Reaction Pathways for the Enzymatic Degradation of Poly(Ethylene Terephthalate): What Characterizes an Efficient PET-Hydrolase?

Bioprocessing of polyester waste has emerged as a promising tool in the quest for a cyclic plastic economy. One key step is the enzymatic breakdown of the polymer, and this entails a complicated pathway with substrates, intermediates, and products of variable size and solubility. We have elucidated this pathway for poly(ethylene terephthalate) (PET) and four enzymes. Specifically, we combined different kinetic measurements and a novel stochastic model and found that the ability to hydrolyze internal bonds in the polymer (endo-lytic activity) was a key parameter for overall enzyme performance. Endo-lytic activity promoted the release of soluble PET fragments with two or three aromatic rings, which, in turn, were broken down with remarkable efficiency (kcat /KM values of about 105  M-1 s-1 ) in the aqueous bulk. This meant that approximatly 70 % of the final, monoaromatic products were formed via soluble di- or tri-aromatic intermediates.

Replica-Exchange Enveloping Distribution Sampling Using Generalized AMBER Force-Field Topologies: Application to Relative Hydration Free-Energy Calculations for Large Sets of Molecules.

Free-energy differences between pairs of end-states can be estimated based on molecular dynamics (MD) simulations using standard pathway-dependent methods such as thermodynamic integration (TI), free-energy perturbation, or Bennett's acceptance ratio. Replica-exchange enveloping distribution sampling (RE-EDS), on the other hand, allows for the sampling of multiple end-states in a single simulation without the specification of any pathways. In this work, we use the RE-EDS method as implemented in GROMOS together with generalized AMBER force-field (GAFF) topologies, converted to a GROMOS-compatible format with a newly developed GROMOS++ program amber2gromos, to compute relative hydration free energies for a series of benzene derivatives. The results obtained with RE-EDS are compared to the experimental data as well as calculated values from the literature. In addition, the estimated free-energy differences in water and in vacuum are compared to values from TI calculations carried out with GROMACS. The hydration free energies obtained using RE-EDS for multiple molecules are found to be in good agreement with both the experimental data and the results calculated using other free-energy methods. While all considered free-energy methods delivered accurate results, the RE-EDS calculations required the least amount of total simulation time. This work serves as a validation for the use of GAFF topologies with the GROMOS simulation package and the RE-EDS approach. Furthermore, the performance of RE-EDS for a large set of 28 end-states is assessed with promising results.

Substrate binding in the processive cellulase Cel7A: Transition state of complexation and roles of conserved tryptophan residues.

Cellobiohydrolases effectively degrade cellulose and are of biotechnological interest because they can convert lignocellulosic biomass to fermentable sugars. Here, we implemented a fluorescence-based method for real-time measurements of complexation and decomplexation of the processive cellulase Cel7A and its insoluble substrate, cellulose. The method enabled detailed kinetic and thermodynamic analyses of ligand binding in a heterogeneous system. We studied WT Cel7A and several variants in which one or two of four highly conserved Trp residues in the binding tunnel had been replaced with Ala. WT Cel7A had on/off-rate constants of 1 × 105 m-1 s-1 and 5 × 10-3 s-1, respectively, reflecting the slow dynamics of a solid, polymeric ligand. Especially the off-rate constant was many orders of magnitude lower than typical values for small, soluble ligands. Binding rate and strength both were typically lower for the Trp variants, but effects of the substitutions were moderate and sometimes negligible. Hence, we propose that lowering the activation barrier for complexation is not a major driving force for the high conservation of the Trp residues. Using so-called Φ-factor analysis, we analyzed the kinetic and thermodynamic results for the variants. The results of this analysis suggested a transition state for complexation and decomplexation in which the reducing end of the ligand is close to the tunnel entrance (near Trp-40), whereas the rest of the binding tunnel is empty. We propose that this structure defines the highest free-energy barrier of the overall catalytic cycle and hence governs the turnover rate of this industrially important enzyme.

Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations.

Parametrization of small organic molecules for classical molecular dynamics simulations is not trivial. The vastness of the chemical space makes approaches using building blocks challenging. The most common approach is therefore an individual parametrization of each compound by deriving partial charges from semiempirical or ab initio calculations and inheriting the bonded and van der Waals (Lennard-Jones) parameters from a (bio)molecular force field. The quality of the partial charges generated in this fashion depends on the level of the quantum-chemical calculation as well as on the extraction procedure used. Here, we present a machine learning (ML) based approach for predicting partial charges extracted from density functional theory (DFT) electron densities. The training set was chosen with the goal to provide a broad coverage of the known chemical space of druglike molecules. In addition to the speed of the approach, the partial charges predicted by ML are not dependent on the three-dimensional conformation in contrast to the ones obtained by fitting to the electrostatic potential (ESP). To assess the quality and compatibility with standard force fields, we performed benchmark calculations for the free energy of hydration and liquid properties such as density and heat of vaporization.

Structure-activity relationship of GPR15L peptide analogues and investigation of their interaction with the GPR15 receptor.

The 57-mer full-length GPR15L(25-81) peptide has been identified as the principal endogenous agonist of the G protein-coupled receptor GPR15. Its main activity resides in the C-terminal 11-mer GPR15L(71-81), which has full efficacy but ~40-fold lower potency than the full-length peptide. Here, we systematically investigated the structure-activity relationship of GPR15L(71-81) by truncations/extensions, alanine-scanning, and N- and C-terminal capping. The synthesized peptide analogues were tested at GPR15 stably expressed in HEK293A cells using a homogenous time-resolved Förster resonance energy transfer-based Gi cAMP functional assay. We show that the C-terminal α carboxyl group and the residues Leu78 , Pro75 , Val74 , and Trp72 are critical for receptor interaction and contribute significantly to the peptide potency. Furthermore, we tested the ability of GPR15L(71-81), C-terminally amidated GPR15L(71-81), and GPR15L(25-81) to activate the three GPR15 receptor mutants in a bioluminescence resonance energy transfer-based G protein activation assay. The results demonstrate that the Lys192 and Glu272 residues in GPR15 are important for the potency of the GPR15L peptide. Overall, our study identifies critical residues in the peptide and receptor sequences for future drug design.

The Sabatier principle as a tool for discovery and engineering of industrial enzymes.

The recent breakthrough in all-atom, protein structure prediction opens new avenues for a range of computational approaches in enzyme design. These new approaches could become instrumental for the development of technical biocatalysts, and hence our transition toward more sustainable industries. Here, we discuss one approach, which is well-known within inorganic catalysis, but essentially unexploited in biotechnology. Specifically, we review examples of linear free-energy relationships (LFERs) for enzyme reactions and discuss how LFERs and the associated Sabatier Principle may be implemented in algorithms that estimate kinetic parameters and enzyme performance based on model structures.

Computing Cellulase Kinetics with a Two-Domain Linear Interaction Energy Approach.

While heterogeneous enzyme reactions play an essential role in both nature and green industries, computational predictions of their catalytic properties remain scarce. Recent experimental work demonstrated the applicability of the Sabatier principle for heterogeneous biocatalysis. This provides a simple relationship between binding strength and the catalytic rate and potentially opens a new way for inexpensive computational determination of kinetic parameters. However, broader implementation of this approach will require fast and reliable prediction of binding free energies of complex two-phase systems, and computational procedures for this are still elusive. Here, we propose a new framework for the assessment of the binding strengths of multidomain proteins, in general, and interfacial enzymes, in particular, based on an extended linear interaction energy (LIE) method. This two-domain LIE (2D-LIE) approach was successfully applied to predict binding and activation free energies of a diverse set of cellulases and resulted in robust models with high accuracy. Overall, our method provides a fast computational screening tool for cellulases that have not been experimentally characterized, and we posit that it may also be applicable to other heterogeneously acting biocatalysts.

Physical constraints and functional plasticity of cellulases.

Enzyme reactions, both in Nature and technical applications, commonly occur at the interface of immiscible phases. Nevertheless, stringent descriptions of interfacial enzyme catalysis remain sparse, and this is partly due to a shortage of coherent experimental data to guide and assess such work. In this work, we produced and kinetically characterized 83 cellulases, which revealed a conspicuous linear free energy relationship (LFER) between the substrate binding strength and the activation barrier. The scaling occurred despite the investigated enzymes being structurally and mechanistically diverse. We suggest that the scaling reflects basic physical restrictions of the hydrolytic process and that evolutionary selection has condensed cellulase phenotypes near the line. One consequence of the LFER is that the activity of a cellulase can be estimated from its substrate binding strength, irrespectively of structural and mechanistic details, and this appears promising for in silico selection and design within this industrially important group of enzymes.

Removal of N-linked glycans in cellobiohydrolase Cel7A from Trichoderma reesei reveals higher activity and binding affinity on crystalline cellulose.

BACKGROUND: Cellobiohydrolase from glycoside hydrolase family 7 is a major component of commercial enzymatic mixtures for lignocellulosic biomass degradation. For many years, Trichoderma reesei Cel7A (TrCel7A) has served as a model to understand structure-function relationships of processive cellobiohydrolases. The architecture of TrCel7A includes an N-glycosylated catalytic domain, which is connected to a carbohydrate-binding module through a flexible, O-glycosylated linker. Depending on the fungal expression host, glycosylation can vary not only in glycoforms, but also in site occupancy, leading to a complex pattern of glycans, which can affect the enzyme's stability and kinetics. RESULTS: Two expression hosts, Aspergillus oryzae and Trichoderma reesei, were utilized to successfully express wild-types TrCel7A (WT Ao and WT Tr ) and the triple N-glycosylation site deficient mutants TrCel7A N45Q, N270Q, N384Q (ΔN-glyc Ao and ΔN-glyc Tr ). Also, we expressed single N-glycosylation site deficient mutants TrCel7A (N45Q Ao , N270Q Ao , N384Q Ao ). The TrCel7A enzymes were studied by steady-state kinetics under both substrate- and enzyme-saturating conditions using different cellulosic substrates. The Michaelis constant (K M ) was consistently found to be lowered for the variants with reduced N-glycosylation content, and for the triple deficient mutants, it was less than half of the WTs' value on some substrates. The ability of the enzyme to combine productively with sites on the cellulose surface followed a similar pattern on all tested substrates. Thus, site density (number of sites per gram cellulose) was 30-60% higher for the single deficient variants compared to the WT, and about twofold larger for the triple deficient enzyme. Molecular dynamic simulation of the N-glycan mutants TrCel7A revealed higher number of contacts between CD and cellulose crystal upon removal of glycans at position N45 and N384. CONCLUSIONS: The kinetic changes of TrCel7A imposed by removal of N-linked glycans reflected modifications of substrate accessibility. The presence of N-glycans with extended structures increased K M and decreased attack site density of TrCel7A likely due to steric hindrance effect and distance between the enzyme and the cellulose surface, preventing the enzyme from achieving optimal conformation. This knowledge could be applied to modify enzyme glycosylation to engineer enzyme with higher activity on the insoluble substrates.

Discovery of Novel Molecular Frameworks of Farnesoid†X Receptor Modulators by Ensemble Machine Learning.

Invited for this month's cover picture is the group of Prof. Dr. Gisbert Schneider from the Swiss Federal Institute of Technology (ETH) Zurich (Switzerland). The cover picture illustrates the application of machine-learning methods to expand the chemical space of farnesoid X receptor (FXR)-targeting small molecules, by employing an ensemble of three complementary machine-learning approaches (counter-propagation artificial neural network, k-nearest neighbor learner, and three-dimensional pharmacophore model). Read the full text of their Full Paper at 10.1002/open.201800156.

Discovery of Novel Molecular Frameworks of Farnesoid†X Receptor Modulators by Ensemble Machine Learning.

The bile acid activated transcription factor farnesoid X receptor (FXR) has revealed therapeutic potential as a molecular drug target for the treatment of hepatic and metabolic disorders. Despite strong efforts in FXR ligand development, the structural diversity among the known FXR modulators is limited. Only four molecular frameworks account for more than 50 % of the FXR modulators annotated in ChEMBL. Here, we leverage machine learning methods to expand the chemical space of FXR-targeting small molecules by employing an ensemble of three complementary machine learning approaches. A counter-propagation artificial neural network, a k-nearest neighbor learner, and a three-dimensional pharmacophore descriptor were combined to retrieve novel FXR ligands from a collection of more than 3 million compounds. The ensemble machine learning model identified six new FXR modulators among ten top-ranked candidates. These active hits comprise both FXR activators and antagonists with micromolar potencies. With four novel FXR ligand scaffolds, these computationally identified bioactive compounds appreciably expand the chemical space of known FXR modulators and may serve as starting points for hit-to-lead expansion.