Computational screening of coumarin derivatives as inhibitors of the NACHT domain of NLRP3 inflammasome for the treatment of Alzheimer's disease.

The nucleotide-binding oligomerization domain (NOD)-like receptor (NLR), leucine-rich-repeat (LRR), and pyrin domain containing 3 (NLRP3) is one of the key players in neuroinflammation, which is a major pathological hallmark of Alzheimer's Disease (AD). Activated NLRP3 causes release of pro-inflammatory molecules that aggravate neurodegeneration. Thus, pharmacologically inhibiting the NLRP3 inflammasome has the potential to alleviate the inflammatory injury to the neurons. Coumarin is a multifunctional nucleus with potent anti-inflammatory properties and can be utilized to develop novel drugs for the treatment and management of AD. In the present study, we have explored the NLRP3-inhibitory activities of a library of coumarin derivatives through a computational drug discovery approach. Drug-like, PAINS free, and potentially BBB permeable compounds were screened out and subjected to molecular docking and in silico ADMET studies, resulting in three virtual hits, i.e. MolPort-050-872-358, MolPort-050-884-068, and MolPort-051-135-630. The hits exhibited better NLRP3-binding affinity than MCC950, a selective inhibitor of NLRP3. Further, molecular dynamics (MD) simulations, post-MD simulation analyses, and binding free energy calculations of the hits established their potential as promising virtual leads with a common coumarin scaffold for the inhibition of NLRP3 inflammasome.Communicated by Ramaswamy H. Sarma.

Identification of potent histone deacetylase 2 (HDAC2) inhibitors through combined structure and ligand-based designs and molecular modelling approach.

Histone deacetylase 2 (HDAC2) is associated with various neuropathic degenerative diseases and is considered a novel target for Alzheimer's disease (AD). Elevated levels of HDAC2 trigger excitatory neurotransmission and reduce synaptic plasticity, synaptic number, and memory formation. In the current study, we identified HDAC2 inhibitors using an integrated structure and ligand-based approaches to drug design. Three pharmacophore models were generated by using different pharmacophoric features and validated using the Enrichment factor (EF), Güner-henry (GH) score, and percentage yield. The model of choice was used to screen a library of Zinc-15 compounds and interfering compounds were eliminated by using drug likeliness and PAINS filtering. Further, docking studies in three stages were carried out to obtain hits with good binding energies and were followed by ADMET studies yielding three virtual hits. The virtual hits, i.e. ZINC000008184553, ZINC0000013641114, and ZINC000032533141, were subjected to molecular dynamics simulation studies. Compound ZINC000008184553, identified as lead, was found to have optimal stability, low toxicity under simulated conditions, and may potentially inhibit HDAC2.Communicated by Ramaswamy H. Sarma.

Pharmacophore-based virtual screening, molecular docking and molecular dynamics simulations study for the identification of LIM kinase-1 inhibitors.

LIM kinases (LIMKs) are a family of protein kinases involved in the regulation of actin dynamics. There are two isoforms of LIMKs i.e., LIMK1 and LIMK2. LIMK1 is expressed abundantly in neuronal tissues. LIMK1 plays an essential role in the degradation of dendritic spines, which are important for our higher brain functions, such as memory and learning. The inhibition of LIMK1 improves the size and density of dendritic spines and acts as a protective effect against Alzheimer's disease. In this study, we have adopted ligand-based drug design and molecular modelling methods to identify virtual hits. The pharmacophoric features of PF-00477736 were used to screen the Zinc15 compounds library. The identified hits were then passed through drug-likeliness and PAINS filters. Further, comprehensive docking and rigorous molecular dynamics simulation study afforded three virtual hits viz., ZINC504485634, ZINC16940431 and ZINC1091071. The hits showed a better docking score than the standard ligand, PF-00477736. The docking score was found to be -8.85, -7.50 and -7.68 kcal/mol. These hits exhibited optimal binding properties with the target in docking study, blood-brain barrier permeability, in silico pharmacokinetics and low predicted toxicity.Communicated by Ramaswamy H. Sarma.

Identification of potential death-associated protein kinase-1 (DAPK1) inhibitors by an integrated ligand-based and structure-based computational drug design approach.

Death-associated protein kinase 1 (DAPK1) is a calcium/calmodulin (Ca2+/CaM)-dependent serine/threonine kinase that is abundantly expressed in the memory- and cognition-related brain areas. DAPK1 is associated with several pathological hallmarks of Alzheimer's disease (AD); it is an attractive target for designing a novel DAPK1 inhibitor as an effective therapeutic treatment for AD. In the present study, we have used an integrated ligand-based and structure-based drug design method to identify DAPK1 inhibitors. The pharmacophoric features of compound 38 G (PDB ID 4TXC) were mapped, and the models were evaluated using enrichment factor (EF) and goodness of hit (GH) score. The selected models were used to screen Zinc 15 compounds library. The identified hits were passed through drug-likeliness and PAINS filtering. The docking study was performed in three steps to yield molecules with good binding energy and ligand-target interactions. Finally, three hits were obtained, that is, ZINC000020648330, ZINC000006755051 and ZINC000020650468, which were subjected to rigorous molecular dynamics simulation. All three hits exhibited optimal stability under simulated conditions and low predicted toxicity.Communicated by Ramaswamy H. Sarma.

Recent Applications of Bioinformatics in Target Identification and Drug Discovery for Alzheimer's Disease.

Alzheimer's disease (AD) is a complex multifactorial neurodegenerative disease characterized by progressive memory loss. The main pathological features of the disease are extracellular deposition of amyloid ß (Aß) plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein. Understanding factors contributing to AD progression, the number of molecular signatures, and the development of therapeutic agents played a significant role in the discovery of disease-modifying drugs to treat the disease. Bioinformatics has established its significance in many areas of biology. The role of bioinformatics in drug discovery, is emerging significantly and will continue to evolve. In recent years, different bioinformatics methodologies, viz. protein signaling pathway, molecular signature differences between different classes of drugs, interacting profiles of drugs and their potential therapeutic mechanisms, have been applied to identify potential therapeutic targets of AD. Bioinformatics tools were also found to contribute to the discovery of novel drugs, omics-based biomarkers, and drug repurposing for AD. The review aims to explore the applications of various advanced bioinformatics tools in the identification of targets, biomarkers, pathways, and potential therapeutics for the treatment of the disease.

Cellular and molecular influencers of neuroinflammation in Alzheimer's disease: Recent concepts & roles.

Alzheimer's disease (AD), an extremely common neurodegenerative disorder of the older generation, is one of the leading causes of death globally. Besides the conventional hallmarks i.e. Amyloid-ß (Aß) plaques and neurofibrillary tangles (NFTs), neuroinflammation also serves as a major contributing factor in the pathogenesis of AD. There are mounting evidences to support the fundamental role of cellular (microglia, astrocytes, mast cells, and T-cells) and molecular (cytokines, chemokines, caspases, and complement proteins) influencers of neuroinflammation in producing/promoting neurodegeneration and dementia in AD. Genome-wide association studies (GWAS) have revealed the involvement of various single nucleotide polymorphisms (SNPs) of genes related to neuroinflammation with the risk of developing AD. Modulating the release of the neuroinflammatory molecules and targeting their relevant mechanisms may have beneficial effects on the onset, progress and severity of the disease. Here, we review the distinct role of various mediators and modulators of neuroinflammation that impact the pathogenesis and progression of AD as well as incite further research efforts for the treatment of AD through a neuroinflammatory approach.

Classification of beta-site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods.

The beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a transmembrane aspartyl-protease, that cleaves amyloid precursor protein (APP) at the ß-site. The sequential proteolytic cleavage of APP, first by ß-secretase and then by γ-secretase complex, leads to the production and release of amyloid-ß peptide, a pathological hallmark of Alzheimer's disease (AD). BACE1 inhibitors are reported to possess considerable potential in decreasing the level of amyloid-ß in brain and preventing the progression of AD. A classification study has been conducted on 3536 diverse BACE1 inhibitors, obtained from Binding DB database, by extracting two types of descriptors, that is molecular property (Mordred) and fingerprints (Pubchem, MACCS and KRFP). Furthermore, based on the descriptors, various machine learning algorithms such as Naïve Bayesian (NB), nearest known neighbours (kNN), support vector machine (SVM), random forest (RF) and gradient-boosted algorithms (XGB) were applied to develop classification models. The performance of models was evaluated by using accuracy, precision, recall and F1 score of test set. The best NB, kNN, SVM, RF and XGB classifiers had F1 score of 0.74, 0.85, 0.86, 0.87 and 0.87, respectively. The diverse 3536 BACE1 inhibitors were clustered into 11 subsets, and the structural features of each subset were evaluated. The important fragments present in active and inactive compounds were also identified. The model developed in the study would serve as a valuable tool for the designing of BACE1 inhibitors, and also in virtual screening of molecules to identify these.