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.
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.
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 (k cat /K M values of about 10 5 M À 1 s À 1 ) in the aqueous bulk. This meant that approximatly 70 % of the final, monoaromatic products were formed via soluble di-or triaromatic intermediates.
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 twodomain 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.
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 × 105m−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.
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