Targeting SARS-CoV-2 non-structural protein 13 via helicase-inhibitor-repurposing and non-structural protein 16 through pharmacophore-based screening.

Novel drug compound hunting was carried out for SARS-CoV-2 proteins with low mutation susceptibility. The probability of escape mutation and drug resistance is lower if conserved microbial proteins are targeted by therapeutic drugs. Mutation rate of all SARS-CoV-2 proteins were analyzed via multiple sequence alignment Non-Structural Protein 13 and Non-Structural Protein 16 were selected for the current study due to low mutation rate among viral strains and significant functionality. Cross-species mutation rate analysis for NSP13 and NSP16 showed these are well-conserved proteins among four coronaviral species. Viral helicase inhibitors, identified using literature-mining, were docked against NSP13. Pharmacophore-based screening of 11,375 natural compounds was conducted for NSP16. Stabilities of top compounds inside human body were confirmed via molecular dynamic simulation. ADME properties and LD50 values of the helicase inhibitors and Ambinter natural compounds were analyzed. Compounds against NSP13 showed binding affinities between -10 and -5.9 kcal/mol whereby ivermectin and scutellarein showed highest binding energies of -10 and -9.9 kcal/mol. Docking of 18 hit compounds against NSP16 yielded binding affinities between -8.9 and -4.1 kcal/mol. Hamamelitannin and deacyltunicamycin were the top compounds with binding affinities of -8.9 kcal/mol and -8.4 kcal/mol. The top compounds showed stable ligand-protein interactions in molecular dynamics simulation. The analyses revealed two hit compounds against each targeted protein displaying stable behavior, high binding affinity and molecular interactions. Conversion of these compounds into drugs after in vitro experimentation can become better treatment options to elevate COVID management.

High expression of bone morphogenetic protein 1 (BMP1) is associated with a poor survival rate in human gastric cancer, a dataset approaches.

Bone morphogenetic protein 1 (BMP1) is a secreted metalloprotease of the astacin M12A family of bone morphogenetic proteins (BMPs). BMP1 activates transforming growth factor-ß (TGF-ß) and BMP signaling pathways by proteolytic cleavage, which has dual roles in gastrointestinal tumor development and progression.TGF-ß promotes invasion and metastasis of gastric cancer (GC) by the help of BMP1, so upregulation of the BMP1 may increase cancer invasiveness in GC. In this study,the transcriptional expression, mutations, survival rate, TFs, miRNAs, gene ontology, and signaling pathways of BMP1 were analyzed by using different web servers. We found higher transcriptional and clinicopathological characteristics expression compared to normal tissues, worsening survival rate in GC. We detected 25 missenses, 15 truncating mutations, 23 TFs, and 8 miRNAs. Finally, we identified and analyzed the co-expressed genes and found that the leukemia inhibitory factor is the most positively correlated gene. The gene ontological features and signaling pathways involved in GC development were evaluated as well. We believe that this study will provide a basis for BMP1 to be a significant biomarker for human GC prognosis.

Pharmacophore mapping approach to find anti-cancer phytochemicals with metformin-like activities against transforming growth factor (TGF)-beta receptor I kinase: An in silico study.

The most frequently prescribed first-line treatment for type II diabetes mellitus is metformin. Recent reports asserted that this diabetes medication can also shield users from cancer. Metformin induces cell cycle arrest in cancer cells. However, the exact mechanism by which this occurs in the cancer system is yet to be elucidated. Here, we investigated the impact of metformin on cell cycle arrest in cancer cells utilizing transforming growth factor (TGF)-beta pathway. TGF-ß pathway has significant effect on cell progression and growth. In order to gain an insight on the underlying molecular mechanism of metformin's effect on TGF beta receptor 1 kinase, molecular docking was performed. Metformin was predicted to interact with transforming growth factor (TGF)-beta receptor I kinase based on molecular docking and molecular dynamics simulations. Furthermore, pharmacophore was generated for metformin-TGF-ßR1 complex to hunt for novel compounds having similar pharmacophore as metformin with enhanced anti-cancer potentials. Virtual screening with 29,000 natural compounds from NPASS database was conducted separately for the generated pharmacophores in Ligandscout® software. Pharmacophore mapping showed 60 lead compounds for metformin-TGF-ßR1 complex. Molecular docking, molecular dynamics simulation for 100 ns and ADMET analysis were performed on these compounds. Compounds with CID 72473, 10316977 and 45140078 showed promising binding affinities and formed stable complexes during dynamics simulation with aforementioned protein and thus have potentiality to be developed into anti-cancer medicaments.