Biology subjects

Skretas, G.

Publications and source records attributed to Skretas, G..

4 recordsLinked to original sources

bioRxiv2026

Targeted mining of plastic-associated metagenomes uncovers a novel thermostable PETase expanding scaffold space for engineering

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 {degrees}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 {degrees}C, yet at 60-65 {degrees}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 {degrees}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.

biochemistry↗

bioRxiv2026

A nested shell structure coordinates enzyme communication in pyruvate oxidation

The pyruvate dehydrogenase complex (PDHc)1 links glycolysis to the Krebs cycle by catalyzing the oxidative decarboxylation of pyruvate to acetyl-CoA and CO2, a process essential for life2,3. PDHc is formed by structural proteins (E3-binding protein, E3BP)4-7, enzymatic subunits (E1, E2, E3)4,6, and mobile lipoyl domains (LDs), the latter shuttling intermediates across active sites6,8. Although numerous details regarding pyruvate oxidation steps have been elucidated9, the precise organization of the entire PDHc remains unknown due to its large size and dynamic heterogeneity. Here, we employ in silico, in vitro, and in situ methods to propose a multi-scale model of PDHc that includes approximately one million atoms and to visualize multiple conformational states. This model reveals a [~]40-50 nm nested shell structure, formed by flexible linkers that spatially coordinate the E1 and E3 enzyme complexes around the E2-E3BP core scaffold. This structure acts as a molecular sieve, selectively guiding lipoyl arms while maintaining enzyme positioning with sub-nm precision. During catalysis, the nested shell structure expands and adopts a mechanically reinforced state comparable in magnitude to viral assemblies10. Our findings provide structural context for the textbook "link reaction"11, building on decades of biochemical knowledge, are transferable to functional aspects of other -ketoacid dehydrogenase complexes, and, ultimately, expand our understanding of primary metabolism as a whole. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/724543v1_ufig1.gif" ALT="Figure 1"> View larger version (70K): org.highwire.dtl.DTLVardef@1615612org.highwire.dtl.DTLVardef@159bc8dorg.highwire.dtl.DTLVardef@69f8c1org.highwire.dtl.DTLVardef@14a83a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Design of Protein Sequences with Precisely Tuned Kinetic Properties

Recent advances in computational biology have enabled solutions to the inverse folding problem - finding an amino acid sequence that folds into a target structure. An open question concerns the design of proteins that in addition to having the correct target structure also have precisely tuned kinetic properties, such as folding and unfolding rates. To address this problem, we formulate the inverse folding problem as a quest for a sequence with a target free energy landscape. To propose a procedure to address this problem, here we describe the Inverse Folding Molecular Dynamics (IF-MD) method, which combines inverse folding with enhanced sampling molecular dynamics and Bayesian optimization. IF-MD leverages ensemble averages from molecular dynamics simulations, reweighted according to a Bayesian framework, to guide the design of sequences exhibiting specific kinetic properties. We demonstrate the methodology by optisising the binding kinetics of H11, a nanobody against the SARS-CoV-2 spike receptor-binding domain (RBD), thus identifying nanobody variants with slower unbinding kinetics than H11. Mechanistic analysis reveals that this kinetic property arises from a shift towards configurations closer to the bound state and increased free energy barriers for dissociation. These findings highlight the power of IF-MD for efficiently navigating the vast sequence space to design proteins with a tailored free energy landscape.

bioinformatics↗

bioRxiv2024

ProteoSeeker: A Feature-Rich Metagenomic Analysis Tool for Accessible and Comprehensive Metagenomic Exploration

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.

bioinformatics↗