The α-synuclein is a major component of amyloid fibrils found in Lewy bodies, the characteristic intracellular proteinaceous deposits which are pathological hallmarks of neurodegenerative diseases such as Parkinson’s disease (PD) and dementia. It is an intrinsically disordered protein that may undergo dramatic structural changes to form amyloid fibrils. Aggregation process from α-synuclein monomers to amyloid fibrils through oligomeric intermediates is considered as the disease-causative toxic mechanism. However, mechanism underlying aggregation is not well-known despite several attempts. To characterize the mechanism, we have explored the effects of pH and temperature on the structural properties of wild-type and mutant α-synuclein using molecular dynamics (MD) simulation technique. MD studies suggested that amyloid fibrils can grow by monomer. Conformational transformation of the natively unfolded protein into partially folded intermediate could be accountable for aggregation and fibrillation. An intermediate α-strand was observed in the hydrophobic non-amyloid-β component (NAC) region of α-synuclein that could proceed to α-sheet and initiate early assembly events. Water network around the intermediate was analyzed to determine its influence on the α-strand structure. Findings of this study provide novel insights into possible mechanism of α-synuclein aggregation and promising neuroprotective strategy that could aid alleviate PD and its symptoms.

In this study, we report new classes of potent tyrosinase inhibitors identified by enhanced structure-based virtual screening prediction; the enzyme and melanin content assays were also confirmed. Tyrosinase, a type-3 copper protein, participates in two distinct reactions, hydroxylation of tyrosine to DOPA and conversion of DOPA to dopaquinone, in melanin biosynthesis. Although numerous inhibitors of this reaction have been reported, there is a lag in the discovery of the new functional moieties. In order to improve the performance of virtual screening, we first produced an ensemble of 10,000 structures using molecular dynamics simulation. Quantum mechanical calculation was used to determine the partial charges of catalytic copper ions based on the met and deoxy states. Second, we selected a structure showing an optimal receiver operating characteristic (ROC) curve with known direct binders and their physicochemically matched decoys. The structure revealed more than 10-fold higher enrichment at 1% of the ROC curve than those observed in X-ray structures. Third, high-throughput virtual screening with DOCK 3.6 was performed using a library consisting of approximately 400,000 small molecules derived from the ZINC database. Fourth, we obtained the top 60 molecules and tested their inhibition of mushroom tyrosinase. The extended assays included 21 analogs of the 21 initial hits to test their inhibition properties. Here, the moieties of tetrazole and triazole were identified as new binding cores interacting with the dicopper catalytic center. All 42 inhibitors showed inhibitory constant, Ki, values ranging from 11.1 nM and 33.4 μM, with a tetrazole compound exhibiting the strongest activity. Among the 42 molecules, five displayed more than 30% reduction in melanin production when treated in B16F10 melanoma cells; cell viability was>90% at 20 μM. Particularly, a thiosemicarbazone-containing compound reduced melanin content by 55%.

Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study, we collected 5299 hERG inhibitors with diverse chemical structures from a number of sources. Based on this dataset, we evaluated different machine learning (ML) and deep learning (DL) algorithms using various integer and binary type fingerprints. A training set of 3991 compounds was used to develop quantitative structure–activity relationship (QSAR) models. The performance of the developed models was evaluated using a test set of 998 compounds. Models were further validated using external set 1 (263 compounds) and external set 2 (47 compounds). Overall, models with integer type fingerprints showed better performance than models with no fingerprints, converted binary type fingerprints or original binary type fingerprints. Comparison of ML and DL algorithms revealed that integer type fingerprints are suitable for ML, whereas binary type fingerprints are suitable for DL. The outcomes of this study indicate that the rational selection of fingerprints is important for hERG blocker prediction.

Heat shock protein 90 (Hsp90) is one of the most abundant cellular proteins and plays a substantial role in the folding of client proteins. The inhibition of Hsp90 has been regarded as an attractive therapeutic strategy for treating cancer because many oncogenic kinases are Hsp90 client proteins. In this study, we report new inhibitors that directly bind to N-terminal ATP-binding pocket of Hsp90. Optimized structure-based virtual screening predicted candidate molecules, which was followed by confirmation using biophysical and cell-based assays. Among the reported crystal structures, we chose the two structures that show the most favourable early enrichments of true-positives in the receiver operating characteristic curve. Four molecules showed significant changes in the signals of 2D [1H, 15N] correlation NMR spectroscopy. Differential scanning calorimetry analysis supported the results indicating direct binding. Quantified dissociation constant values of the molecules, determined by a series of 2D NMR experiments, lie in the range of 0.1–33 μM. Growth inhibition assay with breast and lung cancer cells confirmed the cellular activities of the molecules. Cheminformatics revealed that the molecules share limited chemical similarities with known inhibitors. Molecular dynamics simulations detailed the putative binding modes of the inhibitors.

Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is a protein kinase with diverse functions in cell regulation. Abnormal expression and activity of DYRK1A contribute to numerous human malignancies, Down syndrome, and Alzheimer’s disease. Notably, DYRK1A has been proposed as a potential therapeutic target for the treatment of diabetes because of its key role in pancreatic β-cell proliferation. Consequently, DYRK1A is an attractive drug target for a variety of diseases. Here, we report the identification of several DYRK1A inhibitors using our in-house topological water network-based approach. All inhibitors were further verified by in vitro assay.