Prediction and expression analysis of deleterious nonsynonymous SNPs of Arabidopsis ACD11 gene by combining computational algorithms and molecular docking approach.

Accelerated cell death 11 (ACD11) is an autoimmune gene that suppresses pathogen infection in plants by preventing plant cells from becoming infected by any pathogen. This gene is widely known for growth inhibition, premature leaf chlorosis, and defense-related programmed cell death (PCD) in seedlings before flowering in Arabidopsis plant. Specific amino acid changes in the ACD11 protein's highly conserved domains are linked to autoimmune symptoms including constitutive defensive responses and necrosis without pathogen awareness. The molecular aspect of the aberrant activity of the ACD11 protein is difficult to ascertain. The purpose of our study was to find the most deleterious mutation position in the ACD11 protein and correlate them with their abnormal expression pattern. Using several computational methods, we discovered PCD vulnerable single nucleotide polymorphisms (SNPs) in ACD11. We analysed the RNA-Seq data, identified the detrimental nonsynonymous SNPs (nsSNP), built genetically mutated protein structures and used molecular docking to assess the impact of mutation. Our results demonstrated that the A15T and A39D mutations in the GLTP domain were likely to be extremely detrimental mutations that inhibit the expression of the ACD11 protein domain by destabilizing its composition, as well as disrupt its catalytic effectiveness. When compared to the A15T mutant, the A39D mutant was more likely to destabilize the protein structure. In conclusion, these mutants can aid in the better understanding of the vast pool of PCD susceptibilities connected to ACD11 gene GLTP domain activation.

Main protease inhibitors and drug surface hotspots for the treatment of COVID-19: A drug repurposing and molecular docking approach.

Here, drug repurposing and molecular docking were employed to screen approved MPP inhibitors and their derivatives to suggest a specific therapeutic agent for the treatment of COVID-19. The approved MPP inhibitors against HIV and HCV were prioritized, while RNA dependent RNA Polymerase (RdRp) inhibitor remdesivir including Favipiravir, alpha-ketoamide were studied as control groups. The target drug surface hotspot was also investigated through the molecular docking technique. Molecular dynamics was performed to determine the binding stability of docked complexes. Absorption, distribution, metabolism, and excretion analysis was conducted to understand the pharmacokinetics and drug-likeness of the screened MPP inhibitors. The results of the study revealed that Paritaprevir (-10.9 kcal/mol) and its analog (CID 131982844) (-16.3 kcal/mol) showed better binding affinity than the approved MPP inhibitors compared in this study, including remdesivir, Favipiravir, and alpha-ketoamide. A comparative study among the screened putative MPP inhibitors revealed that the amino acids T25, T26, H41, M49, L141, N142, G143, C145, H164, M165, E166, D187, R188, and Q189 are at potentially critical positions for being surface hotspots in the MPP of SARS-CoV-2. The top 5 predicted drugs (Paritaprevir, Glecaprevir, Nelfinavir, and Lopinavir) and the topmost analog showed conformational stability in the active site of the SARS-CoV-2 MP protein. The study also suggested that Paritaprevir and its analog (CID 131982844) might be effective against SARS-CoV-2. The current findings are limited to in silico analysis and lack in vivo efficacy testing; thus, we strongly recommend a quick assessment of Paritaprevir and its analog (CID 131982844) in a clinical trial.