Rational design of novel diaryl ether-linked benzimidazole derivatives as potent and selective BACE1 inhibitors.

Due to the large size and high flexibility of the catalytic active site of BACE1 enzyme, the development of nonpeptide inhibitors with optimal pharmacological properties is still highly demanding. In this work, we have discovered 2-aminobenzimidazole-containg ether scaffolds having potent and selective inhibitory potentials against BACE1 enzyme. We have synthesized novel 29 compounds and optimization of aryl linker region resulted in highly potent BACE1 inhibitory activities with EC50 values of 0.05-2.71 µM. The aryloxy-phenyl analogs 20j showed the EC50 value as low as 0.07 µM in the enzyme assay, whereas, the benzyloxyphenyl dervative 24b was comparatively less effective in the enzyme assay. But interestingly the latter was more effective in the cell assay (EC50 value 1.2 µM). While comparing synthesized derivatives in the cell assay using PC12-APPSW cell, compound 27f appeared as the most potent BACE1 inhibitor having EC50 value 0.7 µM. This scaffold also showed high selectivity over BACE2 enzyme and cathepsin D. Furthermore, the research findings were bolstered through the incorporation of molecular docking, molecular dynamics, and DFT studies. We firmly believe that these discoveries will pave the way for the development of a novel class of small-molecule selective BACE1 inhibitors.

Design, synthesis of new 3H-imidazo[4,5-b]pyridine derivatives and evaluation of their inhibitory properties as mixed lineage kinase 3 inhibitors.

Mixed-lineage protein kinase 3 (MLK3) is implicated in several human cancers and neurodegenerative diseases. A series of 3H-imidazo[4,5-b]pyridine derivatives were designed, synthesized and evaluated as novel MLK3 inhibitors. A homology model of MLK3 was developed and all designed compounds were docked to assess their binding pattern and affinity toward the MLK3 active site. Based on this knowledge, we synthesized and experimentally evaluated the designed compounds. Majority of the compounds showed significant inhibition of MLK3 in the enzymatic assay. In particular, compounds 9a, 9e, 9j, 9 k, 12b and 12d exhibited IC50 values of 6, 6, 8, 11, 14 and 14 nM, respectively. Furthermore, compounds 9a, 9e, 9 k and 12b exhibited favorable physicochemical properties among these compounds.

ADis-QSAR: a machine learning model based on biological activity differences of compounds.

Drug candidates identified by the pharmaceutical industry typically have unique structural characteristics to ensure they interact strongly and specifically with their biological targets. Identifying these characteristics is a key challenge for developing new drugs, and quantitative structure-activity relationship (QSAR) analysis has generally been used to perform this task. QSAR models with good predictive power improve the cost and time efficiencies invested in compound development. Generating these good models depends on how well differences between "active" and "inactive" compound groups can be conveyed to the model to be learned. Efforts to solve this difference issue have been made, including generating a "molecular descriptor" that compressively expresses the structural characteristics of compounds. From the same perspective, we succeeded in developing the Activity Differences-Quantitative Structure-Activity Relationship (ADis-QSAR) model by generating molecular descriptors that more explicitly convey features of the group through a pair system that performs direct connections between active and inactive groups. We used popular machine learning algorithms, such as Support Vector Machine, Random Forest, XGBoost and Multi-Layer Perceptron for model learning and evaluated the model using scores such as accuracy, area under curve, precision and specificity. The results showed that the Support Vector Machine performed better than the others. Notably, the ADis-QSAR model showed significant improvements in meaningful scores such as precision and specificity compared to the baseline model, even in datasets with dissimilar chemical spaces. This model reduces the risk of selecting false positive compounds, improving the efficiency of drug development.

Comparative Computational Analysis of Spike Protein Structural Stability in SARS-CoV-2 Omicron Subvariants.

The continuous emergence of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with multiple spike (S) protein mutations pose serious threats to current coronavirus disease 2019 (COVID-19) therapies. A comprehensive understanding of the structural stability of SARS-CoV-2 variants is vital for the development of effective therapeutic strategies as it can offer valuable insights into their potential impact on viral infectivity. S protein mediates a virus' attachment to host cells by binding to angiotensin-converting enzyme 2 (ACE2) through its receptor-binding domain (RBD), and mutations in this protein can affect its stability and binding affinity. We analyzed S protein structural stability in various Omicron subvariants computationally. Notably, the S protein sequences analyzed in this work were obtained directly from our own sample collection. We evaluated the binding free energy between S protein and ACE2 in several complex forms. Additionally, we measured distances between the RBD of each chain in S protein to analyze conformational changes. Unlike most of the prior studies, we analyzed full-length S protein-ACE2 complexes instead of only RBD-ACE2 complexes. Omicron subvariants including BA.1, BA.2, BA.2.12.1, BA.4/BA.5, BA.2.75, BA.2.75_K147E, BA.4.6 and BA.4.6_N658S showed enhanced stability compared to wild type, potentially due to distinct S protein mutations. Among them, BA.2.75 and BA.4.6_N658S exhibited the highest and lowest level of stability, respectively.

A Study on the Effect of the Substituent against PAK4 Inhibition Using In Silico Methods.

The intrinsic inductive properties of atoms or functional groups depend on the chemical properties of either electron-withdrawing groups (EWGs) or electron-donating groups (EDGs). This study aimed to evaluate in silico methods to determine whether changes in chemical properties of the compound by single atomic substitution affect the biological activity of target proteins and whether the results depend on the properties of the functional groups. We found an imidazo[4,5-b]pyridine-based PAK4 inhibitor, compound 1, as an initial hit compound with the well-defined binding mode for PAK4. In this study, we used both experimental and in silico methods to investigate the effect of atomic substitution on biological activity to optimize the initial hit compound. In biological assays, in the case of EWG, as the size of the halogen atom became smaller and the electronegativity increased, the biological activity IC50 value ranged from 5150 nM to inactive; in the case of EDG, biological activity was inactive. Furthermore, we analyzed the interactions of PAK4 with compounds, focusing on the hinge region residues, L398 and E399, and gatekeeper residues, M395 and K350, of the PAK4 protein using molecular docking studies and fragment molecular orbital (FMO) methods to determine the differences between the effect of EWG and EDG on the activity of target proteins. These results of the docking score and binding energy did not explain the differences in biological activity. However, the pair-interaction energy obtained from the results of the FMO method indicated that there was a difference in the interaction energy between the EWG and EDG in the hinge region residues, L398 and E399, as well as in M395 and K350. The two groups with different properties exhibited opposite electrostatic energy and charge transfer energy between L398 and E399. Additionally, we investigated the electron distribution of the parts interacting with the hinge region by visualizing the molecular electrostatic potential (MEP) surface of the compounds. In conclusion, we described the properties of functional groups that affect biological activity using an in silico method, FMO.

Molecular Dynamics Studies on the Structural Stability Prediction of SARS-CoV-2 Variants Including Multiple Mutants.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused the Coronavirus Disease (COVID-19) pandemic worldwide. The spike protein in SARS-CoV-2 fuses with and invades cells in the host respiratory system by binding to angiotensin-converting enzyme 2 (ACE2). The spike protein, however, undergoes continuous mutation from a D614G single mutant to an omicron variant, including multiple mutants. In this study, variants, including multiple mutants (double, triple mutants, B.1.620, delta, alpha, delta_E484Q, mu, and omicron) were investigated in patients. The 3D structure of the full-length spike protein was used in conformational analysis depending on the SARS-CoV-2 variants. The structural stability of the variant types was analyzed based on the distance between the receptor-binding domain (RBD) of each chain in the spike protein and the binding free energy between the spike protein and bound ACE2 in the one-, two-, and three-open-complex forms using molecular dynamics (MD) simulation. Omicron variants, the most prevalent in the recent history of the global pandemic, which consist of 32 mutations, showed higher stability in all open-complex forms compared with that of the wild type and other variants. We suggest that the conformational stability of the spike protein is the one of the important determinants for the differences in viral infectivity among variants, including multiple mutants.

Molecular Dynamics Studies on the Structural Characteristics for the Stability Prediction of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affects the COVID-19 pandemic in the world. The spike protein of the various proteins encoded in SARS-CoV-2 binds to human ACE2, fuses, and enters human cells in the respiratory system. Spike protein, however, is highly variable, and many variants were identified continuously. In this study, Korean mutants for spike protein (D614G and D614A-C terminal domain, L455F and F456L-RBD, and Q787H-S2 domain) were investigated in patients. Because RBD in spike protein is related to direct interaction with ACE2, almost all researches were focused on the RBD region or ACE2-free whole domain region. The 3D structure for spike protein complexed with ACE2 was recently released. The stability analysis through RBD distance among each spike protein chain and the binding free energy calculation between spike protein and ACE2 were performed using MD simulation depending on mutant types in 1-, 2-, and 3-open-complex forms. D614G mutant of CT2 domain, showing to be the most prevalent in the global pandemic, showed higher stability in all open-complex forms than the wild type and other mutants. We hope this study will provide an insight into the importance of conformational fluctuation in the whole domain, although RBD is involved in the direct interaction with ACE2.

Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space.

Drug-induced liver injury (DILI) is a major concern for drug developers, regulators, and clinicians. However, there is no adequate model system to assess drug-associated DILI risk in humans. In the big data era, computational models are expected to play a revolutionary role in this field. This study aimed to develop a deep neural network (DNN)-based model using extended connectivity fingerprints of diameter 4 (ECFP4) to predict DILI risk. Each data set for the predictive model was retrieved and curated from DILIrank, LiverTox, and other literature. The best model was constructed through ten iterations of stratified 10-fold cross-validation, and the applicability domain was defined based on integer ECFP4 bits of the training set which represented substructures. For the robustness test, we employed the concept of the endurance level. The best model showed an accuracy of 0.731, a sensitivity of 0.714, and a specificity of 0.750 on the validation data set in the complete applicability domain. The model was further evaluated with four external data sets and attained an accuracy of 0.867 on 15 drugs with DILI cases reported since 2019. Overall, the results suggested that the ECFP4-based DNN model represents a new tool to identify DILI risk for the evaluation of drug safety.

Optimization of cyclic sulfamide derivatives as 11ß-hydroxysteroid dehydrogenase 1 inhibitors for the potential treatment of ischemic brain injury.

The 11ß-hydroxysteroiddehydrogenase type 1(11ß-HSD1), acortisolregenerating enzyme that amplifies tissue glucocorticoidlevels, plays an important role in diabetes, obesity, and glaucoma and is recognized as a potential therapeutic target for various disease conditions. Moreover, a recent study demonstrated that selective 11ß-HSD1 inhibitor can attenuate ischemic brain injury. This prompted us to optimize cyclic sulfamide derivative for aiming to treat ischemic brain injury. Among the synthesized compounds, 6e has an excellent in vitro activivity with an IC50 value of 1 nM toward human and mouse 11ß-HSD1 and showed good 11ß-HSD1 inhibition in ex vivo study using brain tissue isolated from mice. Furthermore, in the transient middle cerebral artery occlusion model in mice, 6e treatment significantly attenuated infarct volume and neurological deficit following cerebral ischemia/reperfusion injury. Additionally, binding modes of 6e for human and mouse 11ß-HSD1 were suggested.

Small Molecule Inhibitors of DYRK1A Identified by Computational and Experimental Approaches.

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 ß-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.

Aggregation Mechanism of Alzheimer's Amyloid ß-Peptide Mediated by α-Strand/α-Sheet Structure.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and a widespread form of dementia. Aggregated forms of the amyloid ß-peptide (Aß) are identified as a toxic species responsible for neuronal damage in AD. Extensive research has been conducted to reveal the aggregation mechanism of Aß. However, the structure of pathological aggregates and the mechanism of aggregation are not well understood. Recently, experimental studies have confirmed that the α-sheet structure in Aß drives aggregation and toxicity in AD. However, how the α-sheet structure is formed in Aß and how it contributes to Aß aggregation remains elusive. In the present study, molecular dynamics simulations suggest that Aß adopts the α-strand conformation by peptide-plane flipping. Multiple α-strands interact through hydrogen bonding to form α-sheets. This structure acts as a nucleus that initiates and promotes aggregation and fibrillation of Aß. Our findings are supported by previous experimental as well as theoretical studies. This study provides valuable structural insights for the design of anti-AD drugs exploiting the α-strand/α-sheet structure.

The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis.

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.

Pharmacophoric Site Identification and Inhibitor Design for Autotaxin.

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.

Topological Water Network Analysis Around Amino Acids.

Water molecules play a key role in protein stability, folding, function and ligand binding. Protein hydration has been studied using free energy perturbation algorithms. However, the study of protein hydration without free energy calculation is also an active field of research. Accordingly, topological water network (TWN) analysis has been carried out instead of free energy calculation in the present work to investigate hydration of proteins. Water networks around 20 amino acids in the aqueous solution were explored through molecular dynamics (MD) simulations. These simulation results were compared with experimental observations. Water molecules from the protein data bank structures showed TWN patterns similar to MD simulations. This work revealed that TWNs are effected by the surrounding environment. TWNs could provide valuable clues about the environment around amino acid residues in the proteins. The findings from this study could be exploited for TWN-based drug discovery and development.

A novel RIPK1 inhibitor that prevents retinal degeneration in a rat glaucoma model.

In glaucoma, retinal ganglion cells (RGCs) are exposed to ischemic stress with elevation of the intraocular pressure and are subsequently lost. Necroptosis, a type of regulated necrosis, is known to play a pivotal role in this loss. We observed that receptor-interacting protein kinase 1 (RIPK1), the key player of necroptosis, was activated by diverse ischemic stresses, including TCZ, chemical hypoxia (CH), and oxygen glucose deprivation (OGD). In this study, we introduce a RIPK1-inhibitory compound (RIC) with a novel scaffold. RIC inhibited downstream events following RIPK1 activation, including necrosome formation and mitochondrial dysfunction in RGC5 cells. Moreover, RIC protected RGCs against ischemic injury in the rat glaucoma model, which was induced by acute high intraocular pressure. However, RIC displayed biochemical characteristics that are distinct from those of previous RIPK1 inhibitors (necrostatin-1; Nec-1 and Compound 27; Cpd27). RIC protected RGCs against OGD insult, while Nec-1 and Cpd27 did not. Conversely, Nec-1 and Cpd27 protected RGCs from TNF-stimulated death, while RIC failed to inhibit the death of RGCs. This implies that RIPK1 activates alternative pathways depending on the context of the ischemic insults.

Design of a Novel and Selective IRAK4 Inhibitor Using Topological Water Network Analysis and Molecular Modeling Approaches.

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.

Enzyme Kinetics and Molecular Docking Studies on Cytochrome 2B6, 2C19, 2E1, and 3A4 Activities by Sauchinone.

Sauchinone, an active lignan isolated from the aerial parts of Saururus chinensis (Saururaceae), exhibits anti-inflammatory, anti-obesity, anti-hyperglycemic, and anti-hepatic steatosis effects. As herb-drug interaction (HDI) through cytochrome P450s (CYPs)-mediated metabolism limits clinical application of herbs and drugs in combination, this study sought to explore the enzyme kinetics of sauchinone towards CYP inhibition in in vitro human liver microsomes (HLMs) and in vivo mice studies and computational molecular docking analysis. In in vitro HLMs, sauchinone reversibly inhibited CYP2B6, 2C19, 2E1, and 3A4 activities in non-competitive modes, showing inhibition constant (Ki) values of 14.3, 16.8, 41.7, and 6.84 µM, respectively. Also, sauchinone time-dependently inhibited CYP2B6, 2E1 and 3A4 activities in vitro HLMs. Molecular docking study showed that sauchinone could be bound to a few key amino acid residues in the active site of CYP2B6, 2C19, 2E1, and 3A4. When sibutramine, clopidogrel, or chlorzoxazone was co-administered with sauchinone to mice, the systemic exposure of each drug was increased compared to that without sauchinone, because sauchinone reduced the metabolic clearance of each drug. In conclusion, when sauchinone was co-treated with drugs metabolized via CYP2B6, 2C19, 2E1, or 3A4, sauchinone-drug interactions occurred because sauchinone inhibited the CYP-mediated metabolic activities.

The core regulatory network in human cells.

In order to discover the common characteristics of various cell types in the human body, many researches have been conducted to find the set of genes commonly expressed in various cell types and tissues. However, the functional characteristics of a cell is determined by the complex regulatory relationships among the genes rather than by expressed genes themselves. Therefore, it is more important to identify and analyze a core regulatory network where all regulatory relationship between genes are active across all cell types to uncover the common features of various cell types. Here, based on hundreds of tissue-specific gene regulatory networks constructed by recent genome-wide experimental data, we constructed the core regulatory network. Interestingly, we found that the core regulatory network is organized by simple cascade and has few complex regulations such as feedback or feed-forward loops. Moreover, we discovered that the regulatory links from genes in the core regulatory network to genes in the peripheral regulatory network are much more abundant than the reverse direction links. These results suggest that the core regulatory network locates at the top of regulatory network and plays a role as a 'hub' in terms of information flow, and the information that is common to all cells can be modified to achieve the tissue-specific characteristics through various types of feedback and feed-forward loops in the peripheral regulatory networks. We also found that the genes in the core regulatory network are evolutionary conserved, essential and non-disease, non-druggable genes compared to the peripheral genes. Overall, our study provides an insight into how all human cells share a common function and generate tissue-specific functional traits by transmitting and processing information through regulatory network.

Design, synthesis, docking and biological evaluation of 4-phenyl-thiazole derivatives as autotaxin (ATX) inhibitors.

The autotaxin-lysophophatidic acid (ATX-LPA) signaling pathway is involved in several human diseases such as cancer, autoimmune diseases, inflammatory diseases neurodegenerative diseases and fibrotic diseases. Herein, a series of 4-phenyl-thiazole based compounds was designed and synthesized. Compounds were evaluated for their ATX inhibitory activity using FS-3 and human plasma assays. In the FS-3 assay, compounds 20 and 21 significantly inhibited the ATX at low nanomolar level (IC50=2.99 and 2.19nM, respectively). Inhibitory activity of 21 was found to be slightly better than PF-8380 (IC50=2.80nM), which is one of the most potent ATX inhibitors reported till date. Furthermore, 21 displayed higher potency (IC50=14.99nM) than the first clinical ATX inhibitor, GLPG1690 (IC50=242.00nM) in the human plasma assay. Molecular docking studies were carried out to explore the binding pattern of newly synthesized compounds within active site of ATX. Docking studies suggested the putative binding mode of the novel compounds. Good ATX inhibitory activity of 21 was attributed to the hydrogen bonding interactions with Asn230, Trp275 and active site water molecules; electrostatic interaction with catalytic zinc ion and hydrophobic interactions with amino acids of the hydrophobic pocket.

Design and synthesis of fluorescent and biotin tagged probes for the study of molecular actions of FAF1 inhibitor.

To study the molecular action of ischemic Fas-mediated cell death inhibitor, we prepared fluorescent-tagged and biotin-tagged probes of the potent inhibitor, KR-33494, of ischemic cell death. We used the molecular modeling technique to find the proper position for attaching those probes with minimum interference in the binding process of probes with Fas-mediated cell death target, FAF1.