Earth is flooded with plastics and the need for sustainable recycling strategies for polymers has become increasingly urgent. Enzyme‐based hydrolysis of post‐consumer plastic is an emerging strategy for closed‐loop recycling of polyethylene terephthalate (PET). The polyester hydrolase PHL7, isolated from a compost metagenome, completely hydrolyzes amorphous PET films, releasing 91 mg of terephthalic acid per hour and mg of enzyme. Vertical scanning interferometry shows degradation rates of the PET film of 6.8 μm h⁻¹. Structural analysis indicates the importance of leucine at position 210 for the extraordinarily high PET‐hydrolyzing activity of PHL7. Within 24 h, 0.6 mg_enzyme g_PET⁻¹ completely degrades post‐consumer thermoform PET packaging in an aqueous buffer at 70 °C without any energy‐intensive pretreatments. Terephthalic acid recovered from the enzymatic hydrolysate is then used to synthesize virgin PET, demonstrating the potential of polyester hydrolases as catalysts in sustainable PET recycling processes with a low carbon footprint.

The recently discovered metagenomic-derived polyester hydrolase PHL7 is able to efficiently degrade amorphous polyethylene terephthalate (PET) in post-consumer plastic waste. We present the cocrystal structure of this hydrolase with its hydrolysis product terephthalic acid and elucidate the influence of 17 single mutations on the PET-hydrolytic activity and thermal stability of PHL7. The substrate-binding mode of terephthalic acid is similar to that of the thermophilic polyester hydrolase LCC and deviates from the mesophilicIsPETase. The subsite I modifications L93F and Q95Y, derived from LCC, increased the thermal stability, while exchange of H185S, derived fromIsPETase, reduced the stability of PHL7. The subsite II residue H130 is suggested to represent an adaptation for high thermal stability, whereas L210 emerged as the main contributor to the observed high PET-hydrolytic activity. Variant L210T showed significantly higher activity, achieving a degradation rate of 20 µm h⁻¹with amorphous PET films.

To mobilize sparingly available phosphorus (P) in the rhizosphere, many plant species secrete malate to release P sorbed onto (hydr)oxides of aluminum and iron (Fe). In the presence of Fe, malate can provoke Fe over-accumulation in the root apoplast, triggering a series of events that inhibit root growth. Here, we identified HYPERSENSITIVE TO LOW P1 (HYP1), a CYBDOM protein constituted of a DOMON and a cytochrome b561 domain, as critical to maintain cell elongation and meristem integrity under low P. We demonstrate that HYP1 mediates ascorbate-dependent trans-plasma membrane electron transport and can reduce ferric and cupric substrates in Xenopus laevis oocytes and in planta. HYP1 expression is up-regulated in response to P deficiency in the proximal zone of the root apical meristem. Disruption of HYP1 leads to increased Fe and callose accumulation in the root meristem and causes significant transcriptional changes in roots. We further demonstrate that HYP1 activity overcomes malate-induced Fe accumulation, thereby preventing Fe-dependent root growth arrest in response to low P. Collectively, our results uncover an ascorbate-dependent metalloreductase that is critical to protect root meristems of P-deficient plants from increased Fe availability and provide insights into the physiological function of the yet poorly characterized but ubiquitous CYBDOM proteins.

BiP (immunoglobulin heavy‐chain binding protein) is a Hsp70 monomeric ATPase motor that plays broad and crucial roles in maintaining proteostasis inside the cell. Structurally, BiP is formed by two domains, a nucleotide‐binding domain (NBD) with ATPase activity connected by a flexible hydrophobic linker to the substrate‐binding domain. While the ATPase and substrate binding activities of BiP are allosterically coupled, the latter is also dependent on nucleotide binding. Recent structural studies have provided new insights into BiP's allostery; however, the influence of temperature on the coupling between substrate and nucleotide binding to BiP remains unexplored. Here, we study BiP's binding to its substrate at the single molecule level using thermo‐regulated optical tweezers which allows us to mechanically unfold the client protein and explore the effect of temperature and different nucleotides on BiP binding. Our results confirm that the affinity of BiP for its protein substrate relies on nucleotide binding, by mainly regulating the binding kinetics between BiP and its substrate. Interestingly, our findings also showed that the apparent affinity of BiP for its protein substrate in the presence of nucleotides remains invariable over a wide range of temperatures, suggesting that BiP may interact with its client proteins with similar affinities even when the temperature is not optimal. Thus, BiP could play a role as a “thermal buffer” in proteostasis.

Energetic frustration, characterized by conflicting local interactions, is a key determinant of protein dynamics, allostery, protein-protein interactions, enzyme catalysis, and overall protein function. Although the principle of minimal frustration describes an evolutionary bias towards a reduction of energetic conflicts for efficient protein folding, local violations are selectively embedded to encode the complex energy landscapes necessary for protein functions. However, the computational cost of traditional single-residue frustration analysis has prevented the calculation of these energetic conflicts at the proteome scale, creating a significant bottleneck in structural biology. Here, we introduce FrustraMPNN, a message-passing neural network retrained via transfer learning that predicts a complete per-residue frustration-index mutation profile (full mutational scanning per position for a whole protein) orders-of-magnitude faster than existing methods while maintaining high accuracy. This is demonstrated by calculating the single-residue frustration of the E. coli proteome, reducing the calculation time from years to 12 hours. We validate FrustraMPNN on a diverse external set of over 3,400 human protein structures, achieving a Spearman correlation of up to 0.80 across all frustration categories, demonstrating robust generalizability. By conducting a thorough dataset ablation study, we find that the model's performance improves when trained on a broader range of protein sizes, highlighting an inherent limitation of datasets like Megascale. We provide FrustraMPNN as an open-source tool, expecting it will enable exploration of new hypotheses in fields such as personalized structural biology and allow analysis of local energetic frustration patterns involved in protein function at an unprecedented proteomic scale.