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Enzymes exist in ensembles of states that encode the energetics underlying their catalysis. Conformational ensembles built from 1231 structures of 17 serine proteases revealed atomic-level changes across their reaction states. By comparing the enzymatic and solution reaction, we identified molecular features that provide catalysis and quantified their energetic contributions to catalysis. Serine proteases precisely position their reactants in destabilized conformers, creating a downhill energetic gradient that selectively favors the motions required for reaction while limiting off-pathway conformational states. The same catalytic features have repeatedly evolved in proteases and additional enzymes across multiple distinct structural folds. Our ensemble-function analyses revealed previously unknown catalytic features, provided quantitative models based on simple physical and chemical principles, and identified motifs recurrent in nature that may inspire enzyme design. 

When multiple reaction steps occur before thermal equilibration, kinetic energy from one reaction step can influence overall product distributions in ways that are not well predicted by transition state theory. An understanding of how the structural features of mechanophores, such as substitutions, affects reactivity, product distribution, and the extent of dynamic effects in the mechanochemical manifolds is necessary for designing chemical reactions and responsive materials. We synthesized two tetrafluorinated [4]-ladderanes with fluorination on different rungs and found that the fluorination pattern influenced the force sensitivity and stereochemical distribution of products in the mechanochemistry of these fluorinated ladderanes. The threshold forces for mechanochemical unzipping of ladderane were decreased by alpha-fluorination and increased by gamma-fluorination; these changes correlated to the different stabilizing or destabilizing effects of fluorination patterns on the first transition state. Using ab initio steered molecular dynamics (AISMD), we compared the product distributions of synthesized and hypothetical ladderanes with different substitution patterns. These calculations suggest that fluorination on the first two bonds of ladderane gives rise to a larger fraction of dynamic trajectories and a larger fraction of E alkene prod-uct through a mechanism resulting from larger momentum because of the greater atomic mass of fluorine. Fluorination on the third and fourth rungs instead gives a larger fraction of E alkene product primarily due to electronic effects. These com-bined experimental and computational studies of the mechanochemical unzipping of fluorinated ladderanes provide an example of how relatively simple substituents can affect the extent of non-statistical dynamics, and thus mechanochemical outcomes. 

Pterodactylane is a [4]-ladderane with substituents on the central rung. Comparing the mechanochemistry of the [4]-ladderane structure when pulled from the central rung versus the end rung revealed a striking difference in the threshold force of mechanoactivation: the threshold force is dramatically lowered from 1.9 nN when pulled on the end rung to 0.7 nN when pulled on the central rung. We investigated the bicyclic products formed from the mechanochemical activation of pterodactylane experimentally and computationally, which are distinct from the mechanochemical products of ladderanes being activated from the end rung. We compared the products of pterodactylane’s mechanochemical and thermal activation to reveal differences and similarities in the mechanochemical and thermal pathways of pterodactylane transformation. Interestingly, we also discovered the presence of elementary steps that are accelerated or suppressed by force within the same mechanochemical reaction of pterodactylane, suggesting rich mechanochemical manifolds of multicyclic structures. We rationalized the greatly enhanced mechanochemical reactivity of the central rung of pterodactylane and discovered force-free ground state bond length to be a good low-cost predictor of the threshold force for cyclobutane-based mechanophores. These findings advance our understanding of mechanochemical reactivities and pathways, and they will guide future designs of mechanophores with low threshold forces to facilitate their applications in force-responsive materials. 

Machine learning (ML) offers an attractive method for making predictions about molecular systems while circumventing the need to run expensive electronic structure calculations. Once trained on ab initio data, the promise of ML is to deliver accurate predictions of molecular properties that were previously computationally infeasible. In this work, we develop and train a graph neural network model to correct the basis set incompleteness error (BSIE) between a small and large basis set at the RHF and B3LYP levels of theory. Our results show that, when compared to fitting to the total potential, an ML model fitted to correct the BSIE is better at generalizing to systems not seen during training. We test this ability by training on single molecules while evaluating on molecular complexes. We also show that ensemble models yield better behaved potentials in situations where the training data is insufficient. However, even when only fitting to the BSIE, acceptable performance is only achieved when the training data sufficiently resemble the systems one wants to make predictions on. The test error of the final model trained to predict the difference between the cc-pVDZ and cc-pV5Z potential is 0.184 kcal/mol for the B3LYP density functional, and the ensemble model accurately reproduces the large basis set interaction energy curves on the S66x8 dataset. 

Proton transfer reactions are ubiquitous in chemistry, especially in aqueous solutions. We investigate photoinduced proton transfer between the photoacid 8-hydroxypyrene-1,3,6- trisulfonate (HPTS) and water using fast fluorescence spectroscopy and ab initio molecular dynamics simulations. Photoexcitation causes rapid proton release from the HPTS hydroxyl. Previous experiments on HPTS/water described the progress from photoexcitation to proton diffusion using kinetic equations with two time constants. The shortest time constant has been interpreted as protonated and photoexcited HPTS evolving into an “associated” state, where the proton is “shared” between the HPTS hydroxyl and an originally hydrogen bonded water. The longer time constant has been interpreted as indicating evolution to a “solvent separated” state where the shared proton undergoes long distance diffusion. In this work, we refine the previous experimental results using very pure HPTS. We then use excited state ab initio molecular dynamics to elucidate the detailed molecular mechanism of aqueous excited state proton transfer in HPTS. We find that the initial excitation results in rapid rearrangement of water, forming a strong hydrogen bonded network (a “water wire”) around HPTS. HPTS then deprotonates in ≤3 ps, resulting in a proton that migrates back and forth along the wire before localizing on a single water molecule. We find a near linear relationship between the emission wavelength and proton-HPTS distance over the simulated time scale, suggesting that the emission wavelength can be used as a ruler for the proton distance. Our simulations reveal that the “associated” state corresponds to a water wire with a mobile proton and that the diffusion of the proton away from this water wire (to a generalized “solvent separated” state) corresponds to the longest experimental time constant. 

Proton transfer reactions are ubiquitous in chemistry, especially in aqueous solutions. We investigate photo-induced proton transfer between the photoacid 8-hydroxypyrene-1,3,6-trisulfonate (HPTS) and water using fast fluorescence spectroscopy and ab initio molecular dynamics simulations. Photo-excitation causes rapid proton release from the HPTS hydroxyl. Previous experiments on HPTS/water described the progress from photoexcitation to proton diffusion using kinetic equations with two time constants. The shortest time constant has been interpreted as protonated and photoexcited HPTS evolving into an “associated” state, where the proton is “shared” between the HPTS hydroxyl and an originally hydrogen bonded water. The longer time constant has been interpreted as indicating evolution to a “solvent separated” state where the shared proton undergoes long distance diffusion. In this work, we refine the previous experimental results using very pure HPTS. We then use excited state ab initio molecular dynamics to elucidate the detailed molecular mechanism of aqueous excited state proton transfer in HPTS. We find that the initial excitation results in rapid rearrangement of water, forming a strong hydrogen bonded network (a “water wire”) around HPTS. HPTS then deprotonates in ≤3 ps, resulting in a proton that migrates back and forth along the wire before localizing on a single water molecule. We find a near linear relationship between emission wavelength and proton-HPTS distance over the simulations’ time scale, suggesting that emission wavelength can be used as a ruler for proton distance. Our simulations reveal that the “associated” state corresponds to a water wire with a mobile proton and that the diffusion of the proton away from this water wire (to a generalized “solvent-separated” state) corresponds to the longest experimental time constant.