Inspired by biological motors, researchers have engineered artificial micro- and nanoswimmers able to self-propel by converting chemical energy from their surrounding into motion. In our group, we pioneered the use of enzymes as engines, which catalyse bioavailable substrates (fuel) into products, providing nanomotors with a biocompatible propulsive source and therefore opening many possibilities for nanomedicine-related applications. Although there are a few reports on enzyme-nanomotors, the key properties to generate self-propulsion have not been studied so far.

In this work, we aim at understanding at fundamental level the enzyme properties that predict the best performance of micromotors. To do so, we selected four different enzymes, urease, acetylcholinesterase, glucose oxidase and aldolase, to catalyze their specific “fuels” when bound to the surface of the micromotor. In Samuel Sánchez group (IBEC) we found that urease-motors showed the fastest self-propulsion indicating that self-propulsion of micromotors was correlated with a higher catalytic turnover of the enzyme used as engine. To gain more insight into the mechanistic structure at molecular level, in Sílvia Osuna group (IQCC at UdG), calculations based on Molecular Dynamics simulations revealed that the structural flexibility next to the active site, where the reaction occurs, of each enzyme was crucial for self-propulsion. These theoretical predictions were experimentally confirmed by exposing urease micromotors to inhibitors and compounds that increase enzyme rigidity.

From these results we extracted that the conformational changes are a precondition to catalysis, which in its turn is the key factor for an optimal self-propulsion.

This project has been the first study combining molecular dynamics and experimental work aimed at understanding enzyme-driven self propulsion from a mechanistic point of view. It paves the way towards the comprehension of the processes underlying catalytic self-propulsion and contributes to a more robust criteria for the rational design of enzyme-powered micromotors.

Here, we determined the relative importance of different transcriptional mechanisms in the genome-reduced bacterium Mycoplasma pneumoniae, by employing an array of experimental techniques under multiple genetic and environmental perturbations. Of the 143 genes tested (21% of the bacterium's annotated proteins), only 55% showed an altered phenotype, highlighting the robustness of biological systems. We identified nine transcription factors (TFs) and their targets, representing 43% of the genome, and 16 regulators that indirectly affect transcription. Only 20% of transcriptional regulation is mediated by canonical TFs when responding to perturbations. Using a Random Forest, we quantified the non-redundant contribution of different mechanisms such as supercoiling, metabolic control, RNA degradation, and chromosome topology to transcriptional changes. Model-predicted gene changes correlate well with experimental data in 95% of the tested perturbations, explaining up to 70% of the total variance when also considering noise. This analysis highlights the importance of considering non-TF-mediated regulation when engineering bacteria.