A closed-loop catalytic nanoreactor system on a transistor.

Precision chemistry demands miniaturized catalytic systems for sophisticated reactions with well-defined pathways. An ideal solution is to construct a nanoreactor system functioning as a chemistry laboratory to execute a full chemical process with molecular precision. However, existing nanoscale catalytic systems fail to in situ control reaction kinetics in a closed-loop manner, lacking the precision toward ultimate reaction efficiency. We find an inter-electrochemical gating effect when operating DNA framework-constructed enzyme cascade nanoreactors on a transistor, enabling in situ closed-loop reaction monitoring and modulation electrically. Therefore, a comprehensive system is developed, encapsulating nanoreactors, analyzers, and modulators, where the gate potential modulates enzyme activity and switches cascade reaction "ON" or "OFF." Such electric field-effect property enhances catalytic efficiency of enzyme by 343.4-fold and enables sensitive sarcosine assay for prostate cancer diagnoses, with a limit of detection five orders of magnitude lower than methodologies in clinical laboratory. By coupling with solid-state electronics, this work provides a perspective to construct intelligent nano-systems for precision chemistry.

Combining mass spectrometry and machine learning to discover bioactive peptides.

Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the literature. The majority of these are inactive degradation products of endogenous proteins and peptides, presenting a needle-in-a-haystack problem of identifying the most promising candidate peptides from large-scale peptidomics experiments to test for bioactivity. To address this challenge, we conducted a comprehensive analysis of the mammalian peptidome across seven tissues in four different mouse strains and used the data to train a machine learning model that predicts hundreds of peptide candidates based on patterns in the mass spectrometry data. We provide in silico validation examples and experimental confirmation of bioactivity for two peptides, demonstrating the utility of this resource for discovering lead peptides for further characterization and therapeutic development.