Repurposing FIASMAs against Acid Sphingomyelinase for COVID-19: A Computational Molecular Docking and Dynamic Simulation Approach.

Over the past few years, COVID-19 has caused widespread suffering worldwide. There is great research potential in this domain and it is also necessary. The main objective of this study was to identify potential inhibitors against acid sphingomyelinase (ASM) in order to prevent coronavirus infection. Experimental studies revealed that SARS-CoV-2 causes activation of the acid sphingomyelinase/ceramide pathway, which in turn facilitates the viral entry into the cells. The objective was to inhibit acid sphingomyelinase activity in order to prevent the cells from SARS-CoV-2 infection. Previous studies have reported functional inhibitors against ASM (FIASMAs). These inhibitors can be exploited to block the entry of SARS-CoV-2 into the cells. To achieve our objective, a drug library containing 257 functional inhibitors of ASM was constructed. Computational molecular docking was applied to dock the library against the target protein (PDB: 5I81). The potential binding site of the target protein was identified through structural alignment with the known binding pocket of a protein with a similar function. AutoDock Vina was used to carry out the docking steps. The docking results were analyzed and the inhibitors were screened based on their binding affinity scores and ADME properties. Among the 257 functional inhibitors, Dutasteride, Cepharanthine, and Zafirlukast presented the lowest binding affinity scores of -9.7, -9.6, and -9.5 kcal/mol, respectively. Furthermore, computational ADME analysis of these results revealed Cepharanthine and Zafirlukast to have non-toxic properties. To further validate these findings, the top two inhibitors in complex with the target protein were subjected to molecular dynamic simulations at 100 ns. The molecular interactions and stability of these compounds revealed that these inhibitors could be a promising tool for inhibiting SARS-CoV-2 infection.

Targeted exome sequencing identified a novel frameshift variant in the PGAM2 gene causing glycogen storage disease type X.

BACKGROUND: Phosphoglycerate mutase (PGAM) deficiency is associated with a rare glycogen storage disease (glycogenosis type X) in humans caused by pathogenic variants in the PGAM2 gene. Several genes causing autosomal forms of glycogen storage disease (GSD) have been identified, involved in various forms of neuromuscular anomalies. METHODS: Targeted whole exome sequencing (WES) was performed on the DNA of single affected individual (IV-1) followed by Sanger sequencing confirmation of the identified variant in all available members of the family. RESULTS: In the present study, the affected individual, presenting mild features of glycogen storage disease type X. Targeted exome sequencing revealed a biallelic frameshift variant (c.687dupC; p. Met230Hisfs*6) in the PGAM2 gene located on chromosome 7p13. CONCLUSION: In short, we reported a novel homozygous frameshift variant as a cause of glycogen storage disease type X from Pakistani population. The work presented here proves significance of targeted WES in accurate diagnosis of known complex genetic disorders.

Combining fragment docking with graph theory to improve ligand docking for homology model structures.

Computational protein-ligand docking is well-known to be prone to inaccuracies in input receptor structures, and it is challenging to obtain good docking results with computationally predicted receptor structures (e.g. through homology modeling). Here we introduce a fragment-based docking method and test if it reduces requirements on the accuracy of an input receptor structures relative to non-fragment docking approaches. In this method, small rigid fragments are docked first using AutoDock Vina to generate a large number of favorably docked poses spanning the receptor binding pocket. Then a graph theory maximum clique algorithm is applied to find combined sets of docked poses of different fragment types onto which the complete ligand can be properly aligned. On the basis of these alignments, possible binding poses of complete ligand are determined. This docking method is first tested for bound docking on a series of Cytochrome P450 (CYP450) enzyme-substrate complexes, in which experimentally determined receptor structures are used. For all complexes tested, ligand poses of less than 1 Å root mean square deviations (RMSD) from the actual binding positions can be recovered. Then the method is tested for unbound docking with modeled receptor structures for a number of protein-ligand complexes from different families including the very recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protease. For all complexes, poses with RMSD less than 3 Å from actual binding positions can be recovered. Our results suggest that for docking with approximately modeled receptor structures, fragment-based methods can be more effective than common complete ligand docking approaches.

Comparative modeling and virtual screening for the identification of novel inhibitors for myo-inositol-1-phosphate synthase.

Myo-inositol-1-phosphate (MIP) synthase is a key enzyme in the myo-inositol biosynthesis pathway. Disruption of the inositol signaling pathway is associated with bipolar disorders. Previous work suggested that MIP synthase could be an attractive target for the development of anti-bipolar drugs. Inhibition of this enzyme could possibly help in reducing the risk of a disease in patients. With this objective, three dimensional structure of the protein was modeled followed by the active site prediction. For the first time, computational studies were carried out to obtain structural insights into the interactive behavior of this enzyme with ligands. Virtual screening was carried out using FILTER, ROCS and EON modules of the OpenEye scientific software. Natural products from the ZINC database were used for the screening process. Resulting compounds were docked into active site of the target protein using FRED (Fast Rigid Exhaustive Docking) and GOLD (Genetic Optimization for Ligand Docking) docking programs. The analysis indicated extensive hydrogen bonding network and hydrophobic interactions which play a significant role in ligand binding. Four compounds are shortlisted and their binding assay analysis is underway.