Ligand-Based Discovery of a Small Molecule as Inhibitor of α-Synuclein Amyloid Formation.

α-Synuclein (α-Syn) aggregates are implicated in Parkinson's disease (PD), so inhibitors of α-Syn aggregation have been intensively explored. It has been demonstrated that small molecules might be able to reduce α-Syn aggregation in fibrils, thus exerting neuroprotective effects in models of PD. To expand our knowledge about the structural requirements for blocking the recognition process into the oligomeric assembly of α-Syn aggregates, we performed a ligand-based virtual screening procedure using two well-known α-Syn aggregation inhibitors, SynuClean-D and ZPD-2, as query compounds. A collection of thirty-four compounds bearing distinct chemical functionalities and mutual chemical features were studied in a Th-T fluorescence test, thus identifying 5-(2,6-dinitro-4-(trifluoromethyl)benzyl)-1-methyl-1H-tetrazole (named MeSC-04) as a potent α-Syn amyloid formation inhibitor that demonstrated similar behavior when compared to SynuClean-D in the thioflavin-T-monitored kinetic assays, with both molecules reducing the number and size of amyloid fibrils, as evidenced by electron microscopy. Molecular modeling studies suggested the binding mode of MeSC-04 through the identification of putative druggable pockets on α-syn fibrils and a subsequent consensus docking methodology. Overall, this work could furnish new insights in the development of α-Syn amyloid inhibitors from synthetic sources.

In Silico Insights towards the Identification of SARS-CoV-2 NSP13 Helicase Druggable Pockets.

The merging of distinct computational approaches has become a powerful strategy for discovering new biologically active compounds. By using molecular modeling, significant efforts have recently resulted in the development of new molecules, demonstrating high efficiency in reducing the replication of severe acute respiratory coronavirus 2 (SARS-CoV-2), the agent responsible for the COVID-19 pandemic. We have focused our interest on non-structural protein Nsp13 (NTPase/helicase), as a crucial protein, embedded in the replication-transcription complex (RTC), that controls the virus life cycle. To assist in the identification of the most druggable surfaces of Nsps13, we applied a combination of four computational tools: FTMap, SiteMap, Fpocket and LigandScout. These software packages explored the binding sites for different three-dimensional structures of RTC complexes (PDB codes: 6XEZ, 7CXM, 7CXN), thus, detecting several hot spots, that were clustered to obtain ensemble consensus sites, through a combination of four different approaches. The comparison of data provided new insights about putative druggable sites that might be employed for further docking simulations on druggable surfaces of Nsps13, in a scenario of repurposing drugs.