Reverse Docking for the Identification of Molecular Targets of Anticancer Compounds.

Molecular docking is a useful and powerful computational method for the identification of potential interactions between small molecules and pharmacological targets. In reverse docking, the ability of one or a few compounds to bind a large dataset of proteins is evaluated in silico. This strategy is useful for identifying molecular targets of orphan bioactive compounds, proposing new molecular mechanisms, finding alternative indications of drugs, or predicting drug toxicity. Herein, we describe a detailed reverse docking protocol for the identification of potential targets for 4-hydroxycoumarin (4-HC). Our results showed that RAC1 is a target of 4-HC, which partially explains the biological activities of 4-HC on cancer cells. The strategy reported here can be easily applied to other compounds and protein datasets.

Benchmark of Generic Shapes for Macrocycles.

Macrocycles target proteins that are otherwise considered undruggable because of a lack of hydrophobic cavities and the presence of extended featureless surfaces. Increasing efforts by computational chemists have developed effective software to overcome the restrictions of torsional and conformational freedom that arise as a consequence of macrocyclization. Moloc is an efficient algorithm, with an emphasis on high interactivity, and has been constantly updated since 1986 by drug designers and crystallographers of the Roche biostructural community. In this work, we have benchmarked the shape-guided algorithm using a dataset of 208 macrocycles, carefully selected on the basis of structural complexity. We have quantified the accuracy, diversity, speed, exhaustiveness, and sampling efficiency in an automated fashion and we compared them with four commercial (Prime, MacroModel, molecular operating environment, and molecular dynamics) and four open-access (experimental-torsion distance geometry with additional "basic knowledge" alone and with Merck molecular force field minimization or universal force field minimization, Cambridge Crystallographic Data Centre conformer generator, and conformator) packages. With three-quarters of the database processed below the threshold of high ring accuracy, Moloc was identified as having the highest sampling efficiency and exhaustiveness without producing thousands of conformations, random ring splitting into two half-loops, and possibility to interactively produce globular or flat conformations with diversity similar to Prime, MacroModel, and molecular dynamics. The algorithm and the Python scripts for full automatization of these parameters are freely available for academic use.

Repurposing the HCV NS3-4A protease drug boceprevir as COVID-19 therapeutics.

The rapid growth of COVID-19 cases is causing an increasing death toll and also paralyzing the world economy. De novo drug discovery takes years to move from idea and/or pre-clinic to market, and it is not a short-term solution for the current SARS-CoV-2 pandemic. Drug repurposing is perhaps the only short-term solution, while vaccination is a middle-term solution. Here, we describe the discovery path of the HCV NS3-4A protease inhibitors boceprevir and telaprevir as SARS-CoV-2 main protease (3CLpro) inhibitors. Based on our hypothesis that α-ketoamide drugs can covalently bind to the active site cysteine of the SARS-CoV-2 3CLpro, we performed docking studies, enzyme inhibition and co-crystal structure analyses and finally established that boceprevir, but not telaprevir, inhibits replication of SARS-CoV-2 and mouse hepatitis virus (MHV), another coronavirus, in cell culture. Based on our studies, the HCV drug boceprevir deserves further attention as a repurposed drug for COVID-19 and potentially other coronaviral infections as well.