Protein-ligand Interactions Research Articles

Probing Protein-Ligand Methyl-π Interaction Geometries through Chemical Shift Measurements of Selectively Labeled Methyl Groups.

Fragment-based drug design is heavily dependent on the optimization of initial low-affinity binders. Herein we introduce an approach that uses selective labeling of methyl groups in leucine and isoleucine side chains to directly probe methyl-π contacts, one of the most prominent forms of interaction between proteins and small molecules. Using simple NMR chemical shift perturbation experiments with selected BRD4-BD1 binders, we find good agreement with a commonly used model of the ring-current effect as well as the overall interaction geometries extracted from the Protein Data Bank. By combining both interaction geometries and chemical shift calculations as fit quality criteria, we can position dummy aromatic rings into an AlphaFold model of the protein of interest. The proposed method can therefore provide medicinal chemists with important information about binding geometries of small molecules in fast and iterative matter, even in the absence of high-resolution experimental structures.

From Proteome to Potential Drugs: Integration of Subtractive Proteomics and Ensemble Docking for Drug Repurposing against Pseudomonas aeruginosa RND Superfamily Proteins.

Pseudomonas aeruginosa (P. aeruginosa) poses a significant threat as a nosocomial pathogen due to its robust resistance mechanisms and virulence factors. This study integrates subtractive proteomics and ensemble docking to identify and characterize essential proteins in P. aeruginosa, aiming to discover therapeutic targets and repurpose commercial existing drugs. Using subtractive proteomics, we refined the dataset to discard redundant proteins and minimize potential cross-interactions with human proteins and the microbiome proteins. We identified 12 key proteins, including a histidine kinase and members of the RND efflux pump family, known for their roles in antibiotic resistance, virulence, and antigenicity. Predictive modeling of the three-dimensional structures of these RND proteins and subsequent molecular ensemble-docking simulations led to the identification of MK-3207, R-428, and Suramin as promising inhibitor candidates. These compounds demonstrated high binding affinities and effective inhibition across multiple metrics. Further refinement using non-covalent interaction index methods provided deeper insights into the electronic effects in protein-ligand interactions, with Suramin exhibiting superior binding energies, suggesting its broad-spectrum inhibitory potential. Our findings confirm the critical role of RND efflux pumps in antibiotic resistance and suggest that MK-3207, R-428, and Suramin could be effectively repurposed to target these proteins. This approach highlights the potential of drug repurposing as a viable strategy to combat P. aeruginosa infections.

Novel hybrid motifs of 2-(2-pyridinyl)-1H-benzo[d]imidazole-1,2,3-triazole: Synthesis, anticancer assessment, and in silico study

A new category of 1,2,3-triazole-pyridine-benzo[d]imidazole hybrids (12–21) were prepared by a Cu(I)-catalyzed Alkyne-Azide Cyclic-addition (CuAAC) procedure known as Click reaction. The terminal alkyne reaction of 1-(prop‑2-yn-1-yl)-2-(2-pyridinyl)-1H-benzo[d]imidazole 1 with aryl-bearing azide 2–12 afforded 1,2,3-triazole-pyridine-benzo[d]imidazole hybrids. The synthesized adducts were analyzed by NMR (1H and 13C), mass spectroscopy, and elemental analyses. As a result of the LDH assay, the desired adducts were examined for several cancer cells, such as breast (MCF7), human cell line (BJ1), hepatocellular carcinoma (HepG2), and human colon (HCT116). Since the cytotoxicity of the desired compounds is mostly stronger than that of their cytotoxicity on normal cells, most of them are good anticancer candidate medications for the liver, colon, and breast compared to doxorubicin. Studies of molecular docking correlate hybrid activity with constant protein-ligand interactions with biological targets, it revealed that derivatives 18 & 19 could stand out with 3 new H-bonds, including Asp363, asn368 and Asp381, and low bind energy, including -9.4 and -9.0 kcal/mol, respectively.

Chemical reactivity, molecular electrostatic potential, FTIR, NMR, in vitro, and in silico studies of mannopyranoside derivatives: 3-Nitrobenzoylation leads to improve antimicrobial activity

This investigation Extensively explored the synthesis and potential therapeutic applications of derivatives derived from methyl α-d-mannopyranoside. It encompasses a wide range of evaluations, including antimicrobial assessments, molecular docking, dynamic simulations, and ADMET analysis. Spectroscopic methods (FTIR, 1H NMR, 13C NMR, mass) were used to confirm the structures of the synthesized mannopyranoside derivatives. This study assessed the antibacterial activity of the strains against a range of both gram-positive and gram-negative bacteria and revealed significant inhibitory effects. In addition, the mannopyranoside derivatives exhibited significant antifungal activity. Additionally, we studied how to improve the thermal, frontier molecular orbital (FMO), and molecular electrostatic potential (MEP) properties of mannopyranoside and its acylated analogs by using density functional theory (DFT). Molecular docking studies provided further evidence of the advantageous antibacterial effects of the mannopyranoside derivatives 3, 6, and 7 against the penicillin-binding protein PBP2a (1VQQ) from the methicillin-resistant binding protein Staphylococcus aureus. The docking results were confirmed via molecular dynamics (MD) simulations, which provided a dynamic view of protein stability. The conformational stability increased in the simulations, and the values of the solvent-accessible surface area (SASA) and radius of gyration (Rg) were found to be consistent and stable. Reduced root mean square fluctuations (RMSFs), facilitated by strong hydrogen bonding interactions, characterize ligand–protein interactions. The toxicological and pharmacokinetic profiles of the molecule were assessed using ADMET analysis. The results showed that the molecule had favorable drug-like features, making it a strong candidate for further development. Overall, these findings indicate that 3-nitobenzoylated mannopyranoside derivatives exhibit considerable potential as therapeutic agents for treating microbial infections.

Exploring novel ligands for mGluR5: Design, computational analysis, and protein-ligand interaction studies

Exploring novel ligands for mGluR5: Design, computational analysis, and protein-ligand interaction studies

Modeling Ligands into Maps Derived from Electron Cryomicroscopy.

Deciphering the protein-ligand interactions in a macromolecular complex is crucial for understanding the molecular mechanism, underlying biological processes, and drug development. In recent years, cryogenic sample electron microscopy (cryoEM) has emerged as a powerful technique to determine the structures of macromolecules and to investigate the mode of ligand binding at near-atomic resolution. Identifying and modeling non-protein molecules in cryoEM maps is often challenging due to anisotropic resolution across the molecule of interest and inherent noise in the data. In this article, the readers are introduced to various software and methods currently used for ligand identification, model building, and refinement of atomic coordinates using selected macromolecules. One of the simplest ways to identify the presence of a ligand, as illustrated with the enolase enzyme, is to subtract the two maps obtained with and without the ligand. The extra density of the ligand is likely to stand out in the difference map even at a higher threshold. There are instances, as shown in the case of metabotropic Glutamate receptor mGlu5, when such simple difference maps cannot be generated. The recently introduced method of deriving the Fo-Fc omit map can serve as a tool for validating and demonstrating the presence of the ligand. Finally, using the well-studied β-galactosidase as an example, the effect of resolution on modeling the ligands and solvent molecules in cryoEM maps is analyzed, and an outlook on how cryoEM can be used in drug discovery is presented.

Ultrasound-assisted synthesis of 4-thiazolidinone Schiff bases and their antioxidant, α-glucosidase, α-amylase inhibition, mode of inhibition and computational studies

AbstractDiabetes mellitus (DM) has become a growing concern to global public health, being at the forefront of acute disorders and causes of mortality across the globe. Clinically approved drugs that are currently being used are faced with severe side effects, consequently necessitating the development of new drugs with no/fewer side effects and improved pharmacological potency. Herein, we report a rapid and efficient synthesis of thiazolidinone Schiff bases (2a-2t) from benzylidenehydrazines and thioglycolic acid under neat conditions through ultra-sonication. All the synthesized compounds were obtained in exceptional yields (89–95%) and confirmed by 1D and 2D nuclear magnetic resonance (NMR) spectroscopy, as well as High-resolution mass spectrometry (HRMS). The synthesized compounds were then evaluated for their antidiabetic activity through α-glucosidase and α-amylase inhibitory potentials and their antioxidant activity through Nitric Oxide (NO), 2,2′-diphenyl-1-picrylhydrazyl (DPPH), and Ferric reducing antioxidant power (FRAP) assays. Among them, 2q (IC50 = 96.63 μM) and 2h (IC50 = 125.27 μM) emerged as the most potent derivatives against α-amylase relative to reference drug acarbose (IC50 = 131.63 µM), respectively. Antioxidant evaluation further revealed that the synthesized derivatives were excellent NO scavengers disclosing 2n (IC50 = 44.95 µM) as the most potent derivative. Moreover, in silico ADME calculations predicted these compounds to have excellent drug-like properties. Kinetic studies disclosed the mode of α-amylase inhibition as competitive while molecular docking studies of the most active derivatives performed into the binding active site of human pancreatic α-amylase enzyme deciphered their ligand-protein interactions that explicated their observed experimental potencies.

Rationalizing Diverse Binding Mechanisms to the Same Protein Fold: Insights for Ligand Recognition and Biosensor Design.

The engineering of novel protein-ligand binding interactions, particularly for complex drug-like molecules, is an unsolved problem, which could enable many practical applications of protein biosensors. In this work, we analyzed two engineered biosensors, derived from the plant hormone sensor PYR1, to recognize either the agrochemical mandipropamid or the synthetic cannabinoid WIN55,212-2. Using a combination of quantitative deep mutational scanning experiments and molecular dynamics simulations, we demonstrated that mutations at common positions can promote protein-ligand shape complementarity and revealed prominent differences in the electrostatic networks needed to complement diverse ligands. MD simulations indicate that both PYR1 protein-ligand complexes bind a single conformer of their target ligand that is close to the lowest free-energy conformer. Computational design using a fixed conformer and rigid body orientation led to new WIN55,212-2 sensors with nanomolar limits of detection. This work reveals mechanisms by which the versatile PYR1 biosensor scaffold can bind diverse ligands. This work also provides computational methods to sample realistic ligand conformers and rigid body alignments that simplify the computational design of biosensors for novel ligands of interest.

Naturally Derived Terpenoids Targeting the 3Dpol of Foot-and-Mouth Disease Virus: An Integrated In Silico and In Vitro Investigation.

Foot-and-mouth disease virus (FMDV) belongs to the Picornaviridae family and is an important pathogen affecting cloven-hoof livestock. However, neither effective vaccines covering all serotypes nor specific antivirals against FMDV infections are currently available. In this study, we employed virtual screening to screen for secondary metabolite terpenoids targeting the RNA-dependent RNA polymerase (RdRp), or 3Dpol, of FMDV. Subsequently, we identified the potential antiviral activity of the 32 top-ranked terpenoids, revealing that continentalic acid, dehydroabietic acid (abietic diterpenoids), brusatol, bruceine D, and bruceine E (tetracyclic triterpenoids) significantly reduced cytopathic effects and viral infection in the terpenoid-treated, FMDV-infected BHK-21 cells in a dose-dependent manner, with nanomolar to low micromolar levels. The FMDV minigenome assay demonstrated that brusatol and bruceine D, in particular, effectively blocked FMDV 3Dpol activity, exhibiting IC50 values in the range of 0.37-0.39 µM and surpassing the efficacy of the antiviral drug control, ribavirin. Continentalic acid and bruceine E exhibited moderate inhibition of FMDV 3Dpol. The predicted protein-ligand interaction confirmed that these potential terpenoids interacted with the main catalytic and bystander residues of FMDV 3Dpol. Additionally, brusatol and bruceine D exhibited additive effects when combined with ribavirin. In conclusion, terpenoids from natural resources show promise for the development of anti-FMD agents.

GOLEM: Automated and Robust Cryo-EM-Guided Ligand Docking with Explicit Water Molecules.

A detailed understanding of ligand-protein interaction is essential for developing rational drug-design strategies. In recent years, technological advances in cryo-electron microscopy (cryo-EM) brought a new era to the structural determination of biological macromolecules and assemblies at high resolution, marking cryo-EM as a promising tool for studying ligand-protein interactions. However, even in high-resolution cryo-EM results, the densities for the bound small-molecule ligands are often of lower quality due to their relatively dynamic and flexible nature, frustrating their accurate coordinate assignment. To address the challenge of ligand modeling in cryo-EM maps, here we report the development of GOLEM (Genetic Optimization of Ligands in Experimental Maps), an automated and robust ligand docking method that predicts a ligand's pose and conformation in cryo-EM maps. GOLEM employs a Lamarckian genetic algorithm to perform a hybrid global/local search for exploring the ligand's conformational, orientational, and positional space. As an important feature, GOLEM explicitly considers water molecules and places them at optimal positions and orientations. GOLEM takes into account both molecular energetics and the correlation with the cryo-EM maps in its scoring function to optimally place the ligand. We have validated GOLEM against multiple cryo-EM structures with a wide range of map resolutions and ligand types, returning ligand poses in excellent agreement with the densities. As a VMD plugin, GOLEM is free of charge and accessible to the community. With these features, GOLEM will provide a valuable tool for ligand modeling in cryo-EM efforts toward drug discovery.

Detection of Noncovalent Protein-Ligand Complexes by IR-MALDESI-MS.

Native mass spectrometry (MS) is a powerful analytical technique to directly probe noncovalent protein-protein and protein-ligand interactions. However, not every MS platform can preserve proteins in their native conformation due to high energy deposition from the utilized ionization source. Most small molecules approved as drugs and in development interact with their targets through noncovalent interactions. Therefore, rapid methods to analyze noncovalent protein-ligand interactions are necessary for the early stages of the drug discovery pipeline. Herein, we describe a method for analyzing noncovalent protein-ligand complexes by IR-MALDESI-MS with analysis times of ∼13 s per sample. Carbonic anhydrase and the kinase domain of Bruton's tyrosine kinase are paired with known noncovalent binders to evaluate the effectiveness of native MS by IR-MALDESI.

Computational approaches for lead compound discovery in dipeptidyl peptidase-4 inhibition using machine learning and molecular dynamics techniques

The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Mellitus (T2DM). The inhibition of dipeptidyl peptidase-4 (DPP-4) has been one of the most explored strategies to develop potential drugs against this condition. A diverse dataset of molecules with known experimental inhibitory activity against DPP-4 was constructed and used to develop predictive models using different machine-learning algorithms. Model M36 is the most promising one based on the internal and external performance showing values of Q2CV = 0.813, and Q2EXT = 0.803. The applicability domain evaluation and Tropsha's analysis were conducted to validate M36, indicating its robustness and accuracy in predicting pIC50 values for organic molecules within the established domain. The physicochemical properties of the ligands, including electronegativity, polarizability, and van der Waals volume were relevant to predict the inhibition process. The model was then employed in the virtual screening of potential DPP4 inhibitors, finding 448 compounds from the DrugBank and 9 from DiaNat with potential inhibitory activity. Molecular docking and molecular dynamics simulations were used to get insight into the ligand-protein interaction. From the screening and the favorable molecular dynamic results, several compounds including Skimmin (pIC50 = 3.54, Binding energy = −8.86 kcal/mol), bergenin (pIC50 = 2.69, Binding energy = −13.90 kcal/mol), and DB07272 (pIC50 = 3.97, Binding energy = −25.28 kcal/mol) seem to be promising hits to be tested and optimized in the treatment of T2DM. This results imply a important reduction in cost and time on the application of this drugs because all the information about the its metabolism is already available.

Identification of new benzofuran derivatives as STING agonists with broad-spectrum antiviral activity

The Stimulator of Interferon Genes (STING) is involved in cytosolic DNA sensing and type I Interferons (IFN-I) induction. Aiming to identify new STING agonists with antiviral activity and given the known biological activity of benzothiazole and benzimidazole derivatives, a series of benzofuran derivatives were tested for their ability to act as STING agonists, induce IFN-I and inhibit viral replication. Compounds were firstly evaluated in a gene reporter assay measuring luciferase activity driven by the human IFN-β promoter in cells expressing exogenous STING (HEK293T). Seven of them were able to induce IFN-β transcription while no induction of the IFN promoter was observed in the presence of a mutated and inactive STING, showing specific protein-ligand interaction. Docking studies were performed to predict their putative binding mode. The best hit compounds were then tested on human coronavirus 229E replication in BEAS-2B and MRC-5 cells and three derivatives showed EC50 values in the μM range. Such compounds were also tested on SARS-CoV-2 replication in BEAS-2B cells and in Calu-3 showing they can inhibit SARS-CoV-2 replication at nanomolar concentrations. To further confirm their IFN-dependent antiviral activity, compounds were tested to verify their effect on phospho-IRF3 nuclear localization, that was found to be induced by benzofuran derivatives, and SARS-CoV-2 replication in Vero E6 cells, lacking IFN production, founding them to be inactive. In conclusion, we identified benzofurans as STING-dependent immunostimulatory compounds and host-targeting inhibitors of coronaviruses representing a novel chemical scaffold for the development of broad-spectrum antivirals.

G–PLIP: Knowledge graph neural network for structure-free protein–ligand bioactivity prediction

Protein–ligand interactions (PLIs) determine the efficacy and safety profiles of small molecule drugs. Existing methods rely on either structural information or resource-intensive computations to predict PLI, casting doubt on whether it is possible to perform structure-free PLI predictions at low computational cost. Here we show that a light-weight graph neural network (GNN), trained with quantitative PLIs of a small number of proteins and ligands, is able to predict the strength of unseen PLIs. The model has no direct access to structural information about the protein–ligand complexes. Instead, the predictive power is provided by encoding the entire chemical and proteomic space in a single heterogeneous graph, encapsulating primary protein sequence, gene expression, the protein–protein interaction network, and structural similarities between ligands. This novel approach performs competitively with, or better than, structure-aware models. Our results suggest that existing PLI prediction methods may be improved by incorporating representation learning techniques that embed biological and chemical knowledge.

Uncovering the unique characteristics of different groups of 5-HT5AR ligands with reference to their interaction with the target protein

BackgroundThe serotonin 5-HT5A receptor has attracted much more research attention, due to the therapeutic potential of its ligands being increasingly recognized, and the possibilities that lie ahead of these findings. There is a growing body of evidence indicating that these ligands have procognitive, pro-social, and anti-depressant properties, which offers new avenues for the development of treatments that could address socially important conditions related to the malfunctioning of the central nervous system. The aim of our study was to unravel the molecular determinants for 5-HT5AR ligands that govern their activity towards the receptor.MethodsIn response to the need for identification of molecular determinants for 5-HT5AR activity, we prepared a comprehensive collection of 5-HT5AR ligands, carefully gathering literature and patent data. Leveraging molecular modeling techniques, such as pharmacophore hypothesis development, docking, and molecular dynamics simulations enables to gain valuable insights into the specific interactions of 5-HT5AR ligand groups with the receptor.ResultsThe obtained comprehensive set of 2160 compounds was divided into dozens of subsets, and a pharmacophore model was developed for each group. The results from the docking and molecular dynamics simulations have enabled the identification of crucial ligand–protein interactions that are essential for the compound's activity towards 5-HT5AR.ConclusionsThe findings from the molecular modeling study provide valuable insights that can guide medicinal chemists in the development of new 5-HT5AR ligands. Considering the pharmacological significance of these compounds, they have the potential to become impactful treatments for individuals and communities in the future. Understanding how different crystal/cryo-EM structures of 5-HT5AR affect molecular modeling experiments could have major implications for future computational studies on this receptor.Graphical abstract

Investigating the Antifibrotic Effects of β-Citronellol on a TGF-β1-Stimulated LX-2 Hepatic Stellate Cell Model.

Liver fibrosis, a consequence of chronic liver damage or inflammation, is characterized by the excessive buildup of extracellular matrix components. This progressive condition significantly raises the risk of severe liver diseases like cirrhosis and hepatocellular carcinoma. The lack of approved therapeutics underscores the urgent need for novel anti-fibrotic drugs. Hepatic stellate cells (HSCs), key players in fibrogenesis, are promising targets for drug discovery. This study investigated the anti-fibrotic potential of Citrus hystrix DC. (KL) and its bioactive compound, β-citronellol (β-CIT), in a human HSC cell line (LX-2). Cells exposed to TGF-β1 to induce fibrogenesis were co-treated with crude KL extract and β-CIT. Gene expression was analyzed by real-time qRT-PCR to assess fibrosis-associated genes (ACTA2, COL1A1, TIMP1, SMAD2). The release of matrix metalloproteinase 9 (MMP-9) was measured by ELISA. Proteomic analysis and molecular docking identified potential signaling proteins and modeled protein-ligand interactions. The results showed that both crude KL extract and β-CIT suppressed HSC activation genes and MMP-9 levels. The MAPK signaling pathway emerged as a potential target of β-CIT. This study demonstrates the ability of KL extract and β-CIT to inhibit HSC activation during TGF-β1-induced fibrogenesis, suggesting a promising role of β-CIT in anti-hepatic fibrosis therapies.

Structural Studies of the Taurine Transporter: A Potential Biological Target from the GABA Transporter Subfamily in Cancer Therapy.

The taurine transporter (TauT, SLC6A6) is a member of the solute carrier 6 (SLC6) family, which plays multiple physiological roles. The SLC6 family is divided into four subfamilies: GABA (γ-aminobutyric acid), monoamine, glycine and neutral amino acid transporters. Proteins from the GABA group, including the taurine transporter, are primarily considered therapeutic targets for treating central nervous system disorders. However, recent studies have suggested that inhibitors of SLC6A6 could also serve as anticancer agents. Overexpression of TauT has been associated with the progression of colon and gastric cancer. The pool of known ligands of this transporter is limited and the exact spatial structure of taurine transporter remains unsolved. Understanding its structure could aid in the development of novel inhibitors. Therefore, we utilized homology modelling techniques to create models of TauT. Docking studies and molecular dynamics simulations were conducted to describe protein-ligand interactions. We compared the obtained information for TauT with literature data on other members of the GABA transporter group. Our in silico analysis allowed us to characterize the transporter structure and point out amino acids crucial for ligand binding: Glu406, Gly62 and Tyr138. The significance of selected residues was confirmed through structural studies of mutants. These results will aid in the development of novel taurine transporter inhibitors, which can be explored as anticancer agents.

Inhibitors of SARS-CoV-2 Main Protease (Mpro) as Anti-Coronavirus Agents.

The main protease (Mpro) of SARS-CoV-2 is an essential enzyme that plays a critical part in the virus's life cycle, making it a significant target for developing antiviral drugs. The inhibition of SARS-CoV-2 Mpro has emerged as a promising approach for developing therapeutic agents to treat COVID-19. This review explores the structure of the Mpro protein and analyzes the progress made in understanding protein-ligand interactions of Mpro inhibitors. It focuses on binding kinetics, origin, and the chemical structure of these inhibitors. The review provides an in-depth analysis of recent clinical trials involving covalent and non-covalent inhibitors and emerging dual inhibitors targeting SARS-CoV-2 Mpro. By integrating findings from the literature and ongoing clinical trials, this review captures the current state of research into Mpro inhibitors, offering a comprehensive understanding of challenges and directions in their future development as anti-coronavirus agents. This information provides new insights and inspiration for medicinal chemists, paving the way for developing more effective Mpro inhibitors as novel COVID-19 therapies.

Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets.

In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein-ligand interactions fully and accurately leading to biases and inaccuracies in virtual screening and target predictions. We introduce the "Docking Score ML", developed from an analysis of over 200,000 docked complexes from 155 known targets for cancer treatments. The scoring functions used are founded on bioactivity data sourced from ChEMBL and have been fine-tuned using both supervised machine learning and deep learning techniques. We validated our approach extensively using multiple data sets such as validation of selectivity mechanism, the DUDE, DUD-AD, and LIT-PCBA data sets, and performed a multitarget analysis on drugs like sunitinib. To enhance prediction accuracy, feature fusion techniques were explored. By merging the capabilities of the Graph Convolutional Network (GCN) with multiple docking functions, our results indicated a clear superiority of our methodologies over conventional approaches. These advantages demonstrate that Docking Score ML is an efficient and accurate tool for virtual screening and reverse docking.

In Silico Drug Repurposing Against PSMB8 as a Potential Target for Acute Myeloid Leukemia Treatment.

PSMB8 emerges as a prominent gene associated with cancer survival, yet its potential therapeutic role in acute myeloid leukemia (AML) remains unexplored within the existing literature. The principal aim of this study is to systematically screen an expansive library of molecular entities, curated from various databases to identify the prospective inhibitory agents with an affinity for PSMB8. A comprehensive assortment of molecular compounds obtained from the ZINC15 database was subjected to molecular docking simulations with PSMB8 by usingtheAutoDock tool in PyRx (version 0.9.9) to elucidate binding affinities. Following the docking simulations, a select subset of molecules underwent further investigation through comprehensive ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis employing AdmetSar and SwissADME tools. Finally, RMSD, RMSF, Rg, and H bond analyses were conducted via GROMACS to determine the best conformationally dynamic molecule that represents the candidate agent for the study. Following rigorous evaluation, Adozelesin, Fiduxosin, and Rimegepant have been singled out based on considerations encompassing bioavailability scores, compliance with filter criteria, and acute oral toxicity levels. Additionally, ligand interaction analysis indicates that Adozelesin and Fiduxosin exhibit an augmented propensity for hydrogen bond formation, a factor recognized for its facilitative role in protein-ligand interactions. After final analyses, we report that Fiduxosin may offera treatment possibility by reversing the low survival rates caused by PSMB8 high activation in AML. This study represents a strategic attempt to repurpose readily available pharmaceutical agents, potentially obviating the need for de novo drug development, and thereby offering promising avenues for therapeutic intervention in specific diseases.