High-throughput Docking Research Articles

High Throughput Screening and Molecular Docking Analysis on DNA Polymerase (Pol) Β Cancer Variant K289M

DNA polymerase (pol) β plays a critical role in DNA repair mechanisms, and mutations in this enzyme have been implicated in various cancers. Because it may play a role in oncogenesis by altering the fidelity of DNA synthesis, the K289M variation of pol β is particularly interesting. This study uses Python molecular graphic, Cavity based docking, SwissDock, Structure validation server among other computational tools to undertake high-throughput screening and molecular docking investigations of DNA pol β cancer variant K289M (PDB ID: 6NKR). Ligands such as Boceprevir, Nirmatrelvir, Ritonavir, Carmofur, and Lopinavir were docked to identify potential inhibitors targeting this variant. Structural validation using the SAVES server and topology analysis using PDBSum helped assess the quality of the protein structure. Our results highlight several binding pockets with strong ligand affinities, suggesting potential therapeutic interventions for targeting pol β K289M in cancer.

Proteomic Profiling of Serum Extracellular Vesicles Identifies Diagnostic Signatures and Therapeutic Targets in Breast Cancer.

Analysis of extracellular vesicles (EV) is a promising noninvasive liquid biopsy approach for breast cancer detection, prognosis, and therapeutic monitoring. A comprehensive understanding of the characteristics and proteomic composition of breast cancer-specific EVs from human samples is required to realize the potential of this strategy. In this study, we applied a mass spectrometry-based, data-independent acquisition proteomic approach to characterize human serum EVs derived from patients with breast cancer (n = 126) and healthy donors (n = 70) in a discovery cohort and validated the findings in five independent cohorts. Examination of the EV proteomes enabled the construction of specific EV protein classifiers for diagnosing breast cancer and distinguishing patients with metastatic disease. Of note, TALDO1 was found to be an EV biomarker of distant metastasis of breast cancer. In vitro and in vivo analysis confirmed the role of TALDO1 in stimulating breast cancer invasion and metastasis. Finally, high-throughput molecular docking and virtual screening of a library consisting of 271,380 small molecules identified a potent TALDO1 allosteric inhibitor, AO-022, which could inhibit breast cancer migration in vitro and tumor progression in vivo. Together, this work elucidates the proteomic alterations in the serum EVs of breast cancer patients to guide the development of improved diagnosis, monitoring, and treatment strategies. Significance: Characterization of the proteomic composition of circulating extracellar vesicles in breast cancer patients identifies signatures for diagnosing primary and metastatic tumors and reveals tumor-promoting cargo that can be targeted to improve outcomes.

Chicoric acid acts as an ALOX15 inhibitor to prevent ferroptosis in asthma

BackgroundChicoric acid (CA) is a crucial immunologically active compound found in chicory and echinacea, possessing a range of biological activities. Ferroptosis, a type of iron-dependent cell death induced by lipid peroxidation, plays a key role in the development and advancement of asthma. Targeting ferroptosis could be a potential therapeutic strategy for treating asthma. PurposeThe purpose of this study was to explore the screening of ALOX15, a pivotal target of ferroptosis in asthma, and potential therapeutic agents, as well as to investigate the promising potential of CA as an ALOX15 inhibitor for modulating ferroptosis in asthma. MethodsThrough high-throughput data processing of bronchial epithelial RNA from asthma patients using bioinformatics and machine learning, the key target of ferroptosis in asthma, ALOX15, was identified. An inhibitor of ALOX15 was then obtained through high-throughput molecular docking and molecular dynamics simulation tests. In vitro experiments were conducted using a 16HBE cell model induced by house dust mite (HDM) and lipopolysaccharide (LPS), which were treated with the ALOX15 inhibitor (PD146176), CA treatment, or ALOX15 knockdown. In vivo experiments were also carried out using a mouse model induced by HDM and LPS. ResultsThe composite model of ALOX15 and CA in molecular dynamics simulations shows good stability and flexibility. Network pharmacological analysis reveals that CA regulates ferroptosis through ALOX15 in treating asthma. In vitro studies show that ALOX15 is highly expressed in HDM and LPS treatments, while CA inhibits HDM and LPS-induced ferroptosis in 16HBE cells by reducing ALOX15 expression. Knockdown of ALOX15 has the opposite effect. Metabolomics analysis identifies key compounds associated with ferroptosis, including L-Targinine, eicosapentaenoic acid, 16-hydroxy hexadecanoic acid, and succinic acid. In vivo experiments demonstrate that CA suppresses ALOX15 expression, inhibits ferroptosis, and improves asthma symptoms in mice. ConclusionOur research initially identified CA as a promising asthma treatment that effectively blocks ferroptosis by specifically targeting ALOX15. This study not only highlights CA as a potential therapeutic agent for asthma but also introduces novel targets and treatment options for this condition, along with innovative approaches for utilizing natural compounds to target diseases associated with ferroptosis.

Unveiling key genes in esophageal and lung adenocarcinoma progression: A combined high-throughput analysis and molecular docking approach for targeted therapies

BackgroundEsophageal carcinoma (ESCA) and Lung adenocarcinoma (LUAD) are the prominent causes of death worldwide. There is an urgent need to identify and characterize potential biomarkers for these malignancies to enhance early cancer prognosis. Integrating computational-based early cancer detection with wet lab-based research offers a promising approach toward early-stage cancer prognosis. MethodologyTwo ESCA datasets (GSE17351, GSE23400) with 58 cancerous and 58 normal samples, along with two LUAD datasets (GSE18842, GSE74706) totaling 64 cancer samples and 63 controls, were used to identify DEGs. Visualization of DEGs was achieved using heat-maps, volcano plots, and Venn diagrams. Hub genes were predicted via PPI analysis and the cytoHubba plugin in Cytoscape. Potential hub gene expressions were evaluated with box plots, stage plots, and survival plots for prognostic assessment via GEPIA2. AutoDockVina wizard of PyRx was utilized for molecular docking to determine optimal binding interactions between proteins and hit compounds. FindingsSixty common DEGs were identified, focusing on significant pathways in ESCA and LUAD. The top ten hub genes (KIF4A, HMMR, CENPF, CDK1, ASPM, CDKN3, KIF2C, TTK, UBE2C, and MELK) were found to be linked to both cancers via PPI analysis. Notably, high expression of CDK1 was significantly associated with ESCA and LUAD progression, as evidenced by box plots, stage plots, and survival analysis. Upregulated expression of the targeted genes (CDK1) promotes multi-variant cancer progression that is observed by analyzing and comparing of all findings. Molecular docking with CDK1 highlighted four top compounds: Tanshinone I, Withanolide, Artemether, and Epigallocatechin, suggesting their potential as drug candidates for ESCA and LUAD treatment. ConclusionsIn conclusion, our discoveries unveil potential biomarker candidates, offer insights into ESCA and LUAD treatment strategies, and outline directions for further investigation, enriching our understanding of the pathogenesis of ESCA and LUAD.

Structure-Based Design of CBP/EP300 Degraders: When Cooperativity Overcomes Affinity.

We present the development of dCE-2, a structurally novel PROTAC targeting the CREB-binding protein (CBP) and E1A-associated protein (EP300)-two homologous multidomain enzymes crucial for enhancer-mediated transcription. The design of dCE-2 was based on the crystal structure of an in-house bromodomain (BRD) inhibitor featuring a 3-methyl-cinnoline acetyl-lysine mimic acetyl-lysine mimic discovered by high-throughput fragment docking. Our study shows that, despite its modest binding affinity to CBP/EP300-BRD, dCE-2's remarkable protein degradation activity stems from its good cooperativity, which we demonstrate by the characterization of its ternary complex formation both in vitro and in cellulo. Molecular dynamics simulations indicate that in aqueous solvents, this active degrader populates both folded and extended conformations, which are likely to promote cell permeability and ternary complex formation, respectively.

Construction of IRAK4 inhibitor activity prediction model based on machine learning.

Interleukin-1 receptor-associated kinase 4 (IRAK4) is a crucial serine/threonine protein kinase that belongs to the IRAK family and plays a pivotal role in Toll-like receptor (TLR) and Interleukin-1 receptor (IL-1R) signaling pathways. Due to IRAK4's significant role in immunity, inflammation, and malignancies, it has become an intriguing target for discovering and developing potent small-molecule inhibitors. Consequently, there is a pressing need for rapid and accurate prediction of IRAK4 inhibitor activity. Leveraging a comprehensive dataset encompassing activity data for 1628 IRAK4 inhibitors, we constructed a prediction model using the LightGBM algorithm and molecular fingerprints. This model achieved an R2 of 0.829, an MAE of 0.317, and an RMSE of 0.460 in independent testing. To further validate the model's generalization ability, we tested it on 90 IRAK4 inhibitors collected in 2023. Subsequently, we applied the model to predict the activity of 13,268 compounds with docking scores less than -9.503kcal/mol. These compounds were initially screened from a pool of 1.6 million molecules in the chemdiv database through high-throughput molecular docking. Among these, 259 compounds with predicted pIC50 values greater than or equal to 8.00 were identified. We then performed ADMET predictions on these selected compounds. Finally, through a rigorous screening process, we identified 34 compounds that adhere to the four complementary drug-likeness rules, making them promising candidates for further investigation. Additionally, molecular dynamics simulations confirmed the stable binding of the screened compounds to the IRAK4 protein. Overall, this work presents a machine learning model for accurate prediction of IRAK4 inhibitor activity and offers new insights for subsequent structure-guided design of novel IRAK4 inhibitors.

Identification of Novel GANT61 Analogs with Activity in Hedgehog Functional Assays and GLI1-Dependent Cancer Cells.

Aberrant activation of hedgehog (Hh) signaling has been implicated in various cancers. Current FDA-approved inhibitors target the seven-transmembrane receptor Smoothened, but resistance to these drugs has been observed. It has been proposed that a more promising strategy to target this pathway is at the GLI1 transcription factor level. GANT61 was the first small molecule identified to directly suppress GLI-mediated activity; however, its development as a potential anti-cancer agent has been hindered by its modest activity and aqueous chemical instability. Our study aimed to identify novel GLI1 inhibitors. JChem searches identified fifty-two compounds similar to GANT61 and its active metabolite, GANT61-D. We combined high-throughput cell-based assays and molecular docking to evaluate these analogs. Five of the fifty-two GANT61 analogs inhibited activity in Hh-responsive C3H10T1/2 and Gli-reporter NIH3T3 cellular assays without cytotoxicity. Two of the GANT61 analogs, BAS 07019774 and Z27610715, reduced Gli1 mRNA expression in C3H10T1/2 cells. Treatment with BAS 07019774 significantly reduced cell viability in Hh-dependent glioblastoma and lung cancer cell lines. Molecular docking indicated that BAS 07019774 is predicted to bind to the ZF4 region of GLI1, potentially interfering with its ability to bind DNA. Our findings show promise in developing more effective and potent GLI inhibitors.

Preparation, Characterization, and Oral Bioavailability of Solid Dispersions of Cryptosporidium parvum Alternative Oxidase Inhibitors.

The phenylpyrazole derivative 5-amino-3-[1-cyano-2-(3-phenyl-1H-pyrazol-4-yl) vinyl]-1-phenyl-1H-pyrazole-4-carbonitrile (LN002), which was screened out through high-throughput molecular docking for the AOX target, exhibits promising efficacy against Cryptosporidium. However, its poor water solubility limits its oral bioavailability and therapeutic utility. In this study, solid dispersion agents were prepared by using HP-β-CD and Soluplus® and characterized through differential scanning calorimetry, Fourier transform infrared, powder X-ray diffraction, and scanning electron microscopy. Physical and chemical characterization showed that the crystal morphology of LN002 transformed into an amorphous state, thus forming a solid dispersion of LN002. The solid dispersion prepared with an LN002/HP-β-CD/Soluplus® mass ratio of 1:3:9 (w/w/w) exhibited significantly increased solubility and cumulative dissolution. Meanwhile, LN002 SDs showed good preservation stability under accelerated conditions of 25 °C and 75% relative humidity. The complexation of LN002 with HP-β-CD and Soluplus® significantly improved water solubility, pharmacological properties, absorption, and bioavailability.

ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries.

The emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compounds for experimental validation requires filtering these molecules based on favourable druglike properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET). We developed ADMET-AI, a machine learning platform that provides fast and accurate ADMET predictions both as a website and as a Python package. ADMET-AI has the highest average rank on the TDC ADMET Leaderboard, and it is currently the fastest web-based ADMET predictor, with a 45% reduction in time compared to the next fastest public ADMET web server. ADMET-AI can also be run locally with predictions for one million molecules taking just 3.1 h. The ADMET-AI platform is freely available both as a web server at admet.ai.greenstonebio.com and as an open-source Python package for local batch prediction at github.com/swansonk14/admet_ai (also archived on Zenodo at doi.org/10.5281/zenodo.10372930). All data and models are archived on Zenodo at doi.org/10.5281/zenodo.10372418.

Pharmaceutical Potential of Remedial Plants and Helminths for Treating Inflammatory Bowel Disease.

Research is increasingly revealing that inflammation significantly contributes to various diseases, particularly inflammatory bowel disease (IBD). IBD is a major medical challenge due to its chronic nature, affecting at least one in a thousand individuals in many Western countries, with rising incidence in developing nations. Historically, indigenous people have used natural products to treat ailments, including IBD. Ethnobotanically guided studies have shown that plant-derived extracts and compounds effectively modulate immune responses and reduce inflammation. Similarly, helminths and their products offer unique mechanisms to modulate host immunity and alleviate inflammatory responses. This review explored the pharmaceutical potential of Aboriginal remedial plants and helminths for treating IBD, emphasizing recent advances in discovering anti-inflammatory small-molecule drug leads. The literature from Scopus, MEDLINE Ovid, PubMed, Google Scholar, and Web of Science was retrieved using keywords such as natural product, small molecule, cytokines, remedial plants, and helminths. This review identified 55 important Aboriginal medicinal plants and 9 helminth species that have been studied for their anti-inflammatory properties using animal models and in vitro cell assays. For example, curcumin, berberine, and triptolide, which have been isolated from plants; and the excretory-secretory products and their protein, which have been collected from helminths, have demonstrated anti-inflammatory activity with lower toxicity and fewer side effects. High-throughput screening, molecular docking, artificial intelligence, and machine learning have been engaged in compound identification, while clustered regularly interspaced short palindromic repeats (CRISPR) gene editing and RNA sequencing have been employed to understand molecular interactions and regulations. While there is potential for pharmaceutical application of Aboriginal medicinal plants and gastrointestinal parasites in treating IBD, there is an urgent need to qualify these plant and helminth therapies through reproducible clinical and mechanistic studies.

Discovery of potent antimycobacterial agents targeting lumazine synthase (RibH) of Mycobacterium tuberculosis

Tuberculosis (TB) continues to be a global health crisis, necessitating urgent interventions to address drug resistance and improve treatment efficacy. In this study, we validate lumazine synthase (RibH), a vital enzyme in the riboflavin biosynthetic pathway, as a potential drug target against Mycobacterium tuberculosis (M. tb) using a CRISPRi-based conditional gene knockdown strategy. We employ a high-throughput molecular docking approach to screen ~ 600,000 compounds targeting RibH. Through in vitro screening of 55 shortlisted compounds, we discover 3 compounds that exhibit potent antimycobacterial activity. These compounds also reduce intracellular burden of M. tb during macrophage infection and prevent the resuscitation of the nutrient-starved persister bacteria. Moreover, these three compounds enhance the bactericidal effect of first-line anti-TB drugs, isoniazid and rifampicin. Corroborating with the in silico predicted high docking scores along with favourable ADME and toxicity profiles, all three compounds demonstrate binding affinity towards purified lumazine synthase enzyme in vitro, in addition these compounds exhibit riboflavin displacement in an in vitro assay with purified lumazine synthase indicative of specificity of these compounds to the active site. Further, treatment of M. tb with these compounds indicate reduced production of flavin adenine dinucleotide (FAD), the ultimate end product of the riboflavin biosynthetic pathway suggesting the action of these drugs on riboflavin biosynthesis. These compounds also show acceptable safety profile in mammalian cells, with a high selective index. Hence, our study validates RibH as an important drug target against M. tb and identifies potent antimycobacterial agents.

Analysis of the anti-PCV2 mechanism of Lactobacillus acidophilus based on non-target metabolomics and high-throughput molecular docking.

Our previous studies have revealed that L. acidophilus possesses inhibitory effects on PCV2 proliferation in vivo, although the underlying mechanisms remain elusive. Probiotics like L. acidophilus are known to exert antiviral through their metabolites. Therefore, in this study, non-targeted metabolomics was used to detect the changes in metabolites of L. acidophilus after 24 h of proliferation. Subsequently, high-throughput molecular docking was utilized to analyze the docking scores of these metabolites with PCV2 Cap and Rep, aiming to identify compounds with potential anti-PCV2 effects. The results demonstrated that 128 compounds such as Dl-lactate were significantly increased. The results of high-throughput molecular docking indicated that compounds such as ergocristine, and telmisartan formed complexes with Cap and Rep, suggesting their potential anti-PCV2 properties. Furthermore, compounds like vitamin C, exhibit pharmacological effects consistent with L. acidophilus adding credence to the idea that L. acidophilus may exert pharmacological effects through its metabolites. These results will provide a foundation for the study of L. acidophilus.

Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach

Target-driven cancer therapy is a notable advancement in precision oncology that has been accompanied by substantial medical accomplishments. Ovarian cancer is a highly frequent neoplasm in women and exhibits significant genomic and clinical heterogeneity. In a previous publication, we presented an extensive bioinformatics study aimed at identifying specific biomarkers associated with ovarian cancer. The findings of the network analysis indicate the presence of a cluster of nine dysregulated hub genes that exhibited significance in the underlying biological processes and contributed to the initiation of ovarian cancer. Here in this research article, we are proceeding our previous research by taking all hub genes into consideration for further analysis. GEPIA2 was used to identify patterns in the expression of critical genes. The KM plotter analysis indicated that the out of all genes 5 genes are statistically significant. The cBioPortal platform was further used to investigate the frequency of genetic mutations across the board and how they affected the survival of the patients. Maximum mutation was reported by ELAVL2. In order to discover viable therapeutic candidates after competitive inhibition of ELAVL2 with small molecular drug complex, high throughput screening and docking studies were used. Five compounds were identified. Overall, our results suggest that the ELAV-like protein 2-ZINC03830554 complex was relatively stable during the molecular dynamic simulation. The five compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that ELAVL2, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy.

Repurposing diacerein to suppress colorectal cancer growth by inhibiting the DCLK1/STAT3 signaling pathway

Double cortin-like kinase 1 (DCLK1) exhibits high expression levels across various cancers, notably in human colorectal cancer (CRC). Diacerein, a clinically approved interleukin (IL)-1β inhibitor for osteoarthritis treatment, was evaluated for its impact on CRC proliferation and migration, alongside its underlying mechanisms, through both in vitro and in vivo analyses. The study employed MTT assay, colony formation, wound healing, transwell assays, flow cytometry, and Hoechst 33342 staining to assess cell proliferation, migration, and apoptosis. Additionally, proteome microarray assay and western blotting analyses were conducted to elucidate diacerein's specific mechanism of action. Our findings indicate that diacerein significantly inhibits DCLK1-dependent CRC growth in vitro and in vivo. Through high-throughput proteomics microarray and molecular docking studies, we identified that diacerein directly interacts with DCLK1. Mechanistically, the suppression of p-STAT3 expression following DCLK1 inhibition by diacerein or specific DCLK1 siRNA was observed. Furthermore, diacerein effectively disrupted the DCLK1/STAT3 signaling pathway and its downstream targets, including MCL-1, VEGF, and survivin, thereby inhibiting CRC progression in a mouse model, thereby inhibiting CRC progression in a mouse model.

High-throughput virtual search of small molecules for controlling the mechanical stability of human CD4

Protein mechanical stability determines the function of a myriad of proteins, especially proteins from the extracellular matrix. Failure to maintain protein mechanical stability may result in diseases and disorders such as cancer, cardiomyopathies, or muscular dystrophy. Thus, developing mutation-free approaches to enhance and control the mechanical stability of proteins using pharmacology-based methods, may have important implications in drug development and discovery. Here, we present the first approach that employs computational High-Throughput Virtual Screening (HTVS) and Molecular Docking to search for small-molecules in chemical libraries that function as mechano-regulators of the stability of human CD4, receptor of HIV-1. Using single-molecule force spectroscopy we prove that these small-molecules can increase the mechanical stability of CD4D1D2 domains over 4-fold in addition to modifying the mechanical unfolding pathways. Our experiments demonstrate that chemical libraries are a source of mechanoactive molecules and that drug discovery approaches provide the foundation of a new type of molecular function, i.e., mechano-regulation, paving the way towards mechanopharmacology.

Targeting of essential mycobacterial replication enzyme DnaG primase revealed Mitoxantrone and Vapreotide as novel mycobacterial growth inhibitors.

Tuberculosis (TB) is the second leading cause of mortality after COVID-19, with a global death toll of 1.6 million in 2021. The escalating situation of drug-resistant forms of TB has threatened the current TB management strategies. New therapeutics with novel mechanisms of action are urgently required to address the current global TB crisis. The essential mycobacterial primase DnaG with no structural homology to homo sapiens presents itself as a good candidate for drug targeting. In the present study, Mitoxantrone and Vapreotide, two FDA-approved drugs, were identified as potential anti-mycobacterial agents. Both Mitoxantrone and Vapreotide exhibit a strong Minimum Inhibitory Concentration (MIC) of ≤25μg/ml against both the virulent (M.tb-H37Rv) and avirulent (M.tb-H37Ra) strains of M.tb. Extending the validations further revealed the inhibitory potential drugs in ex vivo conditions. Leveraging the computational high-throughput multi-level docking procedures from the pool of ~2700 FDA-approved compounds, Mitoxantrone and Vapreotide were screened out as potential inhibitors of DnaG. Extensive 200 ns long all-atoms molecular dynamic simulation of DnaGDrugs complexes revealed that both drugs bind strongly and stabilize the DnaG during simulations. Reduced solvent exposure and confined motions of the active centre of DnaG upon complexation with drugs indicated that both drugs led to the closure of the active site of DnaG. From this study's findings, we propose Mitoxantrone and Vapreotide as potential anti-mycobacterial agents, with their novel mechanism of action against mycobacterial DnaG.

In Silico Identification of Natural Products and World-Approved Drugs Targeting the KEAP1/NRF2 Pathway Endowed with Potential Antioxidant Profile

In this study, we applied a computer-based protocol to identify novel antioxidant agents that can reduce oxidative stress (OxS), which is one of the main hallmarks of several disorders, including cancer, cardiovascular disease, and neurodegenerative disorders. Accordingly, the identification of novel and safe agents, particularly natural products, could represent a valuable strategy to prevent and slow down the cellular damage caused by OxS. Employing two chemical libraries that were properly prepared and enclosing both natural products and world-approved and investigational drugs, we performed a high-throughput docking campaign to identify potential compounds that were able to target the KEAP1 protein. This protein is the main cellular component, along with NRF2, that is involved in the activation of the antioxidant cellular pathway. Furthermore, several post-search filtering approaches were applied to improve the reliability of the computational protocol, such as the evaluation of ligand binding energies and the assessment of the ADMET profile, to provide a final set of compounds that were evaluated by molecular dynamics studies for their binding stability. By following the screening protocol mentioned above, we identified a few undisclosed natural products and drugs that showed great promise as antioxidant agents. Considering the natural products, isoxanthochymol, gingerenone A, and meranzin hydrate showed the best predicted profile for behaving as antioxidant agents, whereas, among the drugs, nedocromil, zopolrestat, and bempedoic acid could be considered for a repurposing approach to identify possible antioxidant agents. In addition, they showed satisfactory ADMET properties with a safe profile, suggesting possible long-term administration. In conclusion, the identified compounds represent a valuable starting point for the identification of novel, safe, and effective antioxidant agents to be employed in cell-based tests and in vivo studies to properly evaluate their action against OxS and the optimal dosage for exerting antioxidant effects.

Targeting CDK6 in hormone receptor-positive breast cancer: inhibitor discovery for precision oncology through dynamics study

CDK6 is a critical protein involved in the regulation of the cell cycle, playing an important role in the progression from the G1 to S phase. In breast cancer, dysregulation of this protein is involved in tumour development and progression, particularly in hormone receptor-positive (HR+) breast cancers. The upregulation of CDK6 have been observed in a subset of breast cancers, leading to uncontrolled progression of the cell cycle and increased proliferation of cells. The purpose of this abstract is to provide an outline of CDK6's role. In breast cancer and the therapeutic strategies targeting CDK6 using specific selected inhibitors. To discover viable therapeutic candidates after competitive inhibition of CDK6 with a small molecular drug complex, high throughput screening and docking studies were used. Further, we carried the compounds based on ADMET properties and prediction of activity spectra for substances analysis. Finally, two different compounds were selected to carry out MD simulations. CDK6-IMPHY002642 and CDK6-IMPHY005260 are the two compounds that were identified. Overall, our results suggest that the CDK6-IMPHY002642 and CDK6-IMPHY005260 complex was relatively stable during the simulation. The compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that CDK6, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy. Communicated by Ramaswamy H. Sarma

Virtual screening and identification of promising therapeutic compounds against drug-resistant Mycobacterium tuberculosis β-ketoacyl-acyl carrier protein synthase I (KasA)

The emergence of new Mycobacterium tuberculosis (Mtb) strains resistant to the key drugs currently used in the clinic for tuberculosis treatment can substantially reduce the probability of therapy success, causing the relevance and importance of studies on the development of novel potent antibacterial agents targeting different vulnerable spots of Mtb. In this study, 28,860 compounds from the library of bioactive molecules were screened to identify novel potential inhibitors of β-ketoacyl-acyl carrier protein synthase I (KasA), one of the key enzymes involved in the biosynthesis of mycolic acids of the Mtb cell wall. In doing so, we used a structure-based virtual screening approach to drug repurposing that included high-throughput docking of the C171Q KasA enzyme with compounds from the library of bioactive molecules including the FDA-approved drugs and investigational drug candidates, assessment of the binding affinity for the docked ligand/C171Q KasA complexes, and molecular dynamics simulations followed by binding free energy calculations. As a result, post-modeling analysis revealed 6 top-ranking compounds exhibiting a strong attachment to the malonyl binding site of the enzyme, as evidenced by the values of binding free energy which are significantly lower than those predicted for the KasA inhibitor TLM5 used in the calculations as a positive control. In light of the data obtained, the identified compounds are suggested to form a good basis for the development of new antitubercular molecules of clinical significance with activity against the KasA enzyme of Mtb. Communicated by Ramaswamy H. Sarma

Small-Molecule Inhibitors of the m7G-RNA Writer METTL1.

We discovered the first inhibitors of the m7G-RNA writer METTL1 by high-throughput docking and an enzymatic assay based on luminescence. Eleven compounds, which belong to three different chemotypes, show inhibitory activity in the range 40-300 μM. Two adenine derivatives identified by docking have very favorable ligand efficiency of 0.34 and 0.31 kcal/mol per non-hydrogen atom, respectively. Molecular dynamics simulations provide evidence that the inhibitors compete with the binding of the cosubstrate S-adenosyl methionine to METTL1. We also present a soakable crystal form that was used to determine the structure of the complex of METTL1 with sinefungin at a resolution of 1.85 Å.