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De novo design of protein catalysts with high efficiency and stereoselectivity provides an attractive approach toward the design of environmentally benign catalysts. Here, we design proteins that incorporate histidine-ligated synthetic porphyrin and heme ligands. Four of 10 designed proteins catalyzed cyclopropanation with an enantiomeric ratio greater than 99:1. A second class of proteins were designed to catalyze a silicon-hydrogen insertion and were optimized by directed evolution in whole cells. The evolved proteins incorporated features unlikely to be generated by computational design alone, including a proline in an α helix. Molecular dynamics simulations showed that as the proteins evolved toward higher activity, their conformational ensembles narrowed to favor more productive conformations. Our work demonstrates that efficient de novo protein catalysts are designable and should be useful for manifold chemical processes. 

We present a novel prompting strategy for artificial intelligence driven digital avatars. To better quantify how our prompting strategy affects anthropomorphic features like humor, authenticity, and favorability we present Crowd Vote - an adaptation of Crowd Score that allows for judges to elect a large language model (LLM) candidate over competitors answering the same or similar prompts. To visualize the responses of our LLM, and the effectiveness of our prompting strategy we propose an end-to-end framework for creating high-fidelity artificial intelligence (AI) driven digital avatars. This pipeline effectively captures an individual's essence for interaction and our streaming algorithm delivers a high-quality digital avatar with real-time audio-video streaming from server to mobile device. Both our visualization tool, and our Crowd Vote metrics demonstrate our AI driven digital avatars have state-of-the-art humor, authenticity, and favorability outperforming all competitors and baselines. In the case of our Donald Trump and Joe Biden avatars, their authenticity and favorability are rated higher than even their real-world equivalents. 

The de novo design of small molecule–binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase–1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule–binding proteins with tuned interaction energies is feasible entirely from computation. 

Coarse-grained materials are widely accepted to display the highest strain hardening and the best tensile ductility. We experimentally report an attractive strain hardening rate throughout the deformation stage at 77 kelvin in a stable single-phase alloy with gradient dislocation cells that even surpasses its coarse-grained counterparts. Contrary to conventional understanding, the exceptional strain hardening arises from a distinctive dynamic structural refinement mechanism facilitated by the emission and motion of massive multiorientational tiny stacking faults (planar defects), which are fundamentally distinct from the traditional linear dislocation–mediated deformation. The dominance of atomic-scale planar deformation faulting in plastic deformation introduces a different approach for strengthening and hardening metallic materials, offering promising properties and potential applications. 

Gradient structures exist ubiquitously in nature and are increasingly being introduced in engineering. However, understanding structural gradient–related mechanical behaviors in all gradient structures, including those in engineering materials, has been challenging. We explored the mechanical performance of a gradient nanotwinned structure with highly tunable structural gradients in pure copper. A large structural gradient allows for superior work hardening and strength that can exceed those of the strongest component of the gradient structure. We found through systematic experiments and atomistic simulations that this unusual behavior is afforded by a unique patterning of ultrahigh densities of dislocations in the grain interiors. These observations not only shed light on gradient structures, but may also indicate a promising route for improving the mechanical properties of materials through gradient design.