CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI

Vasina, M.; Kovar, D.; Kizovsky, M.; Lacko, D.; Vanacek, P.; Herich, M.; Volf, E.; Drdla, L.; Cabalova, S.; Sikorova, P.; Jirasek, M.; Solansky, P.; Jezek, J.; Samek, O.; Dousek, F.; Walner, H.; Zemanek, P.; deMello, A.; Pilat, Z.; Damborsky, J.; Stavrakis, S.; Mazurenko, S.; Prokop, Z.

doi:10.64898/2026.06.04.730034

Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.06.04.730034

CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI

View original source ↗Export metadata ↓

Abstract

Enzyme cascades enable complex biochemical transformations, but their optimization is resource-intensive, requiring navigation through high-dimensional parameter spaces encompassing reaction conditions, enzyme ratios, and buffer composition. Here we introduce CascadeMAP, an autonomous microfluidic platform for closed-loop optimization of enzyme cascades, integrating high-throughput microfluidics with Bayesian optimization and multi-agent AI system. We demonstrate the platform across two cascades: (i) a glycerol detection pathway monitored by fluorescence and (ii) a 1,2,3-trichloropropane degradation pathway monitored by label-free Raman spectroscopy providing orthogonal detection modalities. Bayesian optimization identified optimal conditions three times faster than Design of Experiments. Multi-agent AI system automated hypothesis generation, processing 11 GB of experimental data, pattern recognition, and insight synthesis. Operating without human intervention for 7 days, CascadeMAP processed [~]220,000 reactions across [~]7,400 different conditions. This capability establishes a generalizable framework for the autonomous optimization of enzyme cascades and metabolic pathways and accelerates the development of biocatalytic and synthetic biological systems.

Explore related subjects

biochemistry Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Vasina, M., Kovar, D., Kizovsky, M., Lacko, D., Vanacek, P., Herich, M., Volf, E., Drdla, L., Cabalova, S., Sikorova, P., Jirasek, M., Solansky, P., Jezek, J., Samek, O., Dousek, F., Walner, H., Zemanek, P., deMello, A., Pilat, Z., Damborsky, J., Stavrakis, S., Mazurenko, S., Prokop, Z.. 2026-06-07. CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI. https://doi.org/10.64898/2026.06.04.730034

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Deep reinforcement learning-driven discovery of a MsbA-targeted small-molecule antibiotic for the treatment of Acinetobacter baumannii infection

Antibiotics with new mechanisms are highly pursued to address the threat of infections caused by drug-resistant Gram-negative bacteria. Targeting MsbA, a key protein of the lipopolysaccharide biosynthesis pathway, represents a promising strategy to discover new classes of antibiotics. However, currently available MsbA-targeted molecules either lack sufficient potency or have unfavorable properties, necessitating expansion of chemical space. In this study, we chose the most promising cerastecin Cpd 4 as the template, and used two Artificial Intelligence (AI)-based tools, i.e. Link-INVENT and AutoMolDesigner for molecular design, performed chemical derivatization and antibacterial activity evaluation, which led to the discovery of Y-11 (MIC for A. baumannii: 0.5 g/mL). Encouragingly, Y-11 showed equivalent potency to Cpd4 for carbapenem-resistant A. baumannii, and less cytotoxicity and hemolysis as well as lower spontaneous resistance frequency. In vivo efficacy study demonstrated that Y-11 could effectively reduce bacterial loads in the mice infected by A. baumannii. The following mechanism study including molecular dynamics simulation, biochemical assay, and transmission electron microscope (TEM) analysis suggested that Y-11 inhibited the transport of lipooligosaccharide and impaired the formation of outer membrane, probably by competitively binding to the substrate binding site of MsbA and modulating ATPase activity. Taken together, we have discovered a MsbA-targeted small molecule Y-11 via AI-driven drug design, which provides a foundation for future antibiotic development.

biochemistry↗

bioRxiv2026

Dynamic architecture of the Rixosome reveals mechanism of activation and ITS2 processing

Eukaryotic ribosome assembly requires the coordinated processing and extensive remodeling of pre-rRNAs. During late nuclear maturation of the 60S subunit, sequential removal of the internal transcribed spacer 2 (ITS2) is initiated by endonucleolytic cleavage at site C2 by the conserved Las1 nuclease. Las1 acts together with the kinase Grc3 and the Rix1 complex to form the Rixosome, which also functions in transcriptional regulation. However, the assembly of the Rixosome, its recruitment to pre-ribosomes, and its activation for ITS2 cleavage remain unclear. Here, we present cryo-EM structures of the human LAS1 complex, two structures of the isolated Rixosome and nine transition states of Rix1-bound pre-60S particles from Schizosaccharomyces pombe. These structures reveal a dynamic Rixosome architecture in which the heterotetrameric Las1 complex engages one or two copies of the Rix1 complex. Rix1 binding is highly flexible in the human Rixosome but rigid in the yeast complex. The isolated yeast Rixosome remains inactive, but binding to the pre-60S particle triggers a structural rearrangement that allows for substrate engagement and activation of the nuclease. Together, our results define the dynamic architecture of the Rixosome and provide a structural framework for ITS2 processing during nuclear maturation of the eukaryotic 60S ribosomal subunit.

biochemistry↗

bioRxiv2026

SGFP-Grid Split GFP Graphene Grids

Affinity graphene grids provide a promising approach for selective protein capture in cryo-EM. Here, we introduce a split-GFP graphene grid platform(SGFP-G), in which graphene-conjugated GFP 1-10 selectively captures GFP11 tagged proteins from low concentration samples or cell lysates. This platform enables rapid assessment of target protein enrichment and particle distribution before vitrification via fluorescence imaging, while the grid design positions captured proteins away from the graphene surface and air-water interface. We also introduce a unique strategy to minimize nonspecific protein adsorption, thereby improving the selective enrichment of target proteins on this grid. Using GFP11-tagged apoferritin, we demonstrate fluorescence guided protein capture and obtain a 2.58 [A] cryoEM reconstruction, establishing SGFP-G as an affinity grid platform for high resolution structural studies with reduced sample requirements.

biochemistry↗