Substrates associate and products dissociate from enzyme catalytic sites rapidly, which hampers investigations of their trajectories. The high-resolution structure of the native Hordeum exo-hydrolase HvExoI isolated from seedlings reveals that non-covalently trapped glucose forms a stable enzyme-product complex. Here, we report that the alkyl β- d -glucoside and methyl 6-thio-β-gentiobioside substrate analogues perfused in crystalline HvExoI bind across the catalytic site after they displace glucose, while methyl 2-thio-β-sophoroside attaches nearby. Structural analyses and multi-scale molecular modelling of nanoscale reactant movements in HvExoI reveal that upon productive binding of incoming substrates, the glucose product modifies its binding patterns and evokes the formation of a transient lateral cavity, which serves as a conduit for glucose departure to allow for the next catalytic round. This path enables substrate-product assisted processive catalysis through multiple hydrolytic events without HvExoI losing contact with oligo- or polymeric substrates. We anticipate that such enzyme plasticity could be prevalent among exo-hydrolases.

The development of artificial enzymes aims at expanding the scope of biocatalysis. Over recent years, artificial metalloenzymes based on the insertion of homogeneous catalysts in biomolecules have received an increasing amount of attention. Rational or pseudorational design of these composites is a challenging task because of the complexity of the identification of efficient complementarities among the cofactor, the substrate, and the biological partner. Molecular modeling represents an interesting alternative to help in this task. However, little attention has been paid to this field so far. In this manuscript, we aim at reviewing our efforts in developing strategies efficient to computationally drive the design of artificial metalloenzymes. From protein−ligand dockings to multiscale approaches, we intend to demonstrate that modeling could be useful at the different steps of the design. This Perspective ultimately aims at providing computational chemists with illustration of the applications of their tools for artificial metalloenzymes and convincing enzyme designers of the capabilities, qualitative and quantitative, of computational methodologies.

Open-source workflows have become more and more an integral part of computer-aided drug design (CADD) projects since they allow reproducible and shareable research that can be easily transferred to other projects. Setting up, understanding, and applying such workflows involves either coding or using workflow managers that offer a graphical user interface. We previously reported the TeachOpenCADD teaching platform that provides interactive Jupyter Notebooks (talktorials) on central CADD topics using open-source data and Python packages. Here we present the conversion of these talktorials to KNIME workflows that allow users to explore our teaching material without any line of code. TeachOpenCADD KNIME workflows are freely available on the KNIME Hub: https://hub.knime.com/volkamerlab/space/TeachOpenCADD.

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