Synthesis,molecular docking,acetylcholinesterase and butyrylcholinesterase inhibitory potential of thiazole analogs as new inhibitors for Alzheimer disease

A series of thirty (30) thiazole analogs were prepared, characterized by ¹H NMR, ¹³C NMR and EI-MS and evaluated for Acetylcholinesterase and butyrylcholinesterase inhibitory potential. All analogs exhibited varied butyrylcholinesterase inhibitory activity with IC₅₀ value ranging between 1.59 ± 0.01 and 389.25 ± 1.75 μM when compared with the standard eserine (IC₅₀, 0.85 ± 0.0001 μM). Analogs 15, 7, 12, 9, 14, 1, 30 with IC₅₀ values 1.59 ± 0.01, 1.77 ± 0.01, 6.21 ± 0.01, 7.56 ± 0.01, 8.46 ± 0.01, 14.81 ± 0.32 and 16.54 ± 0.21 μM respectively showed excellent inhibitory potential. Seven analogs 15, 20, 19, 24, 28, 30 and 25 exhibited good acetylcholinesterase inhibitory potential with IC50 values 21.3 ± 0.50, 35.3 ± 0.64, 36.6 ± 0.70, 44.81 ± 0.81, 46.36 ± 0.84, 48.2 ± 0.06 and 48.72 ± 0.91 μM respectively. All other analogs also exhibited well to moderate enzyme inhibition. The binding mode of these compounds was confirmed through molecular docking.     

Identification of abelson tyrosine kinase inhibitors as potential therapeutics for Alzheimer’s disease using multiple e-pharmacophore modeling and molecular dynamics

Efforts to combat Alzheimer’s disease are focused predominantly on inhibiting the activity of the enzyme(s) that have been identified to be responsible for the production of the amyloid-forming peptide. However, the inherent complexity associated with the network of pathways leading to the disease may involve additional targets for designing effective therapies. Recent experimental findings have identified abelson tyrosine kinase, a non-receptor kinase as a new target for Alzheimer’s. In this work, we employed energy optimized multiple pharmacophore modeling strategy from multiple c-Abl structures bound with ligands in the inactive ATP binding conformation (DFG-out). Virtual screening followed by docking of molecules from ChemBridge resulted in the identification of 10 best scoring molecules. MD simulations of the top three complexes revealed that Compound A, C are the most stable complexes with the most persistent protein–ligand interactions consistent with the calculated binding affinities for the top three compounds. Given the implied role of c-Abl not only in AD but in Parkinson’s disease, the identified compounds may serve as leads for effective neurotherapeutics.     

Lignites of Kutch,western India: Dinoflagellate biostratigraphy and palaeoclimate

The Eocene epoch in the Indian subcontinent was marked by widespread deposition of lignite and coal. While several of these deposits formed during the Early Eocene, corresponding to Early Eocene hyperthermal events, the lignites of Kutch in western India formed later during the Middle Eocene. An integrated biostratigraphy based on dinoflagellates and foraminifera assigns a Bartonian age to the succession, which likely corresponds to the time of the Middle Eocene warming. The spores, pollen, dinoflagellates and foraminifera suggest a restricted marine, near shore depositional environment adjacent to tropical rainforest. The lignites of Kutch suggest high precipitation during or just preceding the warm climate of the Middle Eocene.     

AlphaFold and the amyloid landscape

Protein aggregation is a widespread phenomenon with important implications in many scientific areas. Although amyloid formation is typically considered as detrimental, functional amyloids that perform physiological roles have been identified in all kingdoms of life. Despite their functional and pathological relevance, the structural details of the majority of molecular species involved in the amyloidogenic process remains elusive. Here, we explore the application of AlphaFold, a highly accurate protein structure predictor, in the field of protein aggregation. While we envision a straightforward application of AlphaFold in assisting the design of globular proteins with improved solubility for biomedical and industrial purposes, the use of this algorithm for predicting the structure of aggregated species seems far from trivial. First, in amyloid diseases, the presence of multiple amyloid polymorphs and the heterogeneity of aggregation intermediates challenges the “one sequence, one structure” paradigm, inherent to sequence-based predictions. Second, aberrant aggregation is not the subject of positive selective pressure, precluding the use of evolutionary-based approaches, which are the core of the AlphaFold pipeline. Instead, amyloid polymorphism seems to be constrained by the need for a defined structure-activity relationship in functional amyloids. They may thus provide a starting point for the application of AlphaFold in the amyloid landscape.     

A New Monoclonal Antibody Enables BAR Analysis of Subcellular Importin β1 Interactomes

Importin β1 (KPNB1) is a nucleocytoplasmic transport factor with critical roles in both cytoplasmic and nucleocytoplasmic transport, hence there is keen interest in the characterization of its subcellular interactomes. We found limited efficiency of BioID in the detection of importin complex cargos and therefore generated a highly specific and sensitive anti-KPNB1 monoclonal antibody to enable biotinylation by antibody recognition analysis of importin β1 interactomes. The monoclonal antibody recognizes an epitope comprising residues 301-320 of human KPBN1 and strikingly is highly specific for cytoplasmic KPNB1 in diverse applications, with little reaction with KPNB1 in the nucleus. Biotinylation by antibody recognition with this novel antibody revealed numerous new interactors of importin β1, expanding the KPNB1 interactome to cytoplasmic and signaling complexes that highlight potential new functions for the importins complex beyond nucleocytoplasmic transport. Data are available via ProteomeXchange with identifier PXD032728.     

Deciphering Spatial Protein–Protein Interactions in Brain Using Proximity Labeling

Cellular biomolecular complexes including protein–protein, protein–RNA, and protein–DNA interactions regulate and execute most biological functions. In particular in brain, protein–protein interactions (PPIs) mediate or regulate virtually all nerve cell functions, such as neurotransmission, cell–cell communication, neurogenesis, synaptogenesis, and synaptic plasticity. Perturbations of PPIs in specific subsets of neurons and glia are thought to underly a majority of neurobiological disorders. Therefore, understanding biological functions at a cellular level requires a reasonably complete catalog of all physical interactions between proteins. An enzyme-catalyzed method to biotinylate proximal interacting proteins within 10 to 300 nm of each other is being increasingly used to characterize the spatiotemporal features of complex PPIs in brain. Thus, proximity labeling has emerged recently as a powerful tool to identify proteomes in distinct cell types in brain as well as proteomes and PPIs in structures difficult to isolate, such as the synaptic cleft, axonal projections, or astrocyte–neuron junctions. In this review, we summarize recent advances in proximity labeling methods and their application to neurobiology.