COVID‐19 is a disease caused by severe acute respiratory syndrome coronavirus 2. Presently, there is no effective treatment for COVID‐19. As part of the worldwide efforts to find efficient therapies and preventions, it has been reported the crystalline structure of the SARS‐CoV‐2 main protease M^pro (also called 3CL^pro) bound to a synthetic inhibitor, which represents a major druggable target. The druggability of M^pro could be used for discovering drugs to treat COVID‐19. A multilevel computational study was carried out to evaluate the potential antiviral properties of the components of the medicinal herb Uncaria tomentosa (Cat’s claw), focusing on the inhibition of M^pro. The in silico approach starts with protein‐ligand docking of 26 Cat’s claw key components, followed by ligand pathway calculations, molecular dynamics simulations, and MM‐GBSA calculation of the free energy of binding for the best docked candidates. The structural bioinformatics approaches led to identification of three bioactive compounds of Uncaria tomentosa (speciophylline, cadambine, and proanthocyanidin B2) with potential therapeutic effects by strong interaction with 3CL^pro. Additionally, in silico drug‐likeness indices for these components were calculated and showed good predicted therapeutic profiles of these phytochemicals. Our findings suggest the potential effectiveness of Cat’s claw as complementary and/or alternative medicine for COVID‐19 treatment.

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.

Protein–ligand docking is a widely used method to generate solutions for the binding of a small molecule with its target in a short amount of time. However, these methods provide identification of physically sound protein–ligand complexes without a complete view of the binding process dynamics, which has been recognized to be a major discriminant in binding affinity and ligand selectivity. In this paper, a novel piece of open-source software to approach this problem is presented, called GPathFinder. It is built as an extension of the modular GaudiMM platform and is able to simulate ligand diffusion pathways at atomistic level. The method has been benchmarked on a set of 20 systems whose ligand-binding routes were studied by other computational tools or suggested from experimental “snapshots”. In all of this set, GPathFinder identifies those channels that were already reported in the literature. Interestingly, the low-energy pathways in some cases indicate novel possible binding routes. To show the usefulness of GPathFinder, the analysis of three case systems is reported. We believe that GPathFinder is a software solution with a good balance between accuracy and computational cost, and represents a step forward in extending protein–ligand docking capacities, with implications in several fields such as drug or enzyme design.

In an organism, cisplatin and its derivatives are known to interact with proteins besides their principal DNA target. These off-target interactions have major therapeutic consequences including undesired side effects, loss of bioavailability and emergence of resistance. Insulin is one of the prototypical protein targets of platinum drugs as it has been seen to be involved in bioavailability reduction and might also determine resistance in certain cancer lines. However, despite the interest in understanding the nature of the oxaliplatin–insulin adducts, no 3D models have been achieved so far. In this study, we apply our recent computational multiscale protocol optimized for bioinorganic interactions to provide structural insights into these systems. To do so, the initial structures are predicted by blind protein–metalloligand docking calculations optimized to account for a metal-containing species, and then refined using a Molecular Dynamics (MD) and Quantum Mechanics/Molecular Mechanics (QM/MM) integrated protocol. The results are consistent with experimental information obtained from fragment analysis, and also provide novel structural information like conformational changes occurring upon binding and potential effects on the biological functions of the protein. This study opens an avenue towards applying similar strategies to a wide ensemble of metallodrug–protein/peptide systems for which no structural data are available.