Molecular Insights into the Covalent Binding of Zoxamide to the ß-Tubulin of Botrytis cinerea.

Botrytis cinerea is a fungal plant pathogen that causes significant economic losses in the agricultural industry worldwide. Fungicides that target microtubules, such as carbendazim (CBZ), diethofencarb (DEF), and zoxamide (ZOX), are widely used in crop protection against this pathogen. These groups of compounds exert their fungicidal activity by disrupting the microtubule assembly by binding to the ß-tubulin subunit, provoking cell-cycle arrest and cell death. However, with the appearance of isolates resistant to these compounds, it is necessary to search for new alternatives to control this pathogenic fungus. In this work, we gained insight into the binding and stability of these fungicides in the benzimidazole binding site of B. cinerea ß-tubulin through different computational approaches. Our molecular dynamics simulation replicas showed that R enantiomers of ZOX and its analog RH-4032 had better interaction profiles at the site compared to S enantiomers. The simulations also revealed that while the R-isomer fungicides formed H-bonds with the main chain carbonyl of V236 or the side chain residue of S314, only CBZ interacted with E198. Previous experimental data have identified key mutations in B. cinerea's ß-tubulin gene that lead to the development of resistance or, on the contrary, increased sensitivity for treatment with these fungicide compounds. In agreement with experimental findings, alchemical free energy calculations showed that E198A and E198V mutations in B. cinerea ß-tubulin have high sensitivity to (R)-ZOX, whereas the E198K mutation decreased its affinity. Similarly, the results obtained explain the resistance to CBZ of B. cinerea isolates with E198A/V/K mutations and the insensitivity of the wild-type organism to DEF. Our work provides a deeper insight into the molecular mechanism of action of these fungicides, highlighting the importance of understanding the interaction profiles to develop more effective antifungal agents.

A novel machine learning-based screening identifies statins as inhibitors of the calcium pump SERCA.

We report a novel small-molecule screening approach that combines data augmentation and machine learning to identify Food and Drug Administration (FDA)-approved drugs interacting with the calcium pump (Sarcoplasmic reticulum Ca2+-ATPase, SERCA) from skeletal (SERCA1a) and cardiac (SERCA2a) muscle. This approach uses information about small-molecule effectors to map and probe the chemical space of pharmacological targets, thus allowing to screen with high precision large databases of small molecules, including approved and investigational drugs. We chose SERCA because it plays a major role in the excitation-contraction-relaxation cycle in muscle and it represents a major target in both skeletal and cardiac muscle. The machine learning model predicted that SERCA1a and SERCA2a are pharmacological targets for seven statins, a group of FDA-approved 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors used in the clinic as lipid-lowering medications. We validated the machine learning predictions by using in vitro ATPase assays to show that several FDA-approved statins are partial inhibitors of SERCA1a and SERCA2a. Complementary atomistic simulations predict that these drugs bind to two different allosteric sites of the pump. Our findings suggest that SERCA-mediated Ca2+ transport may be targeted by some statins (e.g., atorvastatin), thus providing a molecular pathway to explain statin-associated toxicity reported in the literature. These studies show the applicability of data augmentation and machine learning-based screening as a general platform for the identification of off-target interactions and the applicability of this approach extends to drug discovery.