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.

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.

Protein kinases are deeply involved in immune-related diseases and various cancers. They are a potential target for structure-based drug discovery, since the general structure and characteristics of kinase domains are relatively well-known. However, the ATP binding sites in protein kinases, which serve as target sites, are highly conserved, and thus it is difficult to develop selective kinase inhibitors. To resolve this problem, we performed molecular dynamics simulations on 26 kinases in the aqueous solution, and analyzed topological water networks (TWNs) in their ATP binding sites. Repositioning of a known kinase inhibitor in the ATP binding sites of kinases that exhibited a TWN similar to interleukin-1 receptor-associated kinase 4 (IRAK4) allowed us to identify a hit molecule. Another hit molecule was obtained from a commercial chemical library using pharmacophore-based virtual screening and molecular docking approaches. Pharmacophoric features of the hit molecules were hybridized to design a novel compound that inhibited IRAK4 at low nanomolar levels in the in vitro assay.

We extracted 15 pterosin derivatives from Pteridium aquilinum that inhibited β-site amyloid precursor protein cleaving enzyme 1 (BACE1) and cholinesterases involved in the pathogenesis of Alzheimer’s disease (AD). (2R)-Pterosin B inhibited BACE1, acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) with an IC50 of 29.6, 16.2 and 48.1 µM, respectively. The Ki values and binding energies (kcal/mol) between pterosins and BACE1, AChE, and BChE corresponded to the respective IC50 values. (2R)-Pterosin B was a noncompetitive inhibitor against human BACE1 and BChE as well as a mixed-type inhibitor against AChE, binding to the active sites of the corresponding enzymes. Molecular docking simulation of mixed-type and noncompetitive inhibitors for BACE1, AChE, and BChE indicated novel binding site-directed inhibition of the enzymes by pterosins and the structure−activity relationship. (2R)-Pterosin B exhibited a strong BBB permeability with an effective permeability (Pe) of 60.3×10−6 cm/s on PAMPA-BBB. (2R)-Pterosin B and (2R,3 R)-pteroside C significantly decreased the secretion of Aβ peptides from neuroblastoma cells that overexpressed human β-amyloid precursor protein at 500 μM. Conclusively, our study suggested that several pterosins are potential scaffolds for multitarget-directed ligands (MTDLs) for AD therapeutics.

The nociceptin receptor (NOPR) is an orphan G protein-coupled receptor that contains seven transmembrane helices. NOPR has a distinct mechanism of activation, though it shares a significant homology with other opioid receptors. Previously there have been reports on homology modeling of NOPR and also molecular dynamics simulation studies for a short period. Recently the crystal structure of NOPR was reported. In this study, we analyzed the time dependent behavior of NOPR docked with clinically important agonist molecules such as NOP (natural agonist) peptide and compound 10 (SCH-221510 derivative) using molecular dynamics simulations (MDS) for 100 ns. Molecular dynamics simulations of NOPR-agonist complexes allowed us to refine the system and to also identify stable structures with better binding modes. Structure activity relationships (SAR) for SCH221510 derivatives were investigated and reasons for the activities of these derivatives were determined. Our molecular dynamics trajectory analysis of NOPR-peptide and NOPR-compound 10 complexes found residues to be crucial for binding. Mutagenesis studies on the residues identified from our analysis could prove useful. Our results could also provide useful information in the structure-based drug design of novel and potent agonists targeting NOPR.

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Human CC-chemokine receptor 8 (CCR8) is a crucial drug target in asthma that belongs to G-protein-coupled receptor superfamily, which is characterized by seven transmembrane helices. To date, there is no X-ray crystal structure available for CCR8; this hampers active research on the target. Molecular basis of interaction mechanism of antagonist with CCR8 remains unclear. In order to provide binding site information and stable binding mode, we performed modeling, docking and molecular dynamics (MD) simulation of CCR8. Docking study of biaryl-ether-piperidine derivative (13C) was performed inside predefined CCR8 binding site to get the representative conformation of 13C. Further, MD simulations of receptor and complex (13C-CCR8) inside dipalmitoylphosphatidylcholine lipid bilayers were performed to explore the effect of lipids. Results analyses showed that the Gln91, Tyr94, Cys106, Val109, Tyr113, Cys183, Tyr184, Ser185, Lys195, Thr198, Asn199, Met202, Phe254, and Glu286 were conserved in both docking and MD simulations. This indicated possible role of these residues in CCR8 antagonism. However, experimental mutational studies on these identified residues could be effective to confirm their importance in CCR8 antagonism. Furthermore, calculated Coulombic interactions represented the crucial roles of Glu286, Lys195, and Tyr113 in CCR8 antagonism. Important residues identified in this study overlap with the previous non-peptide agonist (LMD-009) binding site. Though, the non-peptide agonist and currently studied inhibitor (13C) share common substructure, but they differ in their effects on CCR8. So, to get more insight into their agonist and antagonist effects, further side-by-side experimental studies on both agonist (LMD-009) and antagonist (13C) are suggested.

Autotaxin (ATX) is a potential drug target that is associated with inflammatory diseases and various cancers. In our previous studies, we have designed several inhibitors targeting ATX using computational and experimental approaches. Here, we have analyzed topological water networks (TWNs) in the binding pocket of ATX. TWN analysis revealed a pharmacophoric site inside the pocket. We designed and synthesized compounds considering the identified pharmacophoric site. Furthermore, we performed biological experiments to determine their ATX inhibitory activities. High potency of the designed compounds supports the predictions of the TWN analysis.