The prediction of enzyme activity in a general extend is maybe one of the main challenges nowadays in catalysis. Computer-assisted methods have been proven to be able to simulate the reaction mechanism at the atomic level of detail. However, these methods tend to be expensive to be used in a large scale as it is needed in protein engineering campaigns. To alleviate this situation, machine learning methods can help in the generation of predictive-decision models. Herein we train different regression algorithms for the prediction of the reaction energy barrier of the rate-limiting step of the hydrolysis of mono-(2-hydroxyethyl)terephthalic acid by the MHETase of Ideonella sakaiensis. As training data set we use steered QM/MM MD simulation snapshots and their corresponding pulling work values. We have explored three algorithms together with three chemical representations. As outcome, our trained models are able to predict pulling works along the steered QM/MM MD simulations with a mean absolute error below 3 kcal mol-1 and a score value above 0.90. More challenging is the prediction of the energy maximum with a single geometry. Whereas the use of the initial snapshot of the QM/MM MD trajectory as input geometry yields a very poor prediction of the reaction energy barrier, the use of an intermediate snapshot of the former trajectory brings the score value above 0.40 with a low mean absolute error (ca. 3 kcal mol-1). Altogether, in this work we have faced some initial challenges of the final goal of getting an efficient workflow for the semi-automatic prediction of enzyme-catalyzed energy barriers and catalytic efficiencies.

Navigating the mutational landscape of enzymes to alter substrate scope, stereoselectivity or catalytic performance are complex problems. While predicting the total energy of a chemical system like a protein scaffold has been solved with current computational tools, fine-tuning the catalytic properties of a given enzyme scaffold still relies on extensive trial and error experiments. We present in this work SmarTSzyme, a python-based code for the identification of residues in the enzyme that modulate the energetics of the chemical transformation at the active site to bias the selection of residues to mutate. By analysis of the simulation of the catalytic reaction, SmarTSzyme makes uses of the interaction networks along the catalytic mechanism using graph representation and clasify the residues of the enzyme based on a mutability index. Using two enzymes as showcases, we illustrate how SmarTSzyme unveils positions in the enzyme sequence that has been shown important to improve catalysis by others.

Tubulin is a heterodimeric protein composed of α- and β-subunits, which polymerize to form the cell’s microtubules. The latter are key components in mitotic spindle formation and essential targets in anticancer therapy. Compounds such as paclitaxel, tubulysins, dolastatins and synthetic analogues of these latter compounds, including cemadotin, exert their cytotoxic effects by disrupting microtubule dynamics. Previously, we reported the production and anticancer activity of a library of cemadotin analogues featuring a C-terminal tertiary amide functionalized with a variety of N-substituents, thus resulting in compounds occurring as a mixture of amide rotamers. Here we describe a comprehensive NMR and conformational study that provides new insights into the effect of the conformational equilibrium on the binding mode of the novel cemadotin analogues to the tubulin target. The conformational behavior of the isomer equilibrium of cemadotin’s terminal amide bond was investigated by TOCSY and ROESY NMR experiments, which allowed the identification and quantification of individual rotamer populations. A slow interconversion between the s-cis and s-trans amide rotamers was observed under standard NMR conditions (25 °C), indicating a significant energy barrier and conformational rigidity. Molecular docking and saturation transfer difference (STD) NMR experiments were performed with a representative analogue and tubulin to assess the binding mode. The results revealed that the s-trans rotamer is the predominant conformer in solution and exhibits a more favorable interaction with tubulin compared to the s-cis isomer, thus helping to understand the conformational requirements for an improved tubulin binding and the inhibition of the polymerization process.