Pyranose dehydrogenase ligand promiscuity: a generalized approach to simulate monosaccharide solvation, binding, and product formation

Structural and functional changes in the fungal community of plant detritus in an invaded Atlantic Forest

BACKGROUND

Changes in the fungal community in the litter decomposition by invasive plants can negatively impact nutrient cycling in natural ecosystems. One still does not know the dimension of this hypothesis, but apparently, it is not despicable. This study evaluated the assemblage composition of fungi during litter decomposition in areas of Atlantic Forest invaded or not invaded by Tradescantia zebrina using Illumina MiSeq and metabarcoding analysis.

RESULTS

The invaded sample showed significantly higher richness and a difference in the species dominance than the invaded litter. Ascomycota was the first most abundant phylum in both areas. Even so, the dissimilarity between areas can be evidenced. The fungal from Basidiomycota were very representative in the non-invaded areas (ranged from an abundance of 43.29% in the non-invaded to 2.35% in the invaded sample). The genus Lepiota can indicate the primary functional group related to biomass degradation and showed the might difference about the invaded areas due to its essential reduction by the invader. In the invaded sample, there was a total absence of the endophyte-undefined saprotroph guild. Also, some genera not taxonomically characterized were eliminated in the invaded sample, revealing that the fungal biodiversity of areas has not yet been thoroughly characterized.

CONCLUSIONS

Hence, makes impossible the real interpretation of the invasive plant impact, showing the importance of continuing research on fungal biodiversity. It is important to emphasize that the replacement of the native species by T. zebrina may be responsible for the elimination of fungal groups that have not yet been identified.

Structural and functional changes in the fungal community of plant detritus in an invaded Atlantic Forest

BACKGROUND

Changes in the fungal community in the litter decomposition by invasive plants can negatively impact nutrient cycling in natural ecosystems. One still does not know the dimension of this hypothesis, but apparently, it is not despicable. This study evaluated the assemblage composition of fungi during litter decomposition in areas of Atlantic Forest invaded or not invaded by Tradescantia zebrina using Illumina MiSeq and metabarcoding analysis.

RESULTS

The invaded sample showed significantly higher richness and a difference in the species dominance than the invaded litter. Ascomycota was the first most abundant phylum in both areas. Even so, the dissimilarity between areas can be evidenced. The fungal from Basidiomycota were very representative in the non-invaded areas (ranged from an abundance of 43.29% in the non-invaded to 2.35% in the invaded sample). The genus Lepiota can indicate the primary functional group related to biomass degradation and showed the might difference about the invaded areas due to its essential reduction by the invader. In the invaded sample, there was a total absence of the endophyte-undefined saprotroph guild. Also, some genera not taxonomically characterized were eliminated in the invaded sample, revealing that the fungal biodiversity of areas has not yet been thoroughly characterized.

CONCLUSIONS

Hence, makes impossible the real interpretation of the invasive plant impact, showing the importance of continuing research on fungal biodiversity. It is important to emphasize that the replacement of the native species by T. zebrina may be responsible for the elimination of fungal groups that have not yet been identified.

Identification of Optimal Ligand Growth Vectors Using an Alchemical Free-Energy Method

In this work, a novel method to rationally design inhibitors with improved steric contacts and enhanced binding free energies is presented. This new method uses alchemical single step perturbation calculations to rapidly optimize the van der Waals interactions of a small molecule in a protein-ligand complex in order to maximize its binding affinity. The results of the optimizer are used to predict beneficial growth vectors on the ligand, and good agreement is found between the predictions from the optimizer and a more rigorous free energy calculation, with a Spearman's rank order correlation of 0.59. The advantage of the method presented here is the significant speed up of over 10-fold compared to traditional free energy calculations and sublinear scaling with the number of growth vectors assessed. Where experimental data were available, mutations from hydrogen to a methyl group at sites highlighted by the optimizer were calculated with MBAR, and the mean unsigned error between experimental and calculated values of the binding free energy was 0.83 kcal/mol.

Toward Automated Free Energy Calculation with Accelerated Enveloping Distribution Sampling (A-EDS)

Free-energy perturbation (FEP) methods are commonly used in drug design to calculate relative binding free energies of different ligands to a common host protein. Alchemical ligand transformations are usually performed in multiple steps which need to be chosen carefully to ensure sufficient phase-space overlap between neighboring states. With one-step or single-step FEP techniques, a single reference state is designed that samples phase-space not only representative of a full transformation but also ideally resembles multiple ligand end states and hence allows for efficient multistate perturbations. Enveloping distribution sampling (EDS) is one example for such a method in which the reference state is created by a mathematical combination of the different ligand end states based on solid statistical mechanics. We have recently proposed a novel approach to EDS which enables efficient barrier crossing between the different end states, termed accelerated EDS (A-EDS). In this work, we further simplify the parametrization of the A-EDS reference state and demonstrate the automated calculation of multiple free-energy differences between different ligands from a single simulation in three different well-described drug design model systems.

Saturation Mutagenesis by Efficient Free-Energy Calculation

Single-point mutations in proteins can greatly influence protein stability, binding affinity, protein function or its expression per se. Here, we present accurate and efficient predictions of the free energy of mutation of amino acids. We divided the complete mutational free energy into an uncharging step, which we approximate by a third-power fitting (TPF) approach, and an annihilation step, which we approximate using the one-step perturbation (OSP) method. As a diverse set of test systems, we computed the solvation free energy of all amino acid side chain analogues and obtained an excellent agreement with thermodynamic integration (TI) data. Moreover, we calculated mutational free energies in model tripeptides and established an efficient protocol involving a single reference state. Again, the approximate methods agreed excellently with the TI references, with a root-mean-square error of only 3.6 kJ/mol over 17 mutations. Our combined TPF+OSP approach does show not only a very good agreement but also a 2-fold higher efficiency than full blown TI calculations.

Characterization of three pyranose dehydrogenase isoforms from the litter-decomposing basidiomycete Leucoagaricus meleagris (syn. Agaricus meleagris)

Multigenicity is commonly found in fungal enzyme systems, with the purpose of functional compensation upon deficiency of one of its members or leading to enzyme isoforms with new functionalities through gene diversification. Three genes of the flavin-dependent glucose-methanol-choline (GMC) oxidoreductase pyranose dehydrogenase (AmPDH) were previously identified in the litter-degrading fungus Agaricus (Leucoagaricus) meleagris, of which only AmPDH1 was successfully expressed and characterized. The aim of this work was to study the biophysical and biochemical properties of AmPDH2 and AmPDH3 and compare them with those of AmPDH1. AmPDH1, AmPDH2 and AmPDH3 showed negligible oxygen reactivity and possess a covalently tethered FAD cofactor. All three isoforms can oxidise a range of different monosaccarides and oligosaccharides including glucose, mannose, galactose and xylose, which are the main constituent sugars of cellulose and hemicelluloses, and judging from the apparent steady-state kinetics determined for these sugars, the three isoforms do not show significant differences pertaining to their reaction with sugar substrates. They oxidize glucose both at C2 and C3 and upon prolonged reaction C2 and C3 double-oxidized glucose is obtained, confirming that the A. meleagris genes pdh2 (AY753308.1) and pdh3 (DQ117577.1) indeed encode CAZy class AA3_2 pyranose dehydrogenases. While reactivity with electron donor substrates was comparable for the three AmPDH isoforms, their kinetic properties differed significantly for the model electron acceptor substrates tested, a radical (the 2,2'-azino-bis[3-ethylbenzothiazoline-6-sulphonic acid] cation radical), a quinone (benzoquinone) and a complexed iron ion (the ferricenium ion). Thus, a possible explanation for this PDH multiplicity in A. meleagris could be that different isoforms react preferentially with structurally different electron acceptors in vivo.

Multiple Binding Poses in the Hydrophobic Cavity of Bee Odorant Binding Protein AmelOBP14

In the first step of olfaction, odorants are bound and solubilized by small globular odorant binding proteins (OBPs) which shuttle them to the membrane of a sensory neuron. Low ligand affinity and selectivity at this step enable the recognition of a wide range of chemicals. Honey bee Apis mellifera’s OBP14 (AmelOBP14) binds different plant odorants in a largely hydrophobic cavity. In long molecular dynamics simulations in the presence and absence of ligand eugenol, we observe a highly dynamic C-terminal region which forms one side of the ligand-binding cavity, and the ligand drifts away from its crystallized orientation. Hamiltonian replica exchange simulations, allowing exchanges of conformations sampled by the real ligand with those sampled by a noninteracting dummy molecule and several intermediates, suggest an alternative, quite different ligand pose which is adopted immediately and which is stable in long simulations. Thermodynamic integration yields binding free energies which are in reasonable agreement with experimental data.

Reaction of pyranose dehydrogenase from Agaricus meleagris with its carbohydrate substrates

Monomeric Agaricus meleagris pyranose dehydrogenase (AmPDH) belongs to the glucose-methanol-choline family of oxidoreductases. An FAD cofactor is covalently tethered to His103 of the enzyme. AmPDH can double oxidize various mono- and oligosaccharides at different positions (C1 to C4). To study the structure/function relationship of selected active-site residues of AmPDH pertaining to substrate (carbohydrate) turnover in more detail, several active-site variants were generated, heterologously expressed in Pichia pastoris, and characterized by biochemical, biophysical and computational means. The crystal structure of AmPDH shows two active-site histidines, both of which could take on the role as the catalytic base in the reductive half-reaction. Steady-state kinetics revealed that His512 is the only catalytic base because H512A showed a reduction in (kcat /KM )glucose by a factor of 10(5) , whereas this catalytic efficiency was reduced by two or three orders of magnitude for His556 variants (H556A, H556N). This was further corroborated by transient-state kinetics, where a comparable decrease in the reductive rate constant was observed for H556A, whereas the rate constant for the oxidative half-reaction (using benzoquinone as substrate) was increased for H556A compared to recombinant wild-type AmPDH. Steady-state kinetics furthermore indicated that Gln392, Tyr510, Val511 and His556 are important for the catalytic efficiency of PDH. Molecular dynamics (MD) simulations and free energy calculations were used to predict d-glucose oxidation sites, which were validated by GC-MS measurements. These simulations also suggest that van der Waals interactions are the main driving force for substrate recognition and binding.