Exploration of novel human neutrophil elastase inhibitors from natural compounds: virtual screening, in vitro, molecular dynamics simulation and in vivo study.

BACKGROUND: Human neutrophil elastase (HNE) is an important contributor to lung diseases such as acute lung injury (ALI) or acute respiratory distress syndrome. Therefore, this study aimed to identify natural HNE inhibitors with anti-inflammatory activity through machine learning algorithms, in vitro assays, molecular dynamic simulation, and an in vivo ALI assay. METHODS: Based on the optimized Discovery Studio two-dimensional molecular descriptors, combined with different molecular fingerprints, six machine learning models were established using the Naïve Bayesian (NB) method to identify HNE inhibitors. Subsequently, the optimal model was utilized to screen 6,925 drug-like compounds obtained from the Traditional Chinese Medicine Systems Pharmacy Database and Analysis Platform (TCMSP), followed by ADMET analysis. Finally, 10 compounds with reported anti-inflammatory activity were selected to determine their inhibitory activities against HNE in vitro, and the compounds with the best activity were selected for a 100 ns molecular dynamics simulation and its anti-inflammatory effect was evaluated using Poly(I:C)-induced ALI model. RESULTS: The evaluation of the in vitro HNE inhibition efficiency of the 10 selected compounds showed that the flavonoid tricetin had the strongest inhibitory effect on HNE. The molecular dynamics simulation indicated that the binding of tricetin to HNE was relatively stable throughout the simulation. Importantly, in vivo experiments indicated that tricetin treatment substantially improved the Poly(I:C)-induced ALI. CONCLUSION: The proposed NB model was proved valuable for exploring novel HNE inhibitors, and natural tricetin was screened out as a novel HNE inhibitor, which was confirmed by in vitro and in vivo assays for its inhibitory activities.

Structure-based screening, optimization and biological evaluation of novel chrysin-based derivatives as selective PPARγ modulators for the treatment of T2DM and hepatic steatosis.

In consideration of several serious side effects induced by the classical AF-2 involved "lock" mechanism, recently disclosed PPARγ-Ser273 phosphorylation mode of action has become an alternative and mainstream mechanism for currently PPARγ-based drug discovery and development with an improved therapeutic index. In this study, by virtue of structure-based virtual high throughput screening (SB-VHTS), structurally chemical optimization by targeting the inhibition of the PPARγ-Ser273 phosphorylation as well as in vitro biological evaluation, which led to the final identification of a chrysin-based potential hit (YGT-31) as a novel selective PPARγ modulator with potent binding affinity and partial agonism. Further in vivo evaluation demonstrated that YGT-31 possessed potent glucose-lowering and relieved hepatic steatosis effects without involving the TZD-associated side effects. Mechanistically, YGT-31 presented such desired therapeutic index, mainly because it effectively inhibited the CDK5-mediated PPARγ-Ser273 phosphorylation, selectively elevated the level of insulin sensitivity-related Glut4 and adiponectin but decreased the expression of insulin-resistance-associated genes PTP1B and SOCS3 as well as inflammation-linked genes IL-6, IL-1ß and TNFα. Finally, the molecular docking study was also conducted to uncover an interesting hydrogen-bonding network of YGT-31 with PPARγ-Ser273 phosphorylation-related key residues Ser342 and Glu343, which not only gave a clear verification for our targeting modification but also provided a proof of concept for the abovementioned molecular mechanism.

Nilotinib: Disrupting the MYC-MAX Heterocomplex.

MYC is a transcription factor crucial for maintaining cellular homeostasis, and its dysregulation is associated with highly aggressive cancers. Despite being considered "undruggable" due to its unstable protein structure, MYC gains stability through its interaction with its partner protein, MAX. The MYC-MAX heterodimer orchestrates the expression of numerous genes that contribute to an oncogenic phenotype. Previous efforts to develop small molecules, disrupting the MYC-MAX interaction, have shown promise in vitro but none have gained clinical approval. Our current computer-aided study utilizes an approach to explore drug repurposing as a strategy for inhibiting the c-MYC-MAX interaction. We have focused on compounds from DrugBank library, including Food and Drug Administration-approved drugs or those under investigation for other medical conditions. First, we identified a potential druggable site on flat interface of the c-MYC protein, which served as the target for virtual screening. Using both activity-based and structure-based screening, we comprehensively assessed the entire DrugBank library. Structure-based virtual screening was performed on AutoDock Vina and Glide docking tools, while activity-based screening was performed on two independent quantitative structure-activity relationship models. We focused on the top 2% of hit molecules from all screening methods. Ultimately, we selected consensus molecules from these screenings-those that exhibited both a stable interaction with c-MYC and superior inhibitory activity against c-MYC-MAX interaction. Among the evaluated molecules, we identified a protein kinase inhibitor (tyrosine kinase inhibitor [TKI]) known as nilotinib as a promising candidate targeting c-MYC-MAX dimer. Molecular dynamic simulations demonstrated a stable interaction between MYC and nilotinib. The interaction with nilotinib led to the stabilization of a region of the MYC protein that is distorted in apo-MYC and is important for MAX binding. Further analysis of differentially expressed gene revealed that nilotinib, uniquely among the tested TKIs, induced a gene expression program in which half of the genes were known to be responsive to c-MYC. Our findings provide the foundation for subsequent in vitro and in vivo investigations aimed at evaluating the efficacy of nilotinib in managing MYC oncogenic activity.

Discovery of a dual-target DYRK2 and HDAC8 inhibitor for the treatment of hepatocellular carcinoma.

Dual-specificity tyrosine phosphorylation-regulated kinase 2 (DYRK2) and histone deacetylase 8 (HDAC8) have been shown to be associated with the development of several cancers. Here, we identified a dual-target DYRK2/HDAC8 inhibitor (DYC-1) through a combined virtual screening protocol. DYC-1 exhibited nanomolar inhibitory activity against both DYRK2 (IC50 = 5.27 ± 0.13 nM) and HDAC8 (IC50 = 8.06 ± 0.47 nM). Molecular dynamics simulations showed that DYC-1 had positive binding stability with DYRK2 and HDAC8. Importantly, the cytotoxicity assay indicated that DYC-1 exhibited superior antiproliferative activity against human liver cancer, especially SK-HEP-1 cells, and had no significant inhibition on normal liver cells. Moreover, DYC-1 showed a strong inhibitory effect on the growth of SK-HEP-1 xenograft tumors with no significant side effects. These data suggest that DYC-1 is a high-efficacy and low-toxic antitumor agent for the treatment of hepatocellular carcinoma.

Discovery of novel natural-product-derived mutant isocitrate dehydrogenases 1 inhibitors: Structure-based virtual screening, biological evaluation and structure-activity relationship study.

Mutations in IDH1 are commonly observed across various cancers, causing the conversion of α-KG to 2-HG. Elevated levels of 2-HG disrupt histone and DNA demethylation processes, promoting tumor development. Consequently, there is substantial interest in developing small molecule inhibitors targeting the mutant enzymes. Herein, we report a structure-based high-throughput virtual screening strategy using a natural products library, followed by hit-to-lead optimization. Through this process, we discover a potent compound, named 11s, which exhibited significant inhibition to IDH1 R132H and IDH1 R132C with IC50 values of 124.4 and 95.7 nM, respectively. Furthermore, 11s effectively reduced 2-HG formation, with EC50 values of 182 nM in U87 R132H cell, and 84 nM in HT-1080 cell. In addition, 11s significantly reduced U87 R132H and HT-1080 cell proliferation with GC50 values of 3.48 and 1.38 µM, respectively. PK-PD experiments further confirmed that compound 11s significantly decreased 2-HG formation in an HT-1080 xenograft mouse model, resulting in notable suppression of tumor growth without apparent loss in body weight.

Discovery of an EP300 Inhibitor using Structure-based Virtual Screening and Bioactivity Evaluation.

BACKGROUND: EP300 (E1A binding protein p300) played a significant role in serial diseases such as cancer, neurodegenerative disease. Therefore, it became a significant target. METHODS: Targeting EP300 discovery of a novel drug to alleviate these diseases. In this paper, 17 candidate compounds were obtained using a structure-based virtual screening approach, 4449-0460, with an IC50 of 5.89 ± 2.08 uM, which was identified by the EP300 bioactivity test. 4449-0460 consisted of three rings. The middle benzene ring connected the 5-ethylideneimidazolidine-2,4-dione group and the 3-F-Phenylmethoxy group. RESULTS: Furthermore, the interaction mechanism between 4449-0460 and EP300 was explored by combining molecular dynamics (MD) simulations and binding free energy calculation methods. CONCLUSION: The binding free energy of EP300 with 4449-0460 was -10.93 kcal/mol, and mainly came from the nonpolar energy term (ΔGnonpolar). Pro1074, Phe1075, Val1079, Leu1084, and Val1138 were the key residues in EP300/4449-0460 binding with more -1 kcal/mol energy contribution. 4449-0460 was a promising inhibitor targeting EP300, which had implications for the development of drugs for EP300-related diseases.

In silico identification of a novel Cdc2-like kinase 2 (CLK2) inhibitor in triple negative breast cancer.

Dysregulation of RNA splicing processes is intricately linked to tumorigenesis in various cancers, especially breast cancer. Cdc2-like kinase 2 (CLK2), an oncogenic RNA-splicing kinase pivotal in breast cancer, plays a significant role, particularly in the context of triple-negative breast cancer (TNBC), a subtype marked by substantial medical challenges due to its low survival rates. In this study, we employed a structure-based virtual screening (SBVS) method to identify potential CLK2 inhibitors with novel chemical structures for treating TNBC. Compound 670551 emerged as a novel CLK2 inhibitor with a 50% inhibitory concentration (IC50) value of 619.7 nM. Importantly, Compound 670551 exhibited high selectivity for CLK2 over other protein kinases. Functionally, this compound significantly reduced the survival and proliferation of TNBC cells. Results from a cell-based assay demonstrated that this inhibitor led to a decrease in RNA splicing proteins, such as SRSF4 and SRSF6, resulting in cell apoptosis. In summary, we identified a novel CLK2 inhibitor as a promising potential treatment for TNBC therapy.

Structure-Based Virtual Screening for Methyltransferase Inhibitors of SARS-CoV-2 nsp14 and nsp16.

The ongoing COVID-19 pandemic still threatens human health around the world. The methyltransferases (MTases) of SARS-CoV-2, specifically nsp14 and nsp16, play crucial roles in the methylation of the N7 and 2'-O positions of viral RNA, making them promising targets for the development of antiviral drugs. In this work, we performed structure-based virtual screening for nsp14 and nsp16 using the screening workflow (HTVS, SP, XP) of Schrödinger 2019 software, and we carried out biochemical assays and molecular dynamics simulation for the identification of potential MTase inhibitors. For nsp14, we screened 239,000 molecules, leading to the identification of three hits A1-A3 showing N7-MTase inhibition rates greater than 60% under a concentration of 50 µM. For the SAM binding and nsp10-16 interface sites of nsp16, the screening of 210,000 and 237,000 molecules, respectively, from ZINC15 led to the discovery of three hit compounds B1-B3 exhibiting more than 45% of 2'-O-MTase inhibition under 50 µM. These six compounds with moderate MTase inhibitory activities could be used as novel candidates for the further development of anti-SARS-CoV-2 drugs.

An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors.

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC50 value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.

Bioinformatics Study of Flavonoids From Genus Erythrina As Ace2 inhibitor Candidates For Covid-19 Treatment.

Purpose: This study aimed to screen potential drug candidates from the flavonoids of the genus Erythrina for the Corona Virus Disease 2019 (COVID-19) treatment. Patients and Methods: A comprehensive screening was conducted on the structures of 473 flavonoids derived from the genus Erythrina, focusing on their potential toxicity and pharmacokinetic profiles. Subsequently, flavonoids that were non-toxic and possessed favorable pharmacokinetic properties underwent further analysis to explore their interactions with the angiotensin-converting enzyme 2 (ACE2) receptor, employing molecular docking and molecular dynamics simulations. Results: Among 473 flavonoids, 104 were predicted to be safe from being mutagenic, hepatotoxic, and inhibitors of the human ether-a-go-go-related gene (hERG). Among these 104 flavonoids, 18 compounds were predicted not to be substrates of P-glycoprotein (P-gp). Among these 18 flavonoids, gangetinin (471) and erybraedin D (310) exhibit low binding affinities and root mean square deviation (RMSD) values, indicating stable binding to the ACE2 receptor. The physicochemical attributes of compounds 310 and 471 suggest that they possess drug-like properties. Conclusion: Gangetinin (471) and erybraedin D (310) may serve as promising candidates for COVID-19 treatment due to their potential to inhibit the ACE2-RBD interaction. This warrants further investigation into their inhibitory effects on ACE2-RBD binding through in vitro experiments.

EGC enhances tumor antigen presentation and CD8+ T cell-mediated antitumor immunity via targeting oncoprotein SND1.

The Staphylococcal nuclease and Tudor domain containing 1 (SND1) has been identified as an oncoprotein. Our previous study demonstrated that SND1 impedes the major histocompatibility complex class I (MHC-I) assembly by hijacking the nascent heavy chain of MHC-I to endoplasmic reticulum-associated degradation. Herein, we aimed to identify inhibitors to block SND1-MHC-I binding, to facilitate the MHC-I presentation and tumor immunotherapy. Our findings validated the importance of the K490-containing sites in SND1-MHC-I complex. Through structure-based virtual screening and docking analysis, (-)-Epigallocatechin (EGC) exhibited the highest docking score to prevent the binding of MHC-I to SND1 by altering the spatial conformation of SND1. Additionally, EGC treatment resulted in increased expression levels of membrane-presented MHC-I in tumor cells. The C57BL/6J murine orthotopic melanoma model validated that EGC increases infiltration and activity of CD8+ T cells in both the tumor and spleen. Furthermore, the combination of EGC with programmed death-1 (PD-1) antibody demonstrated a superior antitumor effect. In summary, we identified EGC as a novel inhibitor of SND1-MHC-I interaction, prompting MHC-I presentation to improve CD8+ T cell response within the tumor microenvironment. This discovery presents a promising immunotherapeutic candidate for tumors.

Inferring molecular inhibition potency with AlphaFold predicted structures.

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

A combination of virtual screening, molecular dynamics simulation, MM/PBSA, ADMET, and DFT calculations to identify a potential DPP4 inhibitor.

DPP4 inhibitors can control glucose homeostasis by increasing the level of GLP-1 incretins hormone due to dipeptidase mimicking. Despite the potent effects of DPP4 inhibitors, these compounds cause unwanted toxicity attributable to their effect on other enzymes. As a result, it seems essential to find novel and DPP4 selective compounds. In this study, we introduce a potent and selective DPP4 inhibitor via structure-based virtual screening, molecular docking, molecular dynamics simulation, MM/PBSA calculations, DFT analysis, and ADMET profile. The screened compounds based on similarity with FDA-approved DPP4 inhibitors were docked towards the DPP4 enzyme. The compound with the highest docking score, ZINC000003015356, was selected. For further considerations, molecular docking studies were performed on selected ligands and FDA-approved drugs for DPP8 and DPP9 enzymes. Molecular dynamics simulation was run during 200 ns and the analysis of RMSD, RMSF, Rg, PCA, and hydrogen bonding were performed. The MD outputs showed stability of the ligand-protein complex compared to available drugs in the market. The total free binding energy obtained for the proposed DPP4 inhibitor was more negative than its co-crystal ligand (N7F). ZINC000003015356 confirmed the role of the five Lipinski rule and also, have low toxicity parameter according to properties. Finally, DFT calculations indicated that this compound is sufficiently soft.

Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors.

Poly ADP-ribose polymerase 1 (PARP1) is an attractive therapeutic target for cancer treatment. Machine-learning scoring functions constitute a promising approach to discovering novel PARP1 inhibitors. Cutting-edge PARP1-specific machine-learning scoring functions were investigated using semi-synthetic training data from docking activity-labelled molecules: known PARP1 inhibitors, hard-to-discriminate decoys property-matched to them with generative graph neural networks and confirmed inactives. We further made test sets harder by including only molecules dissimilar to those in the training set. Comprehensive analysis of these datasets using five supervised learning algorithms, and protein-ligand fingerprints extracted from docking poses and ligand only features revealed one highly predictive scoring function. This is the PARP1-specific support vector machine-based regressor, when employing PLEC fingerprints, which achieved a high Normalized Enrichment Factor at the top 1% on the hardest test set (NEF1% = 0.588, median of 10 repetitions), and was more predictive than any other investigated scoring function, especially the classical scoring function employed as baseline.

Raloxifene-driven benzothiophene derivatives: Discovery, structural refinement, and biological evaluation as potent PPARγ modulators based on drug repurposing.

By virtue of the drug repurposing strategy, the anti-osteoporosis drug raloxifene was identified as a novel PPARγ ligand through structure-based virtual high throughput screening (SB-VHTS) of FDA-approved drugs and TR-FRET competitive binding assay. Subsequent structural refinement of raloxifene led to the synthesis of a benzothiophene derivative, YGL-12. This compound exhibited potent PPARγ modulation with partial agonism, uniquely promoting adiponectin expression and inhibiting PPARγ Ser273 phosphorylation by CDK5 without inducing the expression of adipongenesis associated genes, including PPARγ, aP2, CD36, FASN and C/EBPα. This specific activity profile resulted in effective hypoglycemic properties, avoiding major TZD-related adverse effects like weight gain and hepatomegaly, which were demonstrated in db/db mice. Molecular docking studies showed that YGL-12 established additional hydrogen bonds with Ile281 and enhanced hydrogen-bond interaction with Ser289 as well as PPARγ Ser273 phosphorylation-related residues Ser342 and Glu343. These findings suggested YGL-12 as a promising T2DM therapeutic candidate, thereby providing a molecular framework for the development of novel PPARγ modulators with an enhanced therapeutic index.

Repurposing of Compounds from Streptomyces spp. as Potential Inhibitors of Aminoacyltransferase FemA: An Essential Drug Target against Drug-resistant Staphylococcus aureus.

BACKGROUND: Drug-resistant Staphylococcus aureus represents a substantial healthcare challenge worldwide, and its range of available therapeutic options continues to diminish progressively. Thus, this study aimed to identify potential inhibitors against FemA, a crucial protein involved in the cell wall biosynthesis of S. aureus. MATERIALS AND METHODS: The screening process involved a comprehensive structure-based virtual screening on the StreptomDB database to identify ligands with potential inhibitory effects on FemA using AutoDock Vina. The most desirable ligands with the highest binding affinity and pharmacokinetic properties were selected. Two ligands with the highest number of hydrogen bonds and hydrophobic interactions were further analyzed by molecular dynamics (MD) using the GROMACS version 2018 simulation package. RESULTS: Six H-donor conserved residues were selected as protein active sites, including Arg- 220, Tyr-38, Gln-154, Asn-73, Arg-74, and Thr-24. Through virtual screening, a total of nine compounds with the highest binding affinity to the FemA protein were identified. Frigocyclinone and C21H21N3O4 exhibited the highest binding affinity and demonstrated favorable pharmacokinetic properties. Molecular dynamics analysis of the FemA-ligand complexes further indicated desirable stability and reliability of complexes, reinforcing the potential efficacy of these ligands as inhibitors of FemA protein. CONCLUSION: Our findings suggest that Frigocyclinone and C21H21N3O4 are promising inhibitors of FemA in S. aureus. To further validate these computational results, experimental studies are planned to confirm the inhibitory effects of these compounds on various S. aureus strains. Combining computational screening with experimental validation contributes valuable insights to the field of drug discovery in comparison to the classical drug discovery approaches.

PL-PatchSurfer3: Improved Structure-Based Virtual Screening for Structure Variation Using 3D Zernike Descriptors.

Structure-based virtual screening (SBVS) is a widely used method in silico drug discovery. It necessitates a receptor structure or binding site to predict the binding pose and fitness of a ligand. Therefore, the performance of the SBVS is affected by the protein conformation. The most frequently used method in SBVS is the protein-ligand docking program, which utilizes atomic distance-based scoring functions. Hence, they are highly prone to sensitivity towards variation in receptor structure, and it is reported that the conformational change significantly drops the performance of the docking program. To address the problem, we have introduced a novel program of SBVS, named PL-PatchSurfer. This program makes use of molecular surface patches and the Zernike descriptor. The surfaces of the pocket and ligand are segmented into several patches by the program. These patches are then mapped with physico-chemical properties such as shape and electrostatic potential before being converted into the Zernike descriptor, which is rotationally invariant. A complementarity between the protein and the ligand is assessed by comparing the descriptors and geometric distribution of the patches in the molecules. A benchmarking study showed that PL-PatchSurfer2 was able to screen active molecules regardless of the receptor structure change with fast speed. However, the program could not achieve high performance for the targets that the hydrogen bonding feature is important such as nuclear hormone receptors. In this paper, we present the newer version of PL-PatchSurfer, PL-PatchSurfer3, which incorporates two new features: a change in the definition of hydrogen bond complementarity and consideration of visibility that contains curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while retaining its characteristic tolerance to receptor structure changes. Interested individuals can access the program at kiharalab.org/plps3.

Unveiling Novel Urease Inhibitors for Helicobacter pylori: A Multi-Methodological Approach from Virtual Screening and ADME to Molecular Dynamics Simulations.

Helicobacter pylori (Hp) infections pose a global health challenge demanding innovative therapeutic strategies by which to eradicate them. Urease, a key Hp virulence factor hydrolyzes urea, facilitating bacterial survival in the acidic gastric environment. In this study, a multi-methodological approach combining pharmacophore- and structure-based virtual screening, molecular dynamics simulations, and MM-GBSA calculations was employed to identify novel inhibitors for Hp urease (HpU). A refined dataset of 8,271,505 small molecules from the ZINC15 database underwent pharmacokinetic and physicochemical filtering, resulting in 16% of compounds for pharmacophore-based virtual screening. Molecular docking simulations were performed in successive stages, utilizing HTVS, SP, and XP algorithms. Subsequent energetic re-scoring with MM-GBSA identified promising candidates interacting with distinct urease variants. Lys219, a residue critical for urea catalysis at the urease binding site, can manifest in two forms, neutral (LYN) or carbamylated (KCX). Notably, the evaluated molecules demonstrated different interaction and energetic patterns in both protein variants. Further evaluation through ADMET predictions highlighted compounds with favorable pharmacological profiles, leading to the identification of 15 candidates. Molecular dynamics simulations revealed comparable structural stability to the control DJM, with candidates 5, 8 and 12 (CA5, CA8, and CA12, respectively) exhibiting the lowest binding free energies. These inhibitors suggest a chelating capacity that is crucial for urease inhibition. The analysis underscores the potential of CA5, CA8, and CA12 as novel HpU inhibitors. Finally, we compare our candidates with the chemical space of urease inhibitors finding physicochemical similarities with potent agents such as thiourea.

Novel inhibitors targeting the PGK1 metabolic enzyme in glycolysis exhibit effective antitumor activity against kidney renal clear cell carcinoma in vitro and in vivo.

Our previous research has revealed phosphoglycerate kinase 1 (PGK1) enhances tumorigenesis and sorafenib resistance of kidney renal clear cell carcinoma (KIRC) by regulating glycolysis, so that PGK1 is a promising drug target. Herein we performed structure-based virtual screening and series of anticancer pharmaceutical experiments in vitro and in vivo to identify novel small-molecule PGK1-targeted compounds. As results, the compounds CHR-6494 and Z57346765 were screened and confirmed to specifically bind to PGK1 and significantly reduced the metabolic enzyme activity of PGK1 in glycolysis, which inhibited KIRC cell proliferation in a dose-dependent manner. While CHR-6494 showed greater anti-KIRC efficacy and fewer side effects than Z57346765 on nude mouse xenograft model. Mechanistically, CHR-9464 impeded glycolysis by decreasing the metabolic enzyme activity of PGK1 and suppressed histone H3T3 phosphorylation to inhibit KIRC cell proliferation. Z57346765 induced expression changes of genes related to cell metabolism, DNA replication and cell cycle. Overall, we screened two novel PGK1 inhibitors, CHR-6494 and Z57346765, for the first time and discovered their potent anti-KIRC effects by suppressing PGK1 metabolic enzyme activity in glycolysis.

The in vitro and in vivo antiviral effects of IGF1R inhibitors against respiratory syncytial virus infection.

The insulin-like growth factor 1 receptor (IGF1R) was recognized as a pivotal receptor that facilitated the cellular entry of RSV. Small molecule inhibitors designed to target IGF1R exhibited potential as potent antiviral agents. Through virtual screening, we conducted a screening process involving small molecule compounds derived from natural products, aiming to target the IGF1R protein against respiratory syncytial virus infection. The molecular dynamics simulation analysis showed that tannic acid and daptomycin interacted with the IGF1R. The experimental results in vivo and in vitro showed that tannic acid and daptomycin had anti-RSV infection potential through reducing viral loads, inflammation, airway resistance and protecting alveolar integrity. The CC50 values of tannic acid and daptomycin were 6 nM and 0.45 µM, respectively. Novel small-molecule inhibitors targeting the IGF1R, tannic acid and daptomycin, may be effective anti-RSV therapy agents. This study may in future broaden the arsenal of therapeutics for use against RSV infection and lead to more effective care against the virus.Communicated by Ramaswamy H. Sarma.