Collaborating Authors

[Report] An artificial metalloenzyme with the kinetics of native enzymes


Natural enzymes contain highly evolved active sites that lead to fast rates and high selectivities. Although artificial metalloenzymes have been developed that catalyze abiological transformations with high stereoselectivity, the activities of these artificial enzymes are much lower than those of natural enzymes. Here, we report a reconstituted artificial metalloenzyme containing an iridium porphyrin that exhibits kinetic parameters similar to those of natural enzymes. This activity leads to intramolecular carbene insertions into unactivated C–H bonds and intermolecular carbene insertions into C–H bonds. These results lift the restrictions on merging chemical catalysis and biocatalysis to create highly active, productive, and selective metalloenzymes for abiological reactions.

If You Could Change by "Inserting" Knowledge… Should You?


John Tillson, philosopher of education and author of Children, Religion, and the Ethics of Influence, asks if, instead of drills and homework, what about just "learning" a skill via a computer cable plugged into the back of your head, the way Neo learned karate in The Matrix?: Even if we dodge the threat of replacement by downloading a modest suite of knowledge at a suitably gentle pace, we might still worry that knowledge insertion would make us become someone we wouldn't want to be. Suppose Neo was racist and wanted to stay racist. I'd say that losing his racism as an unexpected and unwanted side-effect of uploading kung fu would be objectively serendipitous. Any antecedent hostility to becoming non-racist wouldn't constitute a problem: it should be a welcome becoming. However, while unwelcome becoming isn't always a problem, it might sometimes be.

Membrane protein insertion through a mitochondrial {beta}-barrel gate


The outer membranes of Gram-negative bacteria, mitochondria, and chloroplasts characteristically contain β-barrel membrane proteins. These proteins contain multiple amphipathic β strands that form a closed barrel.

Towards Robotic Assembly by Predicting Robust, Precise and Task-oriented Grasps Artificial Intelligence

Robust task-oriented grasp planning is vital for autonomous robotic precision assembly tasks. Knowledge of the objects' geometry and preconditions of the target task should be incorporated when determining the proper grasp to execute. However, several factors contribute to the challenges of realizing these grasps such as noise when controlling the robot, unknown object properties, and difficulties modeling complex object-object interactions. We propose a method that decomposes this problem and optimizes for grasp robustness, precision, and task performance by learning three cascaded networks. We evaluate our method in simulation on three common assembly tasks: inserting gears onto pegs, aligning brackets into corners, and inserting shapes into slots. Our policies are trained using a curriculum based on large-scale self-supervised grasp simulations with procedurally generated objects. Finally, we evaluate the performance of the first two tasks with a real robot where our method achieves 4.28mm error for bracket insertion and 1.44mm error for gear insertion.