The rapid geographic expansion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the infectious agent of Coronavirus Disease 2019 (COVID-19) pandemic, poses an immediate need for potent drugs that can help contain the outbreak. Enveloped viruses infect the host cell by cellular membrane fusion, a crucial mechanism required for virus replication. The SARS-CoV-2 spike glycoprotein, due to its primary interaction with the human angiotensin-converting enzyme 2 (ACE2) cell-surface receptor, is considered as a potential target for drug development. Based on in silico screening followed by in vitro studies, here we report that the existing FDA-approved Bcr-Abl tyrosine kinase inhibitor, imatinib, inhibits SARS-CoV-2 with an IC50 of 130 nM. We provide initial evidence that inhibition of virus fusion may explain the antiviral action of imatinib. This finding is significant since pinpointing the mode of action allows evaluating the drug's affinity to the SARS-CoV-2-specific target protein, and in turn, helps make inferences on the potency of the drug and evidence-based recommendations on its dosage. To this end, we provide evidence that imatinib binds to the receptor-binding domain (RBD) of SARS-CoV-2 spike protein with an affinity at micromolar, i.e., 2.32 ± 0.9 µM, levels. We also show that imatinib inhibits other coronaviruses, SARS-CoV and MERS-CoV, possibly via fusion inhibition. Based on promising in vitro results, we propose the Abl kinase inhibitor (ATKI), imatinib, to be a viable repurposable drug candidate for further clinical validation against COVID-19.

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Pharmacophore modeling is an important step in computer-aided drug design for identifying interaction points between the receptor and ligand complex. Pharmacophore-based models can be used for de novo drug design, lead identification, and optimization in virtual screening as well as for multi-target drug design. There is a need to develop a user-friendly interface to filter the pharmacophore points resulting from multiple ligand conformations. Here, we present ELIXIR-A, a Python-based pharmacophore refinement tool, to help refine the pharmacophores between multiple ligands from multiple receptors. Furthermore, the output can be easily used in virtual pharmacophore-based screening platforms, thereby contributing to the development of drug discovery.

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Citrus greening, also known as Huanglongbing (HLB), is caused by the unculturable bacterium Candidatus Liberibacter spp. (e.g., CLas), and has caused a devastating decline in citrus production in many areas of the world. As of yet, there are no definitive treatments for controlling the disease. Antimicrobial peptides (AMPs) that have the potential to block secretion-dependent effector proteins at the outer-membrane domains were screened in silico. Predictions of drug-receptor interactions were built using multiple in silico techniques, including molecular docking analysis, molecular dynamics, molecular mechanics generalized Born surface area analysis, and principal component analysis. The efflux pump TolC of the Type 1 secretion system interacted with natural bacteriocin plantaricin JLA-9, blocking the β barrel. The trajectory-based principal component analysis revealed the possible binding mechanism of the peptides. Furthermore, in vitro assays using two closely related culturable surrogates of CLas (Liberibacter crescens and Rhizobium spp.) showed that Plantaricin JLA-9 and two other screened AMPs inhibited bacterial growth and caused mortality. The findings contribute to designing effective therapies to manage plant diseases associated with Candidatus Liberibacter spp.