The COVID-19 pandemic has been responsible for several deaths worldwide. The causative
agent behind this disease is the Severe Acute Respiratory Syndrome – novel Coronavirus 2
(SARS-CoV-2). SARS-CoV-2 belongs to the category of RNA viruses. The main protease,
responsible for the cleavage of the viral polyprotein is considered as one of the hot
targets for treating COVID-19. Earlier reports suggest the use of HIV anti-viral drugs for
targeting the main protease of SARS-CoV, which caused SARS in the year 2002–2003. Hence,
drug repurposing approach may prove to be useful in targeting the main protease of
SARS-CoV-2. The high-resolution crystal structure of the main protease of SARS-CoV-2 (PDB
ID: 6LU7) was used as the target. The Food and Drug Administration approved and SWEETLEAD
database of drug molecules were screened. The apo form of the main protease was simulated
for a cumulative of 150 ns and 10 μs open-source simulation data was used, to obtain
conformations for ensemble docking. The representative structures for docking were
selected using RMSD-based clustering and Markov State Modeling analysis. This ensemble
docking approach for the main protease helped in exploring the conformational variation in
the drug-binding site of the main protease leading to the efficient binding of more
relevant drug molecules. The drugs obtained as top hits from the ensemble docking
possessed anti-bacterial and anti-viral properties. This
in
silico
ensemble docking approach would support the identification of potential
candidates for repurposing against COVID-19.
Communicated by Ramaswamy H. Sarma
Slug, a five C2H2 zinc finger (ZF) motif transcription factor mediates cell migration in development, adult tissue repair and regeneration, as well as during tumor metastases through epithelial to mesenchymal transition. At the molecular level, this involves interactions with E-box (CACC/GGTG) consensus elements within target gene promoters to achieve transcriptional repression. However, precise elucidation of events involved in this DNA recognition and binding of specific promoters to regulate target genes have not been achieved. In the present study, we show that besides transcriptional repression, Slug can also directly activate its own expression by preferential binding to specific E-box elements in the distal binding region of its promoter. Our findings suggest that while the first ZF does not contribute to the transcription-associated functions of Slug, all the remaining four ZFs are involved in regulating the expression of target genes with ZF3 and ZF4 being more crucial than ZF2 or ZF5. We also report that recognition and binding preferences of ZFs are defined through intrinsic differences in the E-box core base pairs and/or flanking sequences, with the S2 E-box element being most critical during autoregulation. However, specific target E-box recognition and binding are also defined by the cellular context, which implies that in silico and/or biochemical DNA binding preferences may not necessarily be able to accurately predict in situ events. Our studies thus constitute a novel understanding of transcriptional regulation.
<p>The
COVID-19 pandemic has been responsible for several deaths worldwide. The
causative agent behind this disease is the Severe Acute Respiratory Syndrome –
novel Coronavirus 2 (SARS-nCoV2). SARS-nCoV2 belongs to the category of RNA
viruses. The main protease, responsible for the cleavage of the viral
polyprotein is considered as one of the hot targets for treating COVID-19.
Earlier reports suggest the use of HIV anti-viral drugs for targeting the main
protease of SARS-CoV, which caused SARS in the year 2002-03. Hence, drug
repurposing approach may prove to be useful in targeting the main protease of
SARS-nCoV2. The high-resolution crystal structure of 3CL<sup>pro</sup> (main protease) of
SARS-nCoV2 (PDB ID: 6LU7) was used as the target. The Food and Drug
Administration (FDA) approved and SWEETLEAD database of drug molecules were
screened. The apo form of the main protease was simulated for a cumulative of
150 ns and 10 μs open source simulation data was used, to obtain conformations
for ensemble docking. The representative structures for docking were selected using
RMSD-based clustering and Markov State Modeling analysis. This ensemble docking
approach for main protease helped in exploring the conformational variation in
the drug binding site of the main protease leading to efficient binding of more
relevant drug molecules. The drugs obtained as best hits from the ensemble
docking possessed anti-bacterial and anti-viral properties. Small molecules
with these properties may prove to be useful to treat symptoms exhibited in
COVID-19. This <i>in-silico</i> ensemble docking approach would support identification
of potential candidates for repurposing against COVID-19.</p>
relationship between the quantum chemical descriptors and the experimental data, which may be useful in designing new molecules or modifications tuned for specific requirements of antisense molecules.
Lead optimization is one of the crucial steps in the drug discovery pipeline. After identifying the lead molecule and obtaining its 2D geometry, understanding the best conformation it would attain in 3D still remains one of the most challenging steps in drug discovery. There have been multiple methods and algorithms that are directed toward achieving best conformation for the lead molecules. TANGO focuses on conformation generation and its optimization using semiempirical energy calculations. The conformation generation is based on torsion angle rotation of the exocyclic bonds. The energy calculations are performed using MOPAC. The unique feature of this tool lies in the implementation of Message Passing Interface (MPI) for conformation generation and semiempirical-based optimization. A well-defined architecture handling the input and output generation has been used. The master and slave approach to handle operations involved in torsion angle rotation and energy calculations has helped in load balancing the process of conformation generation. The benchmarking results suggest that TANGO scales significantly well across eight nodes with each node utilizing 16 cores. This tool may prove to very useful in high throughput generation of semiempirically optimized small molecule conformations. The use of semiempirical methods for optimization generates a conformational ensemble thereby helping to obtain stable and alternate stable conformers for a given ligand molecule.
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