designed enzyme as a whole catalytic construct. Starting with complete de novo enzyme design examples, we consider additional design factors such as entropy of individual residues, correlated motion between side chains (mutual information), dynamical correlations of the enzyme motions that could aid the reaction, reorganization energy, and electric fields as ways to exploit the entire protein scaffold to improve upon the catalytic rate, thereby providing directed evolution with better starting sequences for increasing biocatalytic performance.

We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multi-resolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training dataset by an order of magnitude without additional cost. We obtain very good agreement for 13C, 15N, and 17O chemical shifts, with the highest accuracy found for 1H chemical shifts that is equivalent to the best predictions using ab initio quantum chemistry methods.