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.

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.

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.

TWN-FS method: A novel fragment screening method for drug discovery.

Fragment-based drug discovery (FBDD) is a well-established and effective method for generating diverse and novel hits in drug design. Kinases are suitable targets for FBDD due to their well-defined structure. Water molecules contribute to structure and function of proteins and also influence the environment within the binding pocket. Water molecules form a variety of hydrogen-bonded cyclic water-ring networks, collectively known as topological water networks (TWNs). Analyzing the TWNs in protein binding sites can provide valuable insights into potential locations and shapes for fragments within the binding site. Here, we introduce TWN-based fragment screening (TWN-FS) method, a novel screening method that suggests fragments through grouped TWN analysis within the protein binding site. We used this method to screen known CDK2, CHK1, IGF1R and ERBB4 inhibitors. Our findings suggest that TWN-FS method has the potential to effectively screen fragments. The TWN-FS method package is available on GitHub at https://github.com/pkj0421/TWN-FS.

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.

TWN-RENCOD: A novel method for protein binding site comparison.

Several diverse proteins possess similar binding sites. Protein binding site comparison provides valuable insights for the drug discovery and development. Binding site similarities are useful in understanding polypharmacology, identifying potential off-targets and repurposing of known drugs. Many binding site analysis and comparison methods are available today, however, these methods may not be adequate to explain variation in the activity of a drug or a small molecule against a number of similar proteins. Water molecules surrounding the protein surface contribute to structure and function of proteins. Water molecules form diverse types of hydrogen-bonded cyclic water-ring networks known as topological water networks (TWNs). Analysis of TWNs in binding site of proteins may improve understanding of the characteristics of binding sites. We propose TWN-based residue encoding (TWN-RENCOD), a novel binding site comparison method which compares the aqueous environment in binding sites of similar proteins. As compared to other existing methods, results obtained using our method correlated better with differences in wide range of activity of a known drug (Sunitinib) against nine different protein kinases (KIT, PDGFRA, VEGFR2, PHKG2, ITK, HPK1, MST3, PAK6 and CDK2).

Design and Synthesis of a Novel 4-aryl-N-(2-alkoxythieno [2,3-b]pyrazine-3-yl)-4-arylpiperazine-1-carboxamide DGG200064 Showed Therapeutic Effect on Colon Cancer through G2/M Arrest.

Cancer cells are characterized by an abnormal cell cycle. Therefore, the cell cycle has been a potential target for cancer therapeutic agents. We developed a new lead compound, DGG200064 (7c) with a 2-alkoxythieno [2,3-b]pyrazine-3-yl)-4-arylpiperazine-1-carboxamide core skeleton. To evaluate its properties, compound DGG200064 was tested in vivo through a xenograft mouse model of colorectal cancer using HCT116 cells. The in vivo results showed high cell growth inhibition efficacy. Our results confirmed that the newly synthesized DGG200064 inhibits the growth of colorectal cancer cells by inducing G2/M arrest. Unlike the known cell cycle inhibitors, DGG200064 (GI50 = 12 nM in an HCT116 cell-based assay) induced G2/M arrest by selectively inhibiting the interaction of FBXW7 and c-Jun proteins. Additionally, the physicochemical properties of the lead compounds were analyzed. Based on the results of the study, we suggested further development of DGG200064 as a novel oral anti-colorectal cancer drug.

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.

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.

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.

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.

Systematic Target Screening Revealed That Tif302 Could Be an Off-Target of the Antifungal Terbinafine in Fission Yeast.

We used a heterozygous gene deletion library of fission yeasts comprising all essential and non-essential genes for a microarray screening of target genes of the antifungal terbinafine, which inhibits ergosterol synthesis via the Erg1 enzyme. We identified 14 heterozygous strains corresponding to 10 non-essential [7 ribosomal-protein (RP) coding genes, spt7, spt20, and elp2] and 4 essential genes (tif302, rpl2501, rpl31, and erg1). Expectedly, their erg1 mRNA and protein levels had decreased compared to the control strain SP286. When we studied the action mechanism of the non-essential target genes using cognate haploid deletion strains, knockout of SAGA-subunit genes caused a down-regulation in erg1 transcription compared to the control strain ED668. However, knockout of RP genes conferred no susceptibility to ergosterol-targeting antifungals. Surprisingly, the RP genes participated in the erg1 transcription as components of repressor complexes as observed in a comparison analysis of the experimental ratio of erg1 mRNA. To understand the action mechanism of the interaction between the drug and the novel essential target genes, we performed isobologram assays with terbinafine and econazole (or cycloheximide). Terbinafine susceptibility of the tif302 heterozygous strain was attributed to both decreased erg1 mRNA levels and inhibition of translation. Moreover, Tif302 was required for efficacy of both terbinafine and cycloheximide. Based on a molecular modeling analysis, terbinafine could directly bind to Tif302 in yeasts, suggesting Tif302 as a potential off-target of terbinafine. In conclusion, this genome-wide screening system can be harnessed for the identification and characterization of target genes under any condition of interest.

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.

High-Throughput Platform for Real-Time Monitoring of ATP-Generating Enzymes in Living Cells Based on a Lanthanide Probe.

Remarkable variation between cell-free and cellular measurements of enzyme activity triggered the unmet need to develop tools for monitoring enzyme activity in living cells. Such tools will advance our understanding of the biological functions of enzymes and their potential impact on drug discovery. We report in this study a universal assay for monitoring ATP-generating enzymes in living cells using a self-assembled Tb3+ complex probe. Modulation of the rheological properties of cell culture media enabled shifting the lifetime of the Tb3+ complex in the presence of ATP from micro-to-millisecond range. Based on the response of the Tb3+ complex to ATP, cellular assays for 5 ATP-generating enzymes were developed. Remarkably, assessment of the activity of these enzymes in living cells is made possible for the first time. The pyruvate kinase M2 (PKM2) assay has been optimized for high-throughput screening (HTS) and further implemented in the identification of novel scaffolds as PKM2 inhibitors.

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.

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.

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.

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.

Inhibition of ß-site amyloid precursor protein cleaving enzyme 1 and cholinesterases by pterosins via a specific structure-activity relationship with a strong BBB permeability.

We extracted 15 pterosin derivatives from Pteridium aquilinum that inhibited ß-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 Aß peptides from neuroblastoma cells that overexpressed human ß-amyloid precursor protein at 500 µM. Conclusively, our study suggested that several pterosins are potential scaffolds for multitarget-directed ligands (MTDLs) for AD therapeutics.