High-Throughput Screening Assay to Identify Small Molecule Inhibitors of Marburg Virus VP40 Protein

Identification of novel inhibitors against VP40 protein of Marburg virus by integrating molecular modeling and dynamics approaches

Marburg virus (MV) is a highly etiological agent of haemorrhagic fever in humans and has spread across the world. Its outbreaks caused a 23-90% human death rate. However, there are currently no authorized preventive or curative measures yet. VP40 is the MV matrix protein, which builds protein shell underneath the viral envelope and confers hallmark filamentous. VP40 alone is able to induce assembly and budding of filamentous virus-like particles (VLPs), which resemble authentic virions. As a result, this research is credited with clarifying the function of VP40 and leading to the discovery of new therapeutic targets effective in combating MV disease (MVD). Virtual screening, molecular docking and molecular dynamics (MD) simulation were used to find the putative active chemicals based on a 3D pharmacophore model of the protein's active site cavity. Initially, andrographidine-C, a potent inhibitor was selected for the development of the pharmacophore model. Later, a library of 30,000 compounds along with the andrographidine-C was docked against VP40 protein. Three best hits including avanafil, diuvaretin and macrourone were subjected to further MD simulation analysis, as these compounds had better binding affinities as compared to andrographidine-C. Furthermore, throughout the 100 ns simulations, the back bone of VP40 protein in presence of avanafil, diuvaretin and macrourone remained stable which was further validated by MM-PBSA analysis. Additionally, all of these compounds depict maximum drug-like properties. The predicted drugs based on the ligand, avanafil, diuvaretin and macrourone could be exploited and developed as an alternative or complementary therapy for the treatment of MVD.

Development of a Crystallographic Screening to Identify Sudan Virus VP40 Ligands

The matrix protein VP40 of the highly pathogenic Sudan virus (genus Orthoebolavirus) is a multifunctional protein responsible for the recruitment of viral nucleocapsids to the plasma membrane and the budding of infectious virions. In addition to its role in assembly, VP40 also downregulates viral genome replication and transcription. VP40's existence in various homo-oligomeric states is presumed to underpin its diverse functional capabilities during the viral life cycle. Given the absence of licensed therapeutics targeting the Sudan virus, our study focused on inhibiting VP40 dimers, the structural precursors to critical higher-order oligomers, as a novel antiviral strategy. We have established a crystallographic screening pipeline for the identification of small-molecule fragments capable of binding to VP40. Dimeric VP40 of the Sudan virus was recombinantly expressed in bacteria, purified, crystallized, and soaked in a solution of 96 different preselected fragments. Salicylic acid was identified as a crystallographic hit with clear electron density in the pocket between the N- and the C-termini of the VP40 dimer. The binding interaction is predominantly coordinated by amino acid residues leucine 158 (L158) and arginine 214 (R214), which are key in stabilizing salicylic acid within the binding pocket. While salicylic acid displayed minimal impact on the functional aspects of VP40, we delved deeper into characterizing the druggability of the identified binding pocket. We analyzed the influence of residues L158 and R214 on the formation of virus-like particles and viral RNA synthesis. Site-directed mutagenesis of these residues to alanine markedly affected both VP40's budding activity and its effect on viral RNA synthesis, underscoring the potential of the salicylic acid binding pocket as a drug target. In summary, our findings lay the foundation for structure-guided drug design to provide lead compounds against Sudan virus VP40.

A high-throughput Gaussia luciferase reporter assay for screening potential gasdermin E activators against pancreatic cancer

It is discovered that activated caspase-3 tends to induce apoptosis in gasdermin E (GSDME)-deficient cells, but pyroptosis in GSDME-sufficient cells. The high GSDME expression and apoptosis resistance of pancreatic ductal adenocarcinoma (PDAC) cells shed light on another attractive strategy for PDAC treatment by promoting pyroptosis. Here we report a hGLuc-hGSDME-PCA system for high-throughput screening of potential GSDME activators against PDAC. This screening system neatly quantifies the oligomerization of GSDME-N to characterize whether pyroptosis occurs under the stimulation of chemotherapy drugs. Based on this system, ponatinib and perifosine are screened out from the FDA-approved anti-cancer drug library containing 106 compounds. Concretely, they exhibit the most potent luminescent activity and cause drastic pyroptosis in PDAC cells. Further, we demonstrate that perifosine suppresses pancreatic cancer by promoting pyroptosis via caspase-3/GSDME pathway both in vitro and in vivo. Collectively, this study reveals the great significance of hGLuc-hGSDME-PCA in identifying compounds triggering GSDME-dependent pyroptosis and developing promising therapeutic agents for PDAC.

A hybrid resampling algorithms SMOTE and ENN based deep learning models for identification of Marburg virus inhibitors

Background: Marburg virus (MARV) is a sporadic outbreak of a zoonotic disease that causes lethal hemorrhagic fever in humans. We propose a deep learning model with resampling techniques and predict the inhibitory activity of MARV from unknown compounds in the virtual screening process. Methodology & results: We applied resampling techniques to solve the imbalanced data problem. The classifier model comparisons revealed that the hybrid model of synthetic minority oversampling technique - edited nearest neighbor and artificial neural network (SMOTE-ENN + ANN) achieved better classification performance with 95% overall accuracy. The trained SMOTE-ENN+ANN hybrid model predicted as lead molecules; 25 out of 87,043 from ChemDiv, four out of 340 from ChEMBL anti-viral library, three out of 918 from Phytochemical database, and seven out of 419 from Natural products from NCI divsetIV, and 214 out of 1,12,267 from Natural compounds ZINC database for MARV. Conclusion: Our studies reveal that the proposed SMOTE-ENN + ANN hybrid model can improve overall accuracy more effectively and predict new lead molecules against MARV.