Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO2-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

We report a sustainable in vitro system for enzyme-based photohydrogen production. The [FeFe]-hydrogenase HydA1 from Chlamydomonas reinhardtii was tested for photohydrogen production as a proton-reducing catalyst in combination with eight different photosensitizers. Using the organic dye 5-carboxyeosin as a photosensitizer and plant-type ferredoxin PetF as an electron mediator, HydA1 achieves the highest light-driven turnover number (TON ) yet reported for an enzyme-based in vitro system (2.9×10 mol(H ) mol(cat) ) and a maximum turnover frequency (TOF ) of 550 mol(H ) mol(HydA1) s . The system is fueled very effectively by ambient daylight and can be further simplified by using 5-carboxyeosin and HydA1 as a two-component photosensitizer/biocatalyst system without an additional redox mediator.

The sustainable capture and conversion of carbon dioxide (CO2) is key to achieving a circular carbon economy. Bioelectrocatalysis, which aims at using renewable energies to power the highly specific, direct transformation of CO2 into value added products, holds promise to achieve this goal. However, the functional integration of CO2‐fixing enzymes onto electrode materials for the electrosynthesis of stereochemically complex molecules remains to be demonstrated. Here, we show the electricity‐driven regio‐ and stereoselective incorporation of CO2 into crotonyl‐CoA by an NADPH‐dependent enzymatic reductive carboxylation. Co‐immobilization of a ferredoxin NADP+ reductase and crotonyl‐CoA carboxylase/reductase within a 2,2′‐viologen‐modified hydrogel enabled iterative NADPH recycling and stereoselective formation of (2S)‐ethylmalonyl‐CoA, a prospective intermediate towards multi‐carbon products from CO2, with 92±6 % faradaic efficiency and at a rate of 1.6±0.4 μmol cm−2 h−1. This approach paves the way for realizing even more complex bioelectrocatalyic cascades in the future.

Eleven samples of eight European commercial varieties of winter rye were examined at eight polymorphic enzyme loci. Genotype frequencies fitted Hardy-Weinberg expectations at all loci in all samples studied. Of the total genetic diversity recorded at the 8 loci, only 7% was expressed between varieties. Allele frequency differences between varieties were, however, sufficient to allow a characterization of each variety by a specific set of allele frequencies. Using subsets of the original data, it could be demonstrated that all pairs of varieties but one still showed significant allozyme differences, when only 4 loci were screened in samples half the original size of 200 individuals. Even when only one locus was analyzed, all varieties but two were distinguishable, but this "diagnostic" locus was not identical in all pairwise comparisons.

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Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low cost. Here, we established a cell-free protein synthesis (CFPS) pipeline for the rapid and inexpensive production of antimicrobial peptides (AMPs) directly from DNA templates. To validate our platform, we used deep learning to design thousands of AMPs de novo. Using computational methods, we prioritized 500 candidates that we produced and screened with our CFPS pipeline. We identified 30 functional AMPs, which we characterized further through molecular dynamics simulations, antimicrobial activity and toxicity. Notably, six de novo-AMPs feature broad-spectrum activity against multidrug-resistant pathogens and do not develop bacterial resistance. Our work demonstrates the potential of CFPS for production and testing of bioactive peptides within less than 24 hours and <10$ per screen.

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