Search for: All records

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. 

Abstract We consider the nuclear absorption of dark matter as an alternative to the typical indirect detection search channels of dark matter decay or annihilation. In this scenario, an atomic nucleus transitions to an excited state by absorbing a pseudoscalar dark matter particle and promptly emits a photon as it transitions back to its ground state. The nuclear excitation of carbon and oxygen in the Galactic Center would produce a discrete photon spectrum in the𝒪(10) MeV range that could be detected by gamma-ray telescopes. Using theBIGSTICKlarge-scale shell-model code, we calculate the excitation energies of carbon and oxygen. We constrain the dark matter-nucleus coupling for current COMPTEL data, and provide projections for future experiments AMEGO-X, e-ASTROGAM, and GRAMS for dark matter masses from ∼ 10 to 30 MeV. We find the excitation process to be very sensitive to the dark matter mass and find that the future experiments considered would improve constraints on the dark matter-nucleus coupling within an order of magnitude. 

Abstract Routine high strain rate impacts from the surrounding environment can cause surface erosion, abrasion, and even catastrophic failure to many structural materials. It is thus highly desirable to develop lightweight, thin, and tough impact resistant coatings. Here, inspired by the structurally robust impact surface of the dactyl club of the peacock mantis shrimp, a silicon carbide and chitosan nanocomposite coating is developed to evaluate its impact resistance as a function of particle loading. High strain rate impact tests demonstrate that coatings with 50% and 60% SiC have optimal performance with the greatest reduction in penetration depth and damage area to the substrate. Post‐impact analysis confirms that these concentrations achieve a balance between stiffness and matrix phase continuity, efficiently dissipating impact energy while maintaining coating integrity. The addition of SiC particles helps dissipate impact energy via interphase effects, particle percolation, and frictional losses due to particle jamming. The formation of these stress paths is also modeled to better understand how the addition of particles improves coating stiffness and the stress distribution as a function of particle loading. These findings highlight the potential of bioinspired materials and their promise to promote innovation and breakthroughs in the development of resilient multifunctional materials. 

Barley stripe mosaic virus (BSMV) is a promising biotemplate for the mineralization of metal–organic nanorods. Biomineralization of palladium occurs without an external reducing agent; however, the reduction of gold on wild-type BSMV requires a reducing agent. Recently, histidine has been adopted as a capping and reducing agent for the mineralization of gold nanoparticles. BSMV virus-like particles (BSMV-VLPs) tagged with histidine were investigated for direct gold deposition. However, gold nanoparticles were not formed during the mineralization process. Therefore, the aim of this research was to decorate gold nanoparticles onto palladium-coated BSMV (Pd-BSMV). The gold decoration was achieved through the addition of free histidine. X-ray absorption spectroscopy and energy-dispersive X-ray spectroscopy were used to verify the formation of metallic gold, and a kinetic study of the gold decoration process and the pH effect on the morphologies of gold particles was performed. The development of gold-decorated Pd-BSMV will be crucial for therapeutic applications, such as drug delivery, gene therapy, and photothermal therapy. 

Abstract Desiccation cracking is a frequent natural phenomenon that occurs in drying soil and has a significant negative impact on the mechanical and hydraulic properties of clay or geomaterials in various engineering applications. In this study, recycled glass sand (RGS) was used to reduce the plasticity of clay soil and mitigate desiccation cracks in clay soils. The effect of the RGS particle size and content was investigated using a desiccation crack observation test. Digital image processing technology was used to evaluate the crack rate, length, width, and area during the observation test. The results reveal that the cracking rate was inversely proportional to the RGS content and directly proportional to the RGS particle size. For instance, the cracking rate of clay soil treated with 25% RGS with a particle size of 0.15 mm was reduced to 0.17% compared with untreated soil. The strengths of the untreated and RGS-treated soils were evaluated through unconfined compression tests. The unconfined compressive strength of the RGS-treated clay soil decreased slightly with the addition of RGS. In general, the addition of RGS has great potential for mitigating desiccation cracks in clay soils. 

The hybrid nature of multi-contact robotic systems, due to making and breaking contact with the environment, creates significant challenges for high-quality control. Existing model-based methods typically rely on either good prior knowledge of the multi-contact model or require significant offline model tuning effort, thus resulting in low adaptability and robustness. In this paper, we propose a realtime adaptive multi-contact model predictive control framework, which enables online adaption of the hybrid multi-contact model and continuous improvement of the control performance for contact-rich tasks. This framework includes an adaption module, which continuously learns a residual of the hybrid model to minimize the gap between the prior model and reality, and a real-time multi-contact MPC controller. We demonstrated the effectiveness of the framework in synthetic examples, and applied it on hardware to solve contact-rich manipulation tasks, where a robot uses its end-effector to roll different unknown objects on a table to track given paths. The hardware experiments show that with a rough prior model, the multi-contact MPC controller adapts itself on-the-fly with an adaption rate around 20 Hz and successfully manipulates previously unknown objects with non-smooth surface geometries. Accompanying media can be found at: https://sites.google.com/view/adaptive-contact-implicit-mpc/home 

Abstract The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochromecto achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY’s potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution. 

The prosperity of deep learning and automated machine learning (AutoML) is largely rooted in the development of novel neural networks -- but what defines and controls the "goodness" of networks in an architecture space? Test accuracy, a golden standard in AutoML, is closely related to three aspects: (1) expressivity (how complicated functions a network can approximate over the training data); (2) convergence (how fast the network can reach low training error under gradient descent); (3) generalization (whether a trained network can be generalized from the training data to unseen samples with low test error). However, most previous theory papers focus on fixed model structures, largely ignoring sophisticated networks used in practice. To facilitate the interpretation and understanding of the architecture design by AutoML, we target connecting a bigger picture: how does the architecture jointly impact its expressivity, convergence, and generalization? We demonstrate the "no free lunch" behavior in networks from an architecture space: given a fixed budget on the number of parameters, there does not exist a single architecture that is optimal in all three aspects. In other words, separately optimizing expressivity, convergence, and generalization will achieve different networks in the architecture space. Our analysis can explain a wide range of observations in AutoML. Experiments on popular benchmarks confirm our theoretical analysis. Our codes are attached in the supplement.