Binding Mode Prediction Research Articles

Rational Search for Betaine/GABA Transporter 1 Inhibitors─In Vitro Evaluation of Selected Hit Compound.

Inhibitory neurotransmission mediated by γ-aminobutyric acid (GABA) plays an important role in maintaining body homeostasis. Disturbances in GABA signaling are implicated in a multitude of neurologic and psychiatric conditions, including epilepsy, ischemia, anxiety, depression, insomnia, and mood disorders. Clinically relevant increases in GABA neurotransmitter level can be achieved by inhibition of its uptake into presynaptic neurons and surrounding glial cells, driven by GABA transporters (GAT1, BGT1, GAT2, and GAT3). Herein, we focused on the search for inhibitors of the BGT1 transporter which is understudied and for which the therapeutic potential of its inhibition is partly unknown. We applied multilevel virtual screening to identify compounds with inhibitory properties. Among selected hits, compound 9 was shown to be a preferential inhibitor of BGT1 (IC50 13.9 μM). The compound also revealed some inhibitory activity against GAT3 (4x lower) while showing no or low activity (IC50 > 100 μM) toward GAT1 and GAT2, respectively. The predicted binding mode of compound 9 was confirmed by mutagenesis studies on E52A, E52Y, Q299L, and E52A+Q299L human BGT1 mutants. Subsequent evaluation showed that the selected hit displayed no affinity toward major GABAA receptor subtypes. Moreover, it was nontoxic when tested on normal human astrocytes and even showed some neuroprotective activity in SH-SY5Y cells. Compound 9 is considered a promising candidate for further evaluation of the therapeutic potential of BGT1 transporter inhibition and the development of novel inhibitors.

Mechanistic Studies of Small Molecule Ligands Selective to RNA Single G Bulges.

Small-molecule RNA binders have emerged as an important pharmacological modality. A profound understanding of the ligand selectivity, binding mode, and influential factors governing ligand engagement with RNA targets is the foundation for rational ligand design. Here, we report a novel class of coumarin derivatives exhibiting selective binding affinity towards single G RNA bulges. Harnessing the computational power of all-atom Gaussian accelerated Molecular Dynamics (GaMD) simulations, we unveiled a rare minor groove binding mode of the ligand with a key interaction between the coumarin moiety and the G bulge. This predicted binding mode is consistent with results obtained from structure-activity-relationship (SAR) studies and transverse relaxation measurements by NMR spectroscopy. We further generated 444 molecular descriptors from 69 coumarin derivatives and identified key contributors to the binding events, such as charge state and planarity, by lasso (least absolute shrinkage and selection operator) regression. Strikingly, small structure perturbations on these key contributors, such as the addition of a methyl group that disrupts the planarity of the ligand resulted in > 100-fold reduction in the binding affinity. Our work deepened the understanding of RNA-small molecule interactions and integrated a new generalizable platform for the rational design of selective small-molecule RNA binders.

Utilization of an optimized AlphaFold protein model for structure-based design of a selective HDAC11 inhibitor with anti-neuroblastoma activity.

AlphaFold is an artificial intelligence approach for predicting the three-dimensional (3D) structures of proteins with atomic accuracy. One challenge that limits the use of AlphaFold models for drug discovery is the correct prediction of folding in the absence of ligands and cofactors, which compromises their direct use. We have previously described the optimization and use of the histone deacetylase 11 (HDAC11) AlphaFold model for the docking of selective inhibitors such as FT895 and SIS17. Based on the predicted binding mode of FT895 in the optimized HDAC11 AlphaFold model, a new scaffold for HDAC11 inhibitors was designed, and the resulting compounds were tested in vitro against various HDAC isoforms. Compound 5a proved to be the most active compound with an IC50 of 365 nM and was able to selectively inhibit HDAC11. Furthermore, docking of 5a showed a binding mode comparable to FT895 but could not adopt any reasonable poses in other HDAC isoforms. We further supported the docking results with molecular dynamics simulations that confirmed the predicted binding mode. 5a also showed promising activity with an EC50 of 3.6 µM on neuroblastoma cells.

Novel efficacy of pregnane and flavonoid glycosides from Desmidorchis flava in in vivo nociceptive and inflammatory paradigms and their target prediction by cheminformatics approach

Inflammation is associated with multiple life-threatening conditions. Desmidorchis flava is an edible plant and traditionally used for managing various diseases. Three novel molecules, namely desmiflavaside-C (1), nizwaside (2), and desmiflanoside (3) were isolated from Desmidorchis flava, and their structures were confirmed by mass spectrometry and through reported literature. These compounds were in vivo examined for antinociceptive (tonic visceral nociception) and anti-inflammatory (carrageenan induced paw edema) activities. Significant antinociceptive potential was demonstrated by compound 1 at 0.5 and 1 mg/kg doses followed by compounds 2 and 3. At similar doses, significant anti-inflammatory activity was noted for all the tested compounds. Their antinociceptive and anti-inflammatory activities were comparable to the reference standards. In silico predicted binding modes suggests that these compounds may target allosteric sites of COX-1 and COX-2 enzymes to elicit their anti-inflammatory activities. These isolated natural products may have therapeutic potential in conditions afflicted with pain and inflammation.

Iron chelation capacity and prediction of peptide binding modes identified in California red worm hydrolysates and effects on iron bioavailability in differentiated Caco-2 cells

Metal-chelating peptides represent an alternative for overcoming iron deficiencies, a common nutritional disease affecting populations worldwide. This study explored the iron-chelating activity of California red worm hydrolysates (WH) and its fractions. WH was obtained by hydrolysis with Alcalase 2.4 L and fractionated using a ceramic membrane with molecular weight cutoff 3 kDa. Both the complete hydrolysate and its fractions were assessed for their in-vitro iron-chelating activity and their ability to improve iron bioavailability through ferritin formation by differentiated Caco-2 cells assay. Finally, peptides in the fraction with the highest activity were sequenced by LC-MS/MS analysis and molecular docking was performed to elucidate the chelation mechanism. The findings reveal that the smallest fraction (F < 3 kDa) exhibits the most potent iron-chelating activity with IC50 value like carnosine. Moreover, in intestinal-like differentiated Caco-2 cells, this fraction enhances iron bioavailability in a remarkable 77.9% in comparison to FeSO4 alone. On the other hand, the peptide SLLDDRLDEK identified in this fraction presents the lowest value of affinity energy (−1.5 kcal/mol) and exhibits specific interactions with iron, allowing iron ions to be trapped within its structure. These results underscore the potential of F < 3 kDa fractions from WH as promising candidates for increasing iron bioavailability and addressing iron deficiency and related health concerns.

Design, synthesis, docking studies and bioactivity evaluation of 1,2,3-triazole eugenol derivatives.

Aim: The design, synthesis, docking studies and evaluation of the in vitro antifungal and cytotoxic properties of eugenol (EUG) containing 1,2,3-triazole derivatives are reported. Most of the derivatives have not been reported.Materials & methods: The EUG derivatives were synthesized, molecular docked and tested for their antifungal activity.Results: The compounds showed potent antifungal activity against Trichophyton rubrum, associated with dermatophytosis. Compounds 2a and 2i exhibited promising results, with 2a being four-times more potent than EUG. The binding mode prediction was similar to itraconazole in the lanosterol-14-α-demethylase wild-type and G73E mutant binding sites. Additionally, the pharmacokinetic profile prediction suggests good gastrointestinal absorption and potential oral administration.Conclusion: Compound 2a is a promising antifungal agent against dermatophytosis caused by T. rubrum.

SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening.

The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.

Design, synthesis, antineoplastic activity of new pyrazolo[3,4-d]pyrimidine derivatives as dual CDK2/GSK3β kinase inhibitors; molecular docking study, and ADME prediction

In the current study, novel pyrazolo[3,4-d]pyrimidine derivatives 5a–h were designed and synthesized as targeted anti-cancer agents through dual CDK2/GSK-3β inhibition. The designed compounds demonstrated moderate to potent activity on the evaluated cancer cell lines (MCF-7 and T-47D). Compounds 5c and 5 g showed the most promising cytotoxic activity against the tested cell lines surpassing that of the used reference standard; staurosporine. On the other hand, both compounds showed good safety and tolerability on normal fibroblast cell line (MCR5). The final compounds 5c and 5 g showed a promising dual CDK2/GSK-3β inhibitory activity with IC50 of 0.244 and 0.128 μM, respectively, against CDK2, and IC50 of 0.317 and 0.160 μM, respectively, against GSK-3β. Investigating the effect of compounds 5c and 5 g on CDK2 and GSK-3β downstream cascades showed that they reduced the relative cellular content of phosphorylated RB1 and β-catenin compared to that in the untreated MCF-7 cells. Moreover, compounds 5c and 5 g showed a reasonable selective inhibition against the target kinases CDK2/GSK-3β in comparison to a set of seven off-target kinases. Furthermore, the most potent compound 5 g caused cell cycle arrest at the S phase in MCF-7 cells preventing the cells’ progression to G2/M phase inducing cell apoptosis. Molecular docking studies showed that the final pyrazolo[3,4-d]pyrimidine derivatives have analogous binding modes in the target kinases interacting with the hinge region key amino acids. Molecular dynamics simulations confirmed the predicted binding mode by molecular docking. Moreover, in silico predictions indicated their favorable physicochemical and pharmacokinetic properties in addition to their promising cytotoxic activity.

Synthesis and Biological Evaluation of a Series of New Hybrid Amide Derivatives of Triazole and Thiazolidine-2,4-dione.

A series of hybrid compounds with triazole and thiazolidine nuclei connected by a linker has been synthesized and extensively studied. Various synthetic methods for the target compounds have been tested. A microbiological assessment of the obtained compounds was carried out on strains of pathogenic fungi C. albicans, C. non-albicans, multidrug-resistant C. auris, Rhizopus arrhizus, Aspergillus spp. and some dermatophytes and other yeasts. The lowest obtained MIC values for target compounds lie between 0.003 µg/mL and 0.5 µg/mL and therefore the compounds are not inferior or several times better than commercial azole drugs. The length of the acylpiperazine linker has a limited effect on antifungal activity. Some bioisosteric analogues were tested in microbiological analysis, but turned out to be weaker than the leader in activity. The highest activity was demonstrated by a compound with para-chlorobenzylidene substituent in the thiazolidine fragment. Molecular modelling was used to predict binding modes of synthesized molecules and rationalize experimentally observed SAR. The leader compound is twice more effective in inhibiting the formation of germ tubes by Candida albicans yeast cells compared to voriconazole. An increased level of Pdr5, an azoles drug efflux pump was observed, but the increase is lower than that caused by azoles. The results can be useful for further development of more powerful and safe antifungal agents.

2-(Methyl(phenyl)amino)-N-(phenyloxyphenyl)acetamide structural motif representing a framework for selective SIRT2 inhibition.

The mammalian cytoplasmic protein SIRT2, a class III histone deacetylase family member, possesses NAD+-dependent lysine deacetylase/deacylase activity. Dysregulation of SIRT2 has been implicated in the pathogenesis of several diseases, including neurological and metabolic disorders and cancer; thus, SIRT2 emerges as a potential therapeutic target. Herein, we identified a series of diaryl acetamides (ST61-ST90) by the structural optimization of our hit STH2, followed by enhanced SIRT2 inhibitory potency and selectivity. Among them, ST72, ST85, and ST88 selectively inhibited SIRT2 with IC50 values of 9.97, 5.74, and 8.92 μM, respectively. Finally, the entire study was accompanied by in silico prediction of binding modes of docked compounds and the stability of SIRT2-ligand complexes. We hope our findings will provide substantial information for designing selective inhibitors of SIRT2.

Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction.

Structure-based virtual screening utilizes molecular docking to explore and analyze ligand-macromolecule interactions, crucial for identifying and developing potential drug candidates. Although there is availability of several widely used docking programs, the accurate prediction of binding affinity and binding mode still presents challenges. In this study, we introduced a novel protocol that combines our in-house geometry optimization algorithm, the conjugate gradient with backtracking line search (CG-BS), which is capable of restraining and constraining rotatable torsional angles and other geometric parameters with a highly accurate machine learning potential, ANI-2x, renowned for its precise molecular energy predictions reassembling the wB97X/6-31G(d) model. By integrating this protocol with binding pose prediction using the Glide, we conducted additional structural optimization and potential energy prediction on 11 small molecule-macromolecule and 12 peptide-macromolecule systems. We observed that ANI-2x/CG-BS greatly improved the docking power, not only optimizing binding poses more effectively, particularly when the RMSD of the predicted binding pose by Glide exceeded around 5 Å, but also achieving a 26% higher success rate in identifying those native-like binding poses at the top rank compared to Glide docking. As for the scoring and ranking powers, ANI-2x/CG-BS demonstrated an enhanced performance in predicting and ranking hundreds or thousands of ligands over Glide docking. For example, Pearson's and Spearman's correlation coefficients remarkedly increased from 0.24 and 0.14 with Glide docking to 0.85 and 0.69, respectively, with the addition of ANI-2x/CG-BS for optimizing and ranking small molecules binding to the bacterial ribosomal aminoacyl-tRNA receptor. These results suggest that ANI-2x/CG-BS holds considerable potential for being integrated into virtual screening pipelines due to its enhanced docking performance.

Benchmarking AlphaFold-Generated Structures of Chemokine-Chemokine Receptor Complexes.

AlphaFold and AlphaFold-Multimer have become two essential tools for the modeling of unknown structures of proteins and protein complexes. In this work, we extensively benchmarked the quality of chemokine-chemokine receptor structures generated by AlphaFold-Multimer against experimentally determined structures. Our analysis considered both the global quality of the model, as well as key structural features for chemokine recognition. To study the effects of template and multiple sequence alignment parameters on the results, a new prediction pipeline called LIT-AlphaFold (https://github.com/LIT-CCM-lab/LIT-AlphaFold) was developed, allowing extensive input customization. AlphaFold-Multimer correctly predicted differences in chemokine binding orientation and accurately reproduced the unique binding orientation of the CXCL12-ACKR3 complex. Further, the predictions of the full receptor N-terminus provided insights into a putative chemokine recognition site 0.5. The accuracy of chemokine N-terminus binding mode prediction varied between complexes, but the confidence score permitted the distinguishing of residues that were very likely well positioned. Finally, we generated a high-confidence model of the unsolved CXCL12-CXCR4 complex, which agreed with experimental mutagenesis and cross-linking data.

Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin.

Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.

Exploring Binding Pockets in the Conformational States of the SARS-CoV-2 Spike Trimers for the Screening of Allosteric Inhibitors Using Molecular Simulations and Ensemble-Based Ligand Docking.

Understanding mechanisms of allosteric regulation remains elusive for the SARS-CoV-2 spike protein, despite the increasing interest and effort in discovering allosteric inhibitors of the viral activity and interactions with the host receptor ACE2. The challenges of discovering allosteric modulators of the SARS-CoV-2 spike proteins are associated with the diversity of cryptic allosteric sites and complex molecular mechanisms that can be employed by allosteric ligands, including the alteration of the conformational equilibrium of spike protein and preferential stabilization of specific functional states. In the current study, we combine conformational dynamics analysis of distinct forms of the full-length spike protein trimers and machine-learning-based binding pocket detection with the ensemble-based ligand docking and binding free energy analysis to characterize the potential allosteric binding sites and determine structural and energetic determinants of allosteric inhibition for a series of experimentally validated allosteric molecules. The results demonstrate a good agreement between computational and experimental binding affinities, providing support to the predicted binding modes and suggesting key interactions formed by the allosteric ligands to elicit the experimentally observed inhibition. We establish structural and energetic determinants of allosteric binding for the experimentally known allosteric molecules, indicating a potential mechanism of allosteric modulation by targeting the hinges of the inter-protomer movements and blocking conformational changes between the closed and open spike trimer forms. The results of this study demonstrate that combining ensemble-based ligand docking with conformational states of spike protein and rigorous binding energy analysis enables robust characterization of the ligand binding modes, the identification of allosteric binding hotspots, and the prediction of binding affinities for validated allosteric modulators, which is consistent with the experimental data. This study suggested that the conformational adaptability of the protein allosteric sites and the diversity of ligand bound conformations are both in play to enable efficient targeting of allosteric binding sites and interfere with the conformational changes.

Investigation of the insecticidal potential of curcumin derivatives that target the Helicoverpa armigera sterol carrier protein-2

Cotton bollworm (Helicoverpa armigera) is a highly polyphagous, widely prevalent, and persistent Old World insect pest that affects numerous important crops that are directly consumed by people, including tomato, cotton, pigeon pea, chickpea, rice, sorghum, and cowpea. Insects do not synthesize steroids but obtain them from their diet. Inhibition of dietary uptake of steroids by insects is a potentially effective insecticidal mechanism that should not be toxic to humans and other mammals, who synthesize their steroids. Ten curcumin derivatives were tested against H. armigera sterol carrier protein-2 (HaSCP-2) for their potential as insecticidal agents. Curcumin derivatives were initially docked at the binding site of HaSCP-2 to determine their binding affinities and plausible binding modes. The binding modes predominantly show hydrophobic interactions of derivatives with Phe53, Phe110, and Phe89 as core interacting residues in the active site. Validation of in silico results was carried out by performing a fluorescence binding and displacement assay to determine the binding affinities of curcumin derivatives. Among a collection of curcumin derivatives tested, Cur10 showed the lowest IC50 value of 9.64 μM, while Cur07 was 19.86 μM, and Cur06 was 20.79 μM. There was a significant negative correlation between the ability of the curcumin derivatives tested to displace the fluorescent probe from the sterol binding site of HaSCP-2 and to inhibit Sf9 insect cell growth in culture, which is consistent with the curcumin derivatives acting by the novel mechanism of blocking sterol uptake. Then molecular dynamics simulation studies validated the predicted binding modes and the interactions of curcumin derivatives with HaSCP-2 protein. In conclusion, these studies support the potential use of curcumin derivatives as insecticidal agents.

The Efficacy & Molecular Mechanisms of a Terpenoid Compound Ganoderic Acid C1 on Corticosteroid-Resistant Neutrophilic Airway Inflammation: In vivo and in vitro Validation.

Neutrophil predominant airway inflammation is associated with severe and steroid-resistant asthma clusters. Previously, we reported efficacy of ASHMI, a three-herb TCM asthma formula in a steroid-resistant neutrophil-dominant murine asthma model and further identified Ganoderic Acid C1 (GAC1) as a key ASHMI active compound in vitro. The objective of this study is to investigate GAC1 effect on neutrophil-dominant, steroid-resistant asthma in a murine model. In this study, Balb/c mice were systematically sensitized with ragweed (RW) and alum and intranasally challenged with ragweed. Unsensitized/PBS challenged mice served as normal controls. Post sensitization, mice were given 4 weeks of oral treatment with GAC1 or acute dexamethasone (Dex) treatment at 48 hours prior to challenge. Pulmonary cytokines were measured by ELISA, and lung sections were processed for histology by H&E staining. Furthermore, GAC1 effect on MUC5AC expression and on reactive oxygen species (ROS) production in human lung epithelial cell line (NCI-H292) was determined by qRT-PCR and ROS assay kit, respectively. Computational analysis was applied to select potential targets of GAC1 in steroid-resistant neutrophil-dominant asthma. Molecular docking was performed to predict binding modes between GAC1 and Dex with TNF-α. The result of the study showed that chronic GAC1 treatment, significantly reduced pulmonary inflammation (P < 0.01-0.001 vs Sham) and airway neutrophilia (P < 0.01 vs Sham), inhibited TNF-α, IL-4 and IL-5 levels (P < 0.05-0.001 vs Sham). Acute Dex treatment reduced eosinophilic inflammation and IL-4, IL-5 levels, but had no effect on neutrophilia and TNF-α production. GAC1 treated H292 cells showed decreased MUC5AC gene expression and production of ROS (P < 0.001 vs stimulated/untreated cells). Molecular docking results showed binding energy of complex GAC1-TNF was -10.8 kcal/mol. GAC1 may be a promising anti-asthma botanical drug for treatment of steroid-resistant asthma.

Variability of Isoquinoline Alkaloid Profiles and Anticholinesterase Activities with Binding-Mode Predictions of Glaucium flavum Population.

In this study, phytochemical and biological activity studies supported by docking were carried out on a species of the genus Glaucium, a repository of isoquinoline alkaloids. The GC-MS (Gas Chromatography-Mass Spectrometry) method is used to characterize the isoquinoline alkaloids of Glaucium flavum Crantz. (Papaveraceae). G. flavum was collected from seven different regions of Türkiye (Antalya, Urla-Izmir, Mordogan-Izmir, Mugla, Assos-Canakkale, Karabiga-Canakkale, Giresun) and totally 17 compounds were detected by GC-MS. Glaucine was found to be the major constituent in the sample collected from Mugla, whereas isocorydine was recorded to be the principal alkaloid in other samples. Further fractionation studies on G. flavum collected from Antalya province in Southwestern Türkiye, yielded five major alkaloids (isocorydine 1, dihydrosanguinarine 2, glaucine 3, dehydroglaucine 4, protopine 5) which were characterized by spectroscopic methods. Anticholinesterase activities of the extracts and isolated alkaloids were also tested by in vitro Ellman method. The isolated compounds were also analyzed by a molecular docking technique to determine the binding orientations in the gorge of the active site of acetylcholinesterase (AChE) and a homology model of butyrylcholinesterase (BuChE). This is the first comparative investigation of the phytochemical composition and biodiversity of Glaucium flavum species growing in Türkiye.

Computational analyses of drug resistance mutations in katG and emb complexes in Mycobacterium tuberculosis.

The number of antibiotic resistant pathogens is increasing rapidly, and with this comes a substantial socioeconomic cost that threatens much of the world. To alleviate this problem, we must use antibiotics in a more responsible and informed way, further our understanding of the molecular basis of drug resistance, and design new antibiotics. Here, we focus on a key drug-resistant pathogen, Mycobacterium tuberculosis, and computationally analyze trends in drug-resistant mutations in genes of the proteins embA, embB, embC, and katG, which play essential roles in the action of the first-line drugs ethambutol and isoniazid. We use docking to predict binding modes of isoniazid to katG that agree with suggested binding sites found in our laboratory using cryo-EM. Using mutant stability predictions, we recapitulate the idea that resistance occurs when katG's heme cofactor is destabilized rather than due to a decrease in affinity to isoniazid. Conversely, we have identified resistance mutations that affect the affinity of ethambutol more drastically than the affinity of the natural substrate of embB. With this, we illustrate that we can distinguish between the two types of drug resistance-cofactor destabilization and drug affinity reduction-suggesting potential uses in the prediction of novel drug-resistant mutations.

New thymol-derived triazole exhibits promising activity against Trichophyton rubrum.

Fungal infections have emerged worldwide, and azole antifungals are widely used to control these infections. However, the emergence of antifungal resistance has been compromising the effectiveness of these drugs. Therefore, the objective of this study was to evaluate the antifungal and cytotoxic activities of the nine new 1,2,3 triazole compounds derived from thymol that were synthesized through Click chemistry. The binding mode prediction was carried out by docking studies using the crystallographic structure of Lanosterol 14α-demethylase G73E mutant from Saccharomyces cerevisiae. The new compounds showed potent antifungal activity against Trichophyton rubrum but did not show relevant action against Aspergillus fumigatus and Candida albicans. For T. rubrum, molecules nº 5 and 8 showed promising results, emphasizing nº 8, whose fungicidal and fungistatic effects were similar to fluconazole. In addition, molecule nº 8 showed low toxicity for keratinocytes and fibroblasts, concluding that this compound demonstrates promising characteristics for developing a new drug for dermatophytosis caused by T. rubrum, or serves as a structural basis for further research.

Discovery of Novel Steroid-Based Histamine H3 Receptor Antagonists/Inverse Agonists.

Steroid-based histamine H3 receptor antagonists (d-homoazasteroids) were designed by combining distinct structural elements of HTS hit molecules. They were characterized, and several of them displayed remarkably high affinity for H3 receptors with antagonist/inverse agonist features. Especially, the 17a-aza-d-homolactam chemotype demonstrated excellent H3R activity together with significant in vivo H3 antagonism. Optimization of the chemotype was initiated with special emphasis on the elimination of the hERG and muscarinic affinity. Additionally, ligand-based SAR considerations and molecular docking studies were performed to predict binding modes of the molecules. The most promising compounds (XXI, XXVIII, and XX) showed practically no muscarinic and hERG affinity. They showed antagonist/inverse agonist property in the in vitro functional tests that was apparent in the rat in vivo dipsogenia test. They were considerably stable in human and rat liver microsomes and provided significant in vivo potency in the place recognition and novel object recognition cognitive paradigms.