Taking immediate action to combat the urgent threat of CO₂-driven global warming is crucial for ensuring a habitable planet. Decarbonizing the industrial sector requires implementing sustainable carbon-capture technologies, such as biomimetic hot potassium carbonate capture (BioHPC). BioHPC is superior to traditional amine-based strategies due to its eco-friendly nature. This innovative technology relies on robust carbonic anhydrases (CAs), enzymes that accelerate CO₂ hydration and endure harsh industrial conditions like high temperature and alkalinity. Thus, the discovery of highly stable CAs is crucial for the BioHPC technology advancement. Through high-throughput bioinformatics analysis, we identified a highly thermo- and alkali-stable CA, termed CA-KR1, originating from a metagenomic sample collected at a hot spring in Kirishima, Japan. CA-KR1 demonstrates remarkable stability at high temperatures and pH, with a half-life of 24 h at 80 °C and retains activity and solubility even after 30 d in a 20% (w/v) K₂CO₃/pH 11.5 solution─a standard medium for HPC. In pressurized batch reactions, CA-KR1 enhanced CO₂ absorption by >90% at 90 °C, 20% K₂CO₃, and 7 bar. To our knowledge, CA-KR1 constitutes the most resilient CA biocatalyst for efficient CO₂ capture under HPC-relevant conditions, reported to date. CA-KR1 integration into industrial settings holds great promise in promoting efficient BioHPC, a potentially game-changing development for enhancing carbon-capture capacity toward industrial decarbonization.

Co-encapsulation of hydrophilic and hydrophobic compounds within a single delivery system remains a significant challenge across various scientific and industrial fields. Towards this direction, an encapsulation strategy is proposed, enabling the simultaneous incorporation of both hydrophilic and hydrophobic biomolecules within a hydrogel matrix. Specifically, the cyanobacterial protein phycocyanin (hydrophilic), extracted and purified by dry Arthrospira maxima biomass, and curcumin (hydrophobic) bound to bovine serum albumin (BSA) were utilized. This approach facilitates the indirect entrapment of hydrophobic molecules within the hydrophilic hydrogel network. The structural and physicochemical properties of the resulting hydrogels were characterized using optical analysis, scanning electron microscopy (SEM), and confocal laser scanning microscopy (CLSM). Additionally, the antioxidant potential of the encapsulated biomolecules was evaluated to assess their functionality after the encapsulation. Furthermore, a cell viability assay confirmed the hydrogel’s biocompatibility and lack of toxicity, demonstrating its suitability as a multifunctional biomaterial for biomedical and pharmaceutical applications.

The vast majority of microbial diversity remains unculturable, limiting access to novel biotechnological resources. Advances in metagenomics have expanded the understanding of microbial communities, yet targeted protein discovery remains challenging. This study introduces ProteoSeeker, a command‐line tool for streamlined metagenomic protein identification and annotation. ProteoSeeker operates in two primary modes: i) Seek mode, which screens the proteins according to user‐defined protein families, and ii) Taxonomy mode, which uncovers the taxonomy of the host organisms. By automating key steps, ProteoSeeker reduces computational complexity, enabling time‐efficient and comprehensive metagenomic analysis for both specialized and nonspecialized users. The efficiency of ProteoSeeker to achieve targeted enzyme discovery is demonstrated by identifying extremophilic enzymes with desired biochemical features, such as amylases for starch hydrolysis and carbonic anhydrases for CO₂ capture applications. By democratizing functional metagenomics, ProteoSeeker is anticipated to accelerate biotechnology, synthetic biology, and biomedical research and innovation.

Enzymatic depolymerization of polyethylene terephthalate (PET) has advanced rapidly, alongside a growing volume of publicly available metagenomic data from microbial communities under sustained selective pressure from plastic exposure. Reasoning that such environments may harbor underexplored polyester-active enzymes, we developed a targeted mining workflow that screens exclusively plastic-associated datasets through multi-step bioinformatic filtering—integrating catalytic-motif screening, disulfide-topology validation, structural-similarity scoring, and phylogenetic profiling—to recover high-confidence PETase candidates. Applied to 277 plastic-associated metagenomes, the pipeline yielded 21 non-redundant candidates, several of which combine the Type I catalytic motif (GHSMGGGG) with Type II-like extended loops and secondary disulfide bonds. Two candidates were experimentally confirmed as PET hydrolases; the more active, PET-KR1, is a thermostable enzyme ( Tm = 66.5 °C) that depolymerizes PET across a broad temperature range, with markedly higher productivity on powdered than on film substrate. PET-KR1 achieved optimal depolymerization at 50 °C, yet at 60–65 °C, where total yields declined, the product pool was more strongly enriched in the terminal monomer TPA, suggesting that thermostability and substrate accessibility are the primary targets for further engineering. Molecular dynamics simulations revealed a conserved hydrophobic binding network around the catalytic serine, consistent with established PETase substrate-recognition modes, and rational disulfide engineering raised the melting temperature by 3.5 °C, confirming amenability to further optimization. Overall, PET-KR1 expands the scaffold space available for PETase engineering, while the discovery workflow, built entirely on publicly available tools and open-access data, provides a reproducible strategy for metagenomic mining of novel PET-degrading enzymes toward biocatalytic PET recycling.

Metagenomics have served as a key driver of biotechnology advancements through, among others, the identification of novel proteins. The lack of standardized guidelines and benchmarks in the field, however, complicates the selection of appropriate bioinformatics tool ensembles and hinders faster progress. This study introduces ProteoSeeker , an automated pipeline designed for accessible and comprehensive metagenomic exploration of whole-genome sequencing data. ProteoSeeker identifies proteins within user-defined protein families and uncovers the taxonomy of the host organisms. It is implemented as a command-line tool to facilitate metagenomic dataset analysis for non-expert users, thus enabling facilitated and streamlined protein discovery.