The metagenome-derived esterase PET40 is highly promiscuous and hydrolyses polyethylene terephthalate (PET).

Polyethylene terephthalate (PET) is a widely used synthetic polymer and known to contaminate marine and terrestrial ecosystems. Only few PET-active microorganisms and enzymes (PETases) are currently known, and it is debated whether degradation activity for PET originates from promiscuous enzymes with broad substrate spectra that primarily act on natural polymers or other bulky substrates, or whether microorganisms evolved their genetic makeup to accepting PET as a carbon source. Here, we present a predicted diene lactone hydrolase designated PET40, which acts on a broad spectrum of substrates, including PET. It is the first esterase with activity on PET from a GC-rich Gram-positive Amycolatopsis species belonging to the Pseudonocardiaceae (Actinobacteria). It is highly conserved within the genera Amycolatopsis and Streptomyces. PET40 was identified by sequence-based metagenome search using a PETase-specific hidden Markov model. Besides acting on PET, PET40 has a versatile substrate spectrum, hydrolyzing δ-lactones, ß-lactam antibiotics, the polyester-polyurethane Impranil® DLN, and various para-nitrophenyl ester substrates. Molecular docking suggests that the PET degradative activity is likely a result of the promiscuity of PET40, as potential binding modes were found for substrates encompassing mono(2-hydroxyethyl) terephthalate, bis(2-hydroxyethyl) terephthalate, and a PET trimer. We also solved the crystal structure of the inactive PET40 variant S178A to 1.60 Å resolution. PET40 is active throughout a wide pH (pH 4-10) and temperature range (4-65 °C) and remarkably stable in the presence of 5% SDS, making it a promising enzyme as a starting point for further investigations and optimization approaches.

An archaeal lid-containing feruloyl esterase degrades polyethylene terephthalate.

Polyethylene terephthalate (PET) is a commodity polymer known to globally contaminate marine and terrestrial environments. Today, around 80 bacterial and fungal PET-active enzymes (PETases) are known, originating from four bacterial and two fungal phyla. In contrast, no archaeal enzyme had been identified to degrade PET. Here we report on the structural and biochemical characterization of PET46 (RLI42440.1), an archaeal promiscuous feruloyl esterase exhibiting degradation activity on semi-crystalline PET powder comparable to IsPETase and LCC (wildtypes), and higher activity on bis-, and mono-(2-hydroxyethyl) terephthalate (BHET and MHET). The enzyme, found by a sequence-based metagenome search, is derived from a non-cultivated, deep-sea Candidatus Bathyarchaeota archaeon. Biochemical characterization demonstrated that PET46 is a promiscuous, heat-adapted hydrolase. Its crystal structure was solved at a resolution of 1.71 Å. It shares the core alpha/beta-hydrolase fold with bacterial PETases, but contains a unique lid common in feruloyl esterases, which is involved in substrate binding. Thus, our study widens the currently known diversity of PET-hydrolyzing enzymes, by demonstrating PET depolymerization by a plant cell wall-degrading esterase.

Resolution of Maximum Entropy Method-Derived Posterior Conformational Ensembles of a Flexible System Probed by FRET and Molecular Dynamics Simulations.

Maximum entropy methods (MEMs) determine posterior distributions by combining experimental data with prior information. MEMs are frequently used to reconstruct conformational ensembles of molecular systems for experimental information and initial molecular ensembles. We performed time-resolved Förster resonance energy transfer (FRET) experiments to probe the interdye distance distributions of the lipase-specific foldase Lif in the apo state, which likely has highly flexible, disordered, and/or ordered structural elements. Distance distributions estimated from ensembles of molecular dynamics (MD) simulations serve as prior information, and FRET experiments, analyzed within a Bayesian framework to recover distance distributions, are used for optimization. We tested priors obtained by MD with different force fields (FFs) tailored to ordered (FF99SB, FF14SB, and FF19SB) and disordered proteins (IDPSFF and FF99SBdisp). We obtained five substantially different posterior ensembles. As in our FRET experiments the noise is characterized by photon counting statistics, for a validated dye model, MEM can quantify consistencies between experiment and prior or posterior ensembles. However, posterior populations of conformations are uncorrelated to structural similarities for individual structures selected from different prior ensembles. Therefore, we assessed MEM simulating varying priors in synthetic experiments with known target ensembles. We found that (i) the prior and experimental information must be carefully balanced for optimal posterior ensembles to minimize perturbations of populations by overfitting and (ii) only ensemble-integrated quantities like inter-residue distance distributions or density maps can be reliably obtained but not ensembles of atomistic structures. This is because MEM optimizes ensembles but not individual structures. This result for a highly flexible system suggests that structurally varying priors calculated from varying prior ensembles, e.g., generated with different FFs, may serve as an ad hoc estimate for MEM reconstruction robustness.

Enzyme adaptation to habitat thermal legacy shapes the thermal plasticity of marine microbiomes.

Microbial communities respond to temperature with physiological adaptation and compositional turnover. Whether thermal selection of enzymes explains marine microbiome plasticity in response to temperature remains unresolved. By quantifying the thermal behaviour of seven functionally-independent enzyme classes (esterase, extradiol dioxygenase, phosphatase, beta-galactosidase, nuclease, transaminase, and aldo-keto reductase) in native proteomes of marine sediment microbiomes from the Irish Sea to the southern Red Sea, we record a significant effect of the mean annual temperature (MAT) on enzyme response in all cases. Activity and stability profiles of 228 esterases and 5 extradiol dioxygenases from sediment and seawater across 70 locations worldwide validate this thermal pattern. Modelling the esterase phase transition temperature as a measure of structural flexibility confirms the observed relationship with MAT. Furthermore, when considering temperature variability in sites with non-significantly different MATs, the broadest range of enzyme thermal behaviour and the highest growth plasticity of the enriched heterotrophic bacteria occur in samples with the widest annual thermal variability. These results indicate that temperature-driven enzyme selection shapes microbiome thermal plasticity and that thermal variability finely tunes such processes and should be considered alongside MAT in forecasting microbial community thermal response.

Physiological, Biochemical, and Structural Bioinformatic Analysis of the Multiple Inositol Dehydrogenases from Corynebacterium glutamicum.

Inositols (cyclohexanehexols) comprise nine isomeric cyclic sugar alcohols, several of which occur in all domains of life with various functions. Many bacteria can utilize inositols as carbon and energy sources via a specific pathway involving inositol dehydrogenases (IDHs) as the first step of catabolism. The microbial cell factory Corynebacterium glutamicum can grow with myo-inositol as a sole carbon source. Interestingly, this species encodes seven potential IDHs, raising the question of the reason for this multiplicity. We therefore investigated the seven IDHs to determine their function, activity, and selectivity toward the biologically most important isomers myo-, scyllo-, and d-chiro-inositol. We created an ΔIDH strain lacking all seven IDH genes, which could not grow on the three inositols. scyllo- and d-chiro-inositol were identified as novel growth substrates of C. glutamicum. Complementation experiments showed that only four of the seven IDHs (IolG, OxiB, OxiD, and OxiE) enabled growth of the ΔIDH strain on two of the three inositols. The kinetics of the four purified enzymes agreed with the complementation results. IolG and OxiD are NAD+-dependent IDHs accepting myo- and d-chiro-inositol but not scyllo-inositol. OxiB is an NAD+-dependent myo-IDH with a weak activity also for scyllo-inositol but not for d-chiro-inositol. OxiE on the other hand is an NAD+-dependent scyllo-IDH showing also good activity for myo-inositol and a very weak activity for d-chiro-inositol. Structural models, molecular docking experiments, and sequence alignments enabled the identification of the substrate binding sites of the active IDHs and of residues allowing predictions on the substrate specificity. IMPORTANCE myo-, scyllo-, and d-chiro-inositol are C6 cyclic sugar alcohols with various biological functions, which also serve as carbon sources for microbes. Inositol catabolism starts with an oxidation to keto-inositols catalyzed by inositol dehydrogenases (IDHs). The soil bacterium C. glutamicum encodes seven potential IDHs. Using a combination of microbiological, biochemical, and modeling approaches, we analyzed the function of these enzymes and identified four IDHs involved in the catabolism of inositols. They possess distinct substrate preferences for the three isomers, and modeling and sequence alignments allowed the identification of residues important for substrate specificity. Our results expand the knowledge of bacterial inositol metabolism and provide an important basis for the rational development of producer strains for these valuable inositols, which show pharmacological activities against, e.g., Alzheimer's disease, polycystic ovarian syndrome, or type II diabetes.

Rational Design Yields Molecular Insights on Leaf-Binding of Anchor Peptides.

In times of a constantly growing world population and increasing demand for food, sustainable agriculture is crucial. The rainfastness of plant protection agents is of pivotal importance to reduce the amount of applied nutrients, herbicides, and fungicides. As a result of protective agent wash-off, plant protection is lost, and soils and groundwater are severely polluted. To date, rainfastness of plant protection products has been achieved by adding polymeric adjuvants to the agrochemicals. However, polymeric adjuvants will be regarded as microplastics in the future, and environmentally friendly alternatives are needed. Anchor peptides (APs) are promising biobased and biodegradable adhesion promoters. Although the adhesion of anchor peptides to artificial surfaces, such as polymers, has already been investigated in theory and experimentally, exploiting the adhesion to biological surfaces remains challenging. The complex nature and composition of biological surfaces such as plant leaves and fruit surfaces complicate the generation of accurate models. Here, we present the first detailed three-layered atomistic model of the surface of apple leaves and use it to compute free energy profiles of the adhesion and desorption of APs to and from that surface. Our model is validated by a novel fluorescence-based microtiter plate (MTP) assay that mimics these complex processes and allows for quantifying them. For the AP Macaque Histatin, we demonstrate that aromatic and positively charged amino acids are essential for binding to the waxy apple leaf surface. The established protocols should generally be applicable for tailoring the binding properties of APs to biological interfaces.

Converging a Knowledge-Based Scoring Function: DrugScore2018.

We present DrugScore2018, a new version of the knowledge-based scoring function DrugScore, which builds upon the same formalism used to derive DrugScore but exploits a training data set of nearly 40 000 X-ray complex structures, a highly diverse and the, by far, largest data set ever used for such an endeavor. About 2.5 times as many pair potentials than before now have a data basis required to yield smooth potentials, and pair potentials could now be derived for eight more atom types, including interactions involving halogen atoms and metal ions highly relevant for medicinal chemistry. Probing for dependence on training data set size and quality, we show that DrugScore2018 potentials are converged. We evaluated DrugScore2018 in comprehensive scoring, ranking, docking, and screening tests on the CASF-2013 data set, allowing for a comparison with >30 other scoring functions. There, DrugScore2018 showed similar or improved performance in all aspects when compared to either DrugScore, DrugScoreCSD, or DSX and was, overall, the scoring function showing the most consistently good performance in scoring, ranking, and docking tests. Applying DrugScore2018 as objective function in AutoDock3 in a large-scale docking trial, using 4056 protein-ligand complexes from PDBbind 2016, reproduced a docked pose to within 2 ŠRMSD to the crystal structure in >75% of all dockings. These results are remarkable as the DrugScore2018 potentials were derived from crystallographic information only, without any further adaptation using binding affinity or docking decoy data. DrugScore2018 should thus be a competitive scoring and objective function for structure-based ligand design purposes.