Crystallographic mining driven computer-guided approach to identify the ASK1 inhibitor likely to perturb the catalytic region.

The pathological levels of reactive oxygen species (ROS) and oxidative stress has been recognized as a critical driver for inflammatory disorders. Apoptosis signal-regulating kinase 1 (ASK1) has been reported to be activated by intracellular ROS and its inhibition leads to a down regulation of p38-and JNK-dependent signaling. ASK1 inhibitors are reported to have the potential to treat clinically important inflammatory pathologies including liver, pulmonary and renal disorders. In view of its biological and pathological significance, inhibition of ASK1 with small molecules has been pursued as an attractive strategy to combat human diseases such as non-alcoholic steatohepatitis (NASH). Despite several ASK1 inhibitors being developed, the failure in Phase 3 clinical trials of most advanced candidate selonsertib's, underscores to discover therapeutic agents with diverse chemical moiety. Here, by using structural pharmacophore and enumeration strategy on mining co-crystals of ASK1, different scaffolds were generated to enhance the chemical diversity keeping the critical molecular interaction in the catalytic site intact. A total of 15,772 compounds were generated from diverse chemical scaffolds and were evaluated using a virtual screening pipeline. Based on docking and MM-GBSA scores, a lead candidate, S3C-1-D424 was identified from top hits. A comparative molecular dynamics simulations (MD) of APO, Selonsertib and shortlisted potential candidates combined with pharmacokinetics profiling and thermodynamic analysis, demonstrating their suitability as potential ASK1 inhibitors to explore further for establishment towards hit-to-lead campaign.Communicated by Ramaswamy H. Sarma.

Application of In silico Methods in the Design of Drugs for Neurodegenerative Diseases.

Neurodegenerative diseases are complex disorders that cause neuron loss, brain aging and ultimately lead to death. These diseases are difficult to treat because of the complex nature of the nervous system, and the available medicines are unable to heal them effectively. This fact implies the need for novel therapeutics to be designed that are ready to stop or a minimum of retard the neurodegeneration process. These days, Computer-Assisted Drug Design (CADD) approaches are a passage to extend the drug development efficiency and to reduce time and cost because traditional drug discovery is both time-consuming as well as costly. Computational or in silico methods came up with powerful tools in drug design against neurodegenerative diseases. This review presents the approaches and theoretical basis of CADD. Also, the successful applications of various in silico studies, including homology modeling, molecular docking, Quantitative Structure-Activity Relationship (QSAR), Molecular Dynamic (MD), De novo drug design, Pharmacophore-based drug design, Virtual Screening (VS), LIGPLOT Analysis, In silico ADMET and drug safety prediction, for treating neurodegenerative diseases have also been included in this review. Major emphasis is given to Alzheimer's disease and Parkinson's disease because these two are the most familiar neurodegenerative diseases.

Molecular analysis of HumDN1 VNTR polymorphism of the human deoxyribonuclease I in systemic lupus erythematosus.

Deoxyribonuclease I (DNASE1) may be responsible for the removal of DNA from nuclear antigens at sites of high cell turnover, thus preventing the onset of systemic lupus erythematosus (SLE). The purpose of this study was to screen DNASE1 gene for mutations that may have an effect on susceptibility to develop SLE. DNA was extracted from 76 Kuwaiti SLE patients and 92 race-matched controls. PCR-direct sequencing was used to screen DNASE1 promoter, coding sequence and exon-intron boundaries for mutation. Association of genomic variations was assessed using a Chi-square test. Molecular analysis of the DNASE1 gene did not reveal any mutation. However, a 56-bp repeat was detected in intron4 which was previously reported and named HumDN1. The allelic and genotypic distributions of the HumDN1 VNTR were compared between SLE patients and healthy subjects. Alleles were denoted as 2, 3, 4, 5 and 6 corresponding to the number of repeats of the 56 bp unit. Alleles 4, 5, and 6 showed significant association with SLE. Allele 5 showed the highest association [chi(2) = 32.57; P < or = 0.001; OR = 4.16; 95% CI: (2.55-6.79)]. Association of allele 5 was also found at the genotypic level, where genotype 5/5 is more prevalent in SLE subjects as compared with controls (17% versus 9%). We report a significant association of HumDN1 VNTR polymorphism in DNASE1 gene with SLE. Further functional assays needed to assess the effect of this VNTR on DNASE1 activity and its association with SLE.