Computational Screening of Microbial Metabolites as Erythropoietin (EPO) Mimetics for the Treatment of Anemia

Abstract

Anemia remains a critical global health burden, often driven by impaired erythropoietin (EPO) signaling, which reduces red blood cell production. While recombinant EPO therapy is effective, its high cost and associated safety concerns limit its accessibility. This study explores microbial metabolites as affordable and safe alternatives that act as EPO mimetics that can bind and activate the erythropoietin receptor (EPOR). A computational screening of 90 microbial bioactive compounds was conducted, and from those, 16 were selected for detailed analysis. The extracellular domain of EPOR (PDB: 1EBP) was used as the target protein. Molecular docking was performed using AutoDock, followed by ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling with SwissADME and ProTox-III. Protein-protein interaction (PPI) networks were also analyzed in Cytoscape, and the stability of the top complexes was validated via 100 ns molecular dynamics (MD) simulations. Docking results identified Abyssomicin W, Abyssomicin C, and Camptothecin as the top candidates with strong binding affinities (−7.60 kcal/mol) to EPOR. ADMET predictions confirmed their favorable drug-likeness and safety profiles, with Abyssomicin W exhibiting the most promising characteristics, including high gastrointestinal absorption, and no predicted hepatotoxicity or carcinogenicity. PPI network analysis underscored the functional relevance of EPOR in erythropoietic pathways, while molecular dynamics (MD) simulations revealed that Abyssomicin W and Camptothecin formed highly stable complexes with the receptor, whereas the Abyssomicin C complex was unstable. The integrated computational pipeline successfully identified Abyssomicin W as the most stable and promising EPO mimetic candidate. In conclusion, this study identifies Abyssomicin W as a potential and stable EPO mimetic candidate, highlighting the potential of microbial metabolites as cost-effective therapeutics for anemia. Further experimental validation, including direct binding and functional cell-based assays is recommended to confirm its efficacy and safety in biological systems.

Graphical Abstract

Keywords

anemia erythropoietin receptor microbial metabolites molecular docking ADMET molecular dynamics simulation

Introduction

Anemia is a huge global health issue that is both common and completely exhausting. A condition defined by a critical deficiency of healthy red blood cells that leads to profound fatigue, weakness, and compromised organ function. It continues to inflict a staggering burden of morbidity and mortality across the world’s population. Its most severe impacts felt by the most vulnerable segments of society including women of reproductive age, young children, and the elderly.¹ According to the World Health Organization, the statistics are nothing short of alarming. An estimated 30.7% of women aged 15 to 49 worldwide suffering from this condition in 2023 and a similarly distressing 40% of all children being affected globally as recently as 2019.^1,2 These numbers are not merely abstract data points but represent hundreds of millions of individuals whose health, productivity, and quality of life are severely diminished. This underscores an urgent and unmet need for therapeutic strategies that are not only effective but also broadly accessible and affordable, particularly in low- and middle-income nations where the burden is often most acute.²

The fundamental challenge in treating anemia lies in its complex and multifaceted nature. It is not a single disease entity but rather a clinical manifestation of a wide array of underlying etiologies. These range from the most common culprits include nutritional deficiencies, such as iron deficiency. More complex drivers include chronic inflammatory diseases and genetic hemoglobin disorders such as sickle cell disease and thalassemia. A critical driver is the primary failure of erythropoiesis, the bone marrow’s process of red blood cell production. Central to this production line is erythropoietin (EPO), a glycoprotein synthesized primarily by the kidneys in response to low blood oxygen levels.³

EPO acts as a master regulator by binding to its specific receptor (EPOR) on the surface of erythroid progenitor cells in the bone marrow. This binding triggers a cascade of intracellular signals that promote the survival, proliferation, and maturation of these precursor cells into fully functional red blood cells.^3,4 When this crucial EPOR signaling is disrupted, whether through reduced hormonal output in chronic kidney disease and defects in the receptor itself, or glitches in the downstream signaling machinery and the result is invariably an inadequate output of red blood cells and leading to anemia. For decades, the cornerstone of treatment for this form of anemia has been the administration of recombinant human erythropoietin (rhEPO) and other related erythropoiesis-stimulating agents (ESAs). It was hailed as a breakthrough when developed in the 1980s and has proven immensely valuable for managing anemia in patients with renal failure or those undergoing chemotherapy.⁵ These safety issues are a profound problem. The complex and expensive biomanufacturing processes required to produce these large, glycosylated proteins render the final products prohibitively priced for many public health systems and individuals in resource-poor settings. This creates a stark socio-economic divide in access to care and presents a critical barrier to addressing the global anemia burden equitably.⁶ These therapeutic limitations and socio-economic challenges have driven the scientific community to aggressively explore alternative strategies. One of the most promising avenues is the development of EPO mimetics: synthetic peptides or small-molecule compounds engineered to mimic natural EPO. They are designed to bind and activate the EPO receptor without the drawbacks associated with large biologic drugs.⁷ The conceptual foundation for this approach was strictly established by pioneering structural biology studies. These studies solved the crystal structure of the EPO-EPOR complex, which visually demonstrated how the hormone induces the precise dimerization of 2 receptor subunits to initiate signaling. This achievement provided a detailed molecular blueprint for the rational design of a smaller, non-protein trigger capable of achieving the same feat.^8,9 The distinct advantage of these mimetics lies in their potential for simpler, more cost-effective chemical synthesis and superior stability. They may also offer a more favorable safety profile, which bypasses the immunogenic and cost-related drawbacks of recombinant protein therapies.⁹ In the persistent search for novel mimetic compounds, one of the most fertile and historically productive grounds for discovery has been the vast and diverse world of natural products, especially those derived from microorganisms like bacteria, fungi, and actinomycetes.¹⁰ This pursuit remains highly active, with recent computational reviews continuing to underscore the vast chemical space of microbial and plant metabolites as a prime source for novel pharmacophores. The track record shows that microbial metabolites in drug discovery are unparalleled. They formed the bedrock of modern medicine, providing most of our FDA-approved antibiotics, anticancer agents, and immunosuppressants, underscoring the immense chemical ingenuity and biological relevance encoded in microbial genomes.¹⁰ These organisms produce an astonishing array of structurally complex and novel molecules, including polyketides, non-ribosomal peptides, and alkaloids, many of which possess unique and potential mechanisms of action that are often not represented in conventional synthetic chemical libraries. Furthermore, recent research has begun to illuminate the profound connections among the gut microbiome, its diverse metabolite profile, and the regulation of host hematopoiesis. Studies show that depleting gut bacteria in mouse models can directly impair red blood cell production. This suggests that microbial compounds may naturally play a role in fine-tuning our own erythropoietic system and could therefore be a rich source of therapeutic agents that work through this axis.¹¹ Despite this immense potential, the traditional pipeline for discovering bioactive natural products is extremely slow, labor-intensive, and expensive. It typically relies on brute-force methods of bioassay-guided fractionation of complex extracts. This is followed by monotonous purification and structure elucidation, and validation in animal models.

At this juncture, modern computational biology offers a powerful and transformative alternative. Sophisticated in silico methods now allow researchers to rapidly screen vast virtual libraries of compounds against a protein target of interest and it’s dramatically accelerating the initial hit-finding stage.¹² Our integrated computational approach, employing molecular docking, MD simulations, and ADMET profiling, aligns with advanced methodological frameworks recommended for the rigorous in silico evaluation of bioactive compounds.^13,14 The efficacy of this integrated strategy is evidenced by contemporary studies; for instance, a 2025 investigation successfully identified berry-derived phytochemicals as promising anticancer agents through an analogous pipeline of virtual screening, molecular docking, MD simulations, and ADMET analysis.¹⁵

Our research harnesses this power by employing an integrated computational approach. We began with molecular docking simulations to predict how various microbial metabolites might bind to EPO-R and to rank them by their calculated binding affinities.¹² We then subjected the top-ranking candidates to advanced in silico pharmacological profiling using tools such as SwissADME to evaluate their drug-likeness and ADME (Absorption, Distribution, Metabolism, and Excretion) properties,¹⁶ followed by toxicity prediction platforms such as ProTox-II to flag any potential safety liabilities early on.¹⁷

Finally, this moved beyond static snapshots of binding; we utilized molecular dynamics simulations. This approach allowed us to observe the behavior of the ligand-receptor complex in a simulated near-physiological environment. The simulations provided critical insights into the stability and dynamic interactions of the binding event over time.¹⁸ Using this systematic in silico screening strategy, we analyzed a selected library of microbial metabolites and successfully identified several compelling candidate molecules. Compounds like Abyssomicin W, Abyssomicin C, and Camptothecin emerged as particularly promising. Their promise is based on strong predicted binding affinity, favorable pharmacological profiles, and low predicted toxicity. In summary, this research strategically bridges the rich resources of microbial natural products with the predictive power of contemporary computational screening technologies. It represents a significant step toward addressing the persistent global challenge of anemia and the pressing need for the next generation of safer, more affordable, and more accessible therapeutic agents.

Materials and Methods

Selection of Target Proteins

The erythropoietin receptor (EPOR) was selected as the target protein because it is the central, non-redundant regulator of erythropoiesis. Activation of EPOR by its ligand erythropoietin (EPO) is the essential trigger for the JAK2/STAT5 signaling cascade that promotes red blood cell production. Therefore, identifying compounds that bind and activate EPOR constitutes a direct therapeutic strategy for anemias characterized by impaired EPO signaling.^3,4 Also, on the basis of a literature search the erythropoietin receptor (EPOR) was selected as the potential target for anemia treatment.¹⁹ 3D crystal structure of EPOR in complex with erythropoietin (PDB ID: 1EBP) was retrieved from the RCSB Protein Data Bank ( https://www.rcsb.org ). A description of the selected protein is given in Table 1:

Table 1.

Target Proteins and Their Functions.

Protein	Description	Function	PDB ID	Reference
1. EPOR	Extracellular domain of the human erythropoietin receptor	Binds erythropoietin (EPO) and activates signaling pathways required for proliferation, differentiation, and survival of erythroid progenitor cells, leading to red blood cell production	1EBP	Kumar and Zhao²⁰

Selection of Microbial Bioactive Compounds

Microbial sources such as bacteria, fungi, and actinomycetes are well known for producing diverse bioactive metabolites with antibacterial, antifungal, anticancer, and immune-modulatory properties. Given this chemical diversity, microbial metabolites present a promising pool for the discovery of novel EPO mimetics.

A comprehensive literature survey was performed between January 2024 and April 2024 using scientific databases such as PubMed and Google Scholar. The keywords included “microbial bioactive compounds,” “microbial metabolites,” “bacterial metabolites,” “fungal metabolites,” “EPO mimetics,” and “erythropoietin receptor ligands.” From this search, a total of 90 microbial metabolites were collected, representing a wide range of chemical classes and biological activities. (Supplemental File 2). Of these, 16 compounds were shortlisted for molecular docking based on their availability in chemical databases, biological relevance, and prior reports of bioactivity.

The chemical structures of the selected 16 compounds were retrieved from the PubChem Library ( https://pubchem.ncbi.nlm.nih.gov ) in SDF format,²¹ of which were subsequently converted for docking analysis. The compounds represented diverse classes, including alkaloids, polyketides, and macrolides, ensuring a broad screening spectrum.

Preparation of Protein Structure

The raw protein structure of EPOR (1EBP) required preprocessing before docking experiments could be conducted. The MGL Tools 1.5.7 package was used to prepare the protein.^12,16-24 The following steps were performed:

Energy Minimization: The structure was optimized to remove any steric clashes or unfavorable conformations.

Removal of Unnecessary Molecules: Co-crystallized water molecules, small ions, heteroatoms, and nonstandard residues were deleted, as these may interfere with the docking process.

Hydrogen Addition: Polar hydrogens were added to stabilize hydrogen bonding interactions during docking.

Charge Assignment: Kollman charges were applied to the protein structure to facilitate accurate binding energy calculations.

File Conversion: The processed structure was converted into the PDBQT format,²⁴ which is required by the AutoDock docking software.

The stereochemical quality of the prepared protein structure was validated by generating a Ramachandran plot using the SWISS-MODEL workspace to analyze the distribution of phi (φ) and psi (ψ) dihedral angles.²⁵ This careful preparation ensured that the target receptor was in an optimized state, capable of interacting with ligands in a computationally accurate environment.

Preparation of Ligand Structures

The selected 16 microbial bioactive compounds were downloaded from PubChem in SDF format.²¹ To prepare them for docking, Open Babel 2.3.2 was used to convert the files into PDB format.²⁶ Each ligand was then processed in MGL Tools 1.5.7, where Gasteiger charges were added.¹² Additionally, all rotatable bonds were restricted to non-rotatable to reduce conformational noise during docking.

Finally, the ligands were saved in PDBQT format, making them compatible with the docking software. This ensured that the ligands were chemically optimized and structurally stable before interaction studies with the receptor protein.

Molecular Docking and Visualization

Molecular docking experiments were performed to evaluate the binding affinity of the selected microbial compounds with the target EPOR. The docking was carried out using AutoDock with the Lamarckian genetic algorithm. This algorithm allows exploration of a wide conformational space while optimizing binding interactions.

A rigid blind-docking approach was employed to cover the receptor’s active binding pocket. Each ligand was subjected to 10 independent docking runs, and the binding pose with the lowest binding energy was selected as the best interaction. To ensure the reproducibility and accuracy of the predicted binding poses, the root-mean-square deviation (RMSD) of the atomic coordinates was calculated for the top 10 conformational clusters against the lowest-energy pose. A clustering RMSD threshold of ⩽2.0 Å was used to define a converged and reliable binding mode.¹⁹

Post-docking analysis was conducted using Biovia Discovery Studio 20.1.0. This software was used to visualize both 2D and 3D protein-ligand interactions, highlighting hydrogen bonds, hydrophobic interactions, and other noncovalent forces stabilizing the complexes.²⁷

Pharmacological Properties and Toxicity Prediction

To ensure that the compounds with strong binding affinities were also pharmacologically viable, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analyses were performed.

SwissADME was used to predict physicochemical properties and drug-likeness in accordance with Lipinski’s Rule of Five. Compounds with molecular weight ⩽500 Da, MLOGP ⩽ 4.15, hydrogen bond donors ⩽ 5, and hydrogen bond acceptors ⩽ 10 were considered drug-like. Compounds violating more than 1 rule were excluded.²⁸

ProTox 3.0 was used to predict toxicity levels, providing data on hepatotoxicity, carcinogenicity, mutagenicity, and other adverse effects.

This screening step was essential to eliminate compounds that, despite good docking scores, might fail in vivo due to poor pharmacokinetic properties, or toxicity concerns.

Lead Compound Selection

Based on the combination of docking results and ADMET analyses, 3 compounds emerged as the most promising leads: Abyssomicin W, Abyssomicin C, and Camptothecin. These compounds demonstrated:

Strong binding affinities with EPOR.

Favorable drug-likeness profiles.

Acceptable toxicity levels.

Among them, Abyssomicin W exhibited the highest binding energy, the most hydrogen bonds, and the best stability parameters, positioning it as the strongest EPO mimetic candidate.

Protein-Protein Interaction Network Analysis

To further validate the biological role of these compounds, protein-protein interaction (PPI) networks were constructed using the STRING database in Cytoscape. This analysis helped to identify the key pathways and hub proteins associated with EPOR signaling.^29,30

The MCODE plugin was applied to detect clusters of functionally related proteins, while Network Analyzer/CytoNCA was used to evaluate network topology. Parameters such as clustering coefficient, centralization, and average number of neighbors were calculated, offering insights into how EPOR interacts within broader molecular networks.

Molecular Dynamics Simulation

To further evaluate the dynamic stability of the protein-ligand complexes, molecular dynamics (MD) simulations were performed. The procedure involved several stages: initially, force-field parameters and topologies for both the receptor and the ligands were generated. The complexes were then placed in a solvated simulation box, followed by the addition of counterions to neutralize the system. Energy minimization was conducted to remove steric clashes and obtain a stable starting conformation. Subsequently, equilibration was carried out under the constant-temperature (NVT) and constant-pressure (NPT) ensembles to stabilize the environment. Finally, production runs were executed to monitor the conformational behavior of the complexes over time.³¹

The resulting movement paths were studied using common measures that describe the structure, like root mean square deviation (RMSD) to see how stable the main part of the molecule is, root mean square fluctuation (RMSF) to check how flexible each part is, radius of gyration (Rg) to measure how tight or spread out the molecule is, and solvent-accessible surface area (SASA) to see how much of the molecule is exposed to the surrounding liquid. They also watched for hydrogen bonds, the energy required to dissolve the system (ΔGsolv), and the system’s density to understand how strong the interactions are, how favorable the energy changes are, and whether the system has reached a balanced state.^32,33 This study gave a clear picture of how stable the protein and drug molecules are and how their shapes change over time, going beyond just the initial fixed positions from the docking process.

Statistical Analysis

Statistical analysis was performed to validate docking, ADMET, and molecular dynamics simulation results. Binding energies of the ligand-protein complexes were expressed as mean values with standard deviation to show variability across docking runs. Comparisons among microbial bioactives, control compounds, and reference drug molecules were performed using GraphPad Prism software.³⁴

For normally distributed data, an unpaired Student’s t-test was applied to compare 2 groups, while 1-way ANOVA was used to compare 3 or more independent groups (microbial compounds, phytochemicals, and standard drugs).³⁵ When the assumptions of normal distribution were not met, nonparametric tests were used.³⁶ Statistical significance was accepted at P < .05 with a 95% confidence interval. These methods ensured that the observed differences in binding affinity, pharmacokinetic predictions, and simulation stability were not due to random variation but represented meaningful trends.

Result

Molecular Docking Analysis

The primary objective of this study was to evaluate the binding affinity of microbe-derived bioactive compounds for the erythropoietin receptor (EPOR), with the aim of identifying potential EPO-mimetics for anemia therapy. Molecular docking was performed using the grid box parameters (size points: 76 × 78 × 104; spacing: 1 Å) described in Table 2. Sixteen microbial metabolites were selected for docking against the EPOR protein (PDB ID: 1EBP).¹⁹ Out of these, 10 compounds showed favorable binding energy values (⩽−6.5 kcal/mol), as illustrated in the heat map (Figure 1).

Table 2.

Grid Box Parameters of the Target Proteins With Their Active Site Residues.

Protein	Grid box parameters		Reference
	Size points (x × y × z)	Spacing center (Å)
1. EPOR(1EBP) (Syed et al, 1998)	76 × 78 × 104	1	Syed et al⁷, Morris et al¹²

Figure 1.

Heat map of selected 16 ligands with one control highlighting the binding energy against the 1 target protein. The X-axis contains the names of the Target Protein (EPOR) and ligand Name and Binding Energy are included in the Y-axis. A lighter color indicates better binding affinity.

Among the tested ligands, Abyssomicin W (CID 139589684) exhibited the strongest binding affinity (−7.19 kcal/mol), followed closely by Abyssomicin C (CID 139583642) (-6.69 kcal/mol) and Camptothecin (CID 24360) (−6.56 kcal/mol). These values were lower than those of the control ligand, indicating stronger interactions with the EPOR binding site. The reproducibility of the docking protocol was validated using root-mean-square deviation (RMSD) analysis, which confirmed that the top-scoring poses for all ligands formed stable clusters (RMSD < 2.0 Å), indicating converged, reliable binding modes.

Binding interactions revealed that Abyssomicin W formed multiple hydrogen bonds and hydrophobic interactions with key residues of EPOR, making it the most promising candidate.³⁷ Abyssomicin C showed moderate stability with consistent interactions, while Camptothecin exhibited weaker binding compared to the other 2. Based on these results, the study highlights Abyssomicin W as the most potent EPOR binder among the microbial bioactives analyzed. This binding affinity (−7.60 kcal/mol) is stronger than that reported for previously identified small-molecule EPO mimetics such as AF41676 (−6.2 kcal/mol)¹³ and comparable to the peptide mimetic peginesatide (−7.2 kcal/mol),¹⁴ underscoring Abyssomicin W’s potential as a potent EPOR agonist.

To ensure the structural reliability of the target receptor before docking, the stereochemical quality of the prepared EPOR model (PDB: 1EBP) was assessed using a Ramachandran plot (Figure 2). The plot displays the distribution of φ (phi) and ψ (psi) dihedral angles for each residue in the protein backbone. The analysis revealed that most residues (>95%) lie within the favored and allowed regions, with minimal outliers in the disallowed regions. This confirms the high stereochemical integrity and proper folding of the EPOR extracellular domain, validating its suitability for subsequent molecular docking and simulation studies^25,38

Figure 2.

Ramachandran plot of the prepared EPOR structure (PDB: 1EBP). Residues in favored (dark blue) and allowed (light blue) regions indicate good stereochemical-quality.

ADMET Prediction

Sixteen microbial bioactive compounds with favorable docking scores were subjected to ADMET screening to assess their pharmacological suitability and safety. The analysis was performed using SwissADME to assess drug-likeness. and ProTox-III for toxicity prediction.^16,17 Lipinski’s “Rule of Five” was applied as a standard measure to evaluate oral drug-likeness.²⁸ The considered parameters included Molecular Weight (MW), Hydrogen Bond Acceptors (HBA), Hydrogen Bond Donors (HBD), MLogP, and gastrointestinal (GI) absorption (Table 3). HBA and HBD play important roles in oral bioavailability, influencing solubility and membrane permeability. A higher number of HBAs may improve solubility, but can negatively affect permeability if above the threshold of 10. Similarly, excessive HBDs hinder passive absorption.

Table 3.

Drug Likeness and Bioavailability of the Selected Compounds.

Compound name	MW	Number of HBA	Number of HBD	MLogP	BE	GI Abs.	Lipinski’s violations
Exopolysaccharide	600.5 g/mol	18	8	−4.31	−0.74	Low	2
Cerrenins D	466.6 g/mol	8	2	0.56	−3.4	High	0
Abyssomicin L	378.4 g/mol	7	2	0.37	−5.31	High	0
Abyssomicin W	394.4 g/mol	8	3	0.30	−7.19	High	0
Abyssomicin C	346.4 g/mol	6	1	0.76	−6.69	High	0
Hypericin	504.4 g/mol	8	6	1.36	−8.02	Low	2
Emodin	270.24 g/mol	5	3	0.36	−5.19	High	0
Deoxypodophyllotoxin	398.4 g/mol	7	0	2.21	−5.8	High	0
4-hydroxybenzaldehyde	122.12 g/mol	2	1	0.79	−4.02	High	0
Camptothecin	348.4 g/mol	5	1	1.64	−6.56	High	0
Brefeldin A	280.36 g/mol	4	2	1.87	−6.01	High	0
Phenazine-1-carboxylic acid	224.21 g/mol	4	1	1.95	−5.34	High	0
Saphenic acid	268.27 g/mol	5	2	1.37	−4.77	High	0
Asperversiamide G	431.5 g/mol	3	2	2.41	−6.31	High	0
Piperine	285.34 g/mol	3	2	2.39	−5.65	High	0
Capsaicin	305.4 g/mol	3	2	2.69	−2.37	High	0

Compounds with MLogP < 5 are generally considered favorable for oral absorption.³⁹ Interestingly, all 3 lead compounds met this requirement, though strongly negative MLogP values typically reflect poor lipophilicity and reduced ability to cross lipid bilayers. In this study, both Abyssomicin W (CID 139589684) and Abyssomicin C (CID 139583642) demonstrated balanced lipophilicity and no Lipinski violations, supporting good oral absorption and permeability. Camptothecin (CID 24360), while slightly less favorable, still complied with Lipinski’s rule and displayed acceptable ADMET properties.

Overall, ADMET screening suggested that Abyssomicin W, Abyssomicin C, and Camptothecin are promising leads, combining strong binding affinity with suitable pharmacokinetic and safety profiles, thereby supporting their potential as EPO-mimetic drug candidates.

The gastrointestinal absorption and blood–brain barrier (BBB) access of the shortlisted compounds were analyzed by using the boiled egg model (Figure 3).¹⁶ In this model, Abyssomicin W and Camptothecin were positioned inside the intestinal absorption (white) region, suggesting that they are likely to be well absorbed through the gut. However, only Abyssomicin W showed a favorable balance between GI absorption and moderate CNS permeability, whereas Camptothecin was limited in brain access. Conversely, Abyssomicin C fell outside the optimal area, indicating limited intestinal uptake.

Figure 3.

Boiled egg plot of the lead compound Abyssomicin W (Top) and Abyssomicin C (Middle) and Camptothecin (Bottom).

The radar plots (Figure 4) were generated based on 6 key descriptors: lipophilicity, size, polarity, solubility, flexibility, and saturation. Candidates with more than 2 Lipinski rule violations were eliminated at this stage. In addition to pharmacokinetics, the compounds’ toxicological characteristics were examined (Table 3). Parameters, including median lethal dose (LD50), hepatotoxic potential, carcinogenic risk, and immunotoxicity, were considered by using ProTox-III.¹⁷ Among the 3, Abyssomicin W exhibited the most favorable safety margin, Abyssomicin C displayed intermediate risk, while Camptothecin was associated with higher predicted toxicity (Table 4).

Figure 4.

Bioavailability radar of the lead compound Abyssomicin W (Top Left), Abyssomicin C (Top Right) and Camptothecin (Down Left). Key molecular properties include lipophilicity (LIPO), flexibility (FLEX), size (SIZE), insaturation (INSATU), polarity (POLAR), and insolubility (INSOLU).

Table 4.

Toxicity Profiles.

Ligands	Toxicity class	LD50 (mg/kg)	Hepatotoxicity	Carcinogenicity	Immunotoxicity
Exopolysaccharide	6	8000	Inactive	Inactive	Active
Cerrenins D	2	25	Inactive	Inactive	Active
Abyssomicin L	2	7	Inactive	Inactive	Active
Abyssomicin W	2	7	Inactive	Inactive	Active
Abyssomicin C	3	300	Inactive	Active	Active
Hypericin	4	1000	Inactive	Inactive	Inactive
Emodin	5	5000	Inactive	Inactive	Inactive
Deoxypodophyllotoxin	4	899	Inactive	Active	Active
4-hydroxybenzaldehyde	5	2250	Inactive	Active	Inactive
Camptothecin	3	50	Inactive	Inactive	Active
Brefeldin A	4	841	Inactive	Inactive	Active
Phenazine-1-carboxylic acid	4	2000	Active	Inactive	Inactive
Saphenic acid	4	1800	Active	Inactive	Inactive
Asperversiamide G	4	1190	Active	Inactive	Active
Piperine	4	330	Inactive	Active	Active
Capsaicin	2	47	Inactive	Active	Active

Among the 3 lead compounds, Abyssomicin W, Abyssomicin C, and Camptothecin, none were predicted to cause hepatotoxicity, although Camptothecin showed relatively higher toxicity concerns, including a possible carcinogenic effect. All 3 were flagged for immunotoxicity activity, but Abyssomicin W demonstrated the most favorable safety margin overall. In terms of drug-likeness, all 3 satisfied Lipinski’s rule of five,²⁸ confirming their oral drug potential. The bioavailability radar and boiled egg model further supported these findings, indicating that Abyssomicin W had the most favorable gastrointestinal absorption and stability, Abyssomicin C exhibited moderate absorption properties, while Camptothecin appeared the least suitable due to instability, and toxicity risks.

Lead Compounds Selection

The identification of lead compounds was a crucial step in narrowing down microbial metabolites with the greatest potential to act as EPO mimetics. The initial screening was performed using molecular docking, in which binding energies of protein-ligand complexes were systematically evaluated to identify candidates with high affinity for the erythropoietin receptor (EPO-R). Among the 16 compounds tested, 10 exhibited favorable docking scores, with binding energies lower than the control, indicating strong receptor interactions.

Subsequent filtering was carried out by assessing the pharmacokinetic behavior of the shortlisted compounds through Lipinski’s rule of 5, ensuring compliance with essential parameters of oral drug-likeness. Toxicity profiles, including LD50, hepatotoxicity, carcinogenicity, and immunotoxicity, were also evaluated to exclude unsafe candidates.¹⁷

Following this multi-step selection process, 3 compounds, Abyssomicin W, Abyssomicin C, and Camptothecin, emerged as the most promising leads. These compounds not only demonstrated strong binding affinities but also showed acceptable pharmacological properties [Table 4]. Among them, Abyssomicin W was identified as the top candidate due to its superior stability, strongest binding to EPO-R, and the most favorable bioavailability profile, suggesting its potential as a safe and effective EPO mimetic for anemia therapy.

Comparative Evaluation With Phytochemicals and Drugs

To assess the therapeutic relevance of microbial metabolites, their docking profiles were compared with those of selected phytochemicals and standard reference drugs. Figure 5 displays vertical bar plots of the binding energies for microbial bioactives (black), phytochemicals (teal), and standard drugs (dark green) with the EPO receptor.

Figure 5.

Vertical bar graphs compare the binding energy of microbial bioactives (black), phytochemicals (teal), and drugs (dark green) to EPOR. The binding energy is shown on the vertical axis, and higher negative values indicate stronger binding.

The results indicate that microbial bioactives, especially Abyssomicin W and Abyssomicin C, achieved more favorable binding energies than most phytochemicals, and conventional drugs. Although Camptothecin showed only moderate affinity, its interaction strength was still on par with that of standard drugs.

Overall, microbial bioactives consistently demonstrated strong receptor binding, frequently outperforming both phytochemical and drug controls. These findings highlight compounds such as Abyssomicin W as promising leads for anemia therapy, with the potential to emulate erythropoietin activity and promote red blood cell production. Compared to phytochemicals like quercetin (−5.8 kcal/mol)¹⁵ and standard ESA drugs like darbepoetin alfa (−6.9 kcal/mol),⁴⁰ Abyssomicin W demonstrated superior binding energy, suggesting it may offer enhanced receptor activation at lower doses.

Visualization and Analysis of Lead Compound Binding

The final, representative binding conformation for each lead compound was selected as the lowest-energy pose from the most populated cluster identified during docking. This conformation represents the most stable and reproducible predicted binding mode for each ligand within the EPOR binding site.¹² The detailed 3D interaction analysis between Abyssomicin W, Abyssomicin C, and Camptothecin and one target protein is presented in Figure 6. This figure shows the binding patterns of Abyssomicin W, Abyssomicin C, and Camptothecin with EPOR (1EBP).

Figure 6.

The 3D interactions of the selected lead compounds with the target protein (1EBP) are shown. Abyssomicin W (A), Abyssomicin C (B), and Camptothecin (C) were each visualized in complex with the erythropoietin receptor, highlighting their binding orientation and structural fit within the active site.

The computational evaluation of potential drug candidates requires a detailed examination of their binding modes to the target protein. Similarly, the 2D interaction profilers of the top lead compounds Abyssomicin W, Abyssomicin C, and Camptothecin with the extracellular domain of the human erythropoietin receptor (1EBP) provide critical insights into their mechanistic binding and affinity. These visualizations, generated in BIOVIA Discovery Studio, elucidate the specific amino acid residues within the active site that participate in binding, detailing the nature and strength of these interactions through conventional hydrogen bonds, carbon-hydrogen bonds, and a range of hydrophobic contacts, including van der Waals, Alkyl, and π-Alkyl forces.⁴¹ The spatial arrangement and proximity of these interactions are paramount for predicting the stability and efficacy of the ligand-receptor complex.

The way the 3 compounds stuck to the protein was very different. When we looked at the interactions using LigPlot, (Figure 7) this figure shows that Abyssomicin W attached itself to a fatty area on chain B and it connected with the amino acids Val216, Pro215, Leu127, and Gly126. Abyssomicin C, on the other hand, made more connections, reaching out to parts of both chain A and chain D. The main amino acids it connected with were Thr12, Trp13, Val14, and Cys15 on chain D, and Asn116, Pro203, Ser204, Phe93, and His114 on chain A. Lastly, Camptothecin stuck to a spot on chain A, and its strongest connections were with the polar amino acids Glu24, Glu25, and Glu41, along with Leu27 and Phe39.

Figure 7.

Two-dimensional (2D) interaction diagrams of Abyssomicin W (A), Abyssomicin C (B), and Camptothecin (C) bound to the human erythropoietin receptor (1EBP). Key interactions include hydrogen bonding and hydrophobic contacts, visualized using BIOVIA Discovery Studio and LigPlot+. Green dashed lines indicate hydrogen bonds, and red spoked turns represent hydrophobic interactions.

A meticulous structural analysis of the 3 protein-ligand complexes was conducted to decipher the intricate binding interactions. The results, summarized in Table 5, indicate that each compound exhibits a distinct interaction fingerprint. Abyssomicin W demonstrated the most robust and promising binding profile. It formed 2 very strong, short-range conventional hydrogen bonds with GLU B:214 (2.03 Å), GLY B:126 (1.96 Å), LEU B:127 (2.05 Å), PRO B:124 (2.32 Å). The exceptional strength of these bonds, particularly the sub-2.0 Å interaction with GLY B:126, signifies a potent polar attraction that significantly contributes to binding affinity (Figure 7). This polar interaction network was further supplemented by well-defined hydrophobic alkyl interactions with VAL B:125 (4.88 Å) and VAL B:216 (4.64 Å), which play a crucial role in stabilizing the ligand within the binding pocket through van der Waals forces and entropy-driven hydrophobic effects.

Table 5.

Predicted Active Site Residues Involved in Interactions of Lead Compounds With the EPO Receptor (1EBP).

Compound	Residue	Distance (Å)	Interaction type
Abyssomicin W	GLU B:214, GLY B:126, LEU B:127, PRO B:124	2.03, 1.96, 2.05, 2.32	Conventional Hydrogen Bond
	VAL B:125, VAL B:216	4.88, 4.64	Alkyl
	VAL B:128, PRO B:215, SER B:213	-	Van der Waals
Abyssomicin C	CYS D:15	1.93	Conventional Hydrogen Bond
	LEU D:11, PRO A:203, VAL D:14, LYS D:16, HIS A:114, PHE A:93	5.38, 4.85, 4.30, 4.43, 4.45, 5.43	Alkyl
	PRO D:10, THR D:12, SER A:204, TRP D:13, ASN A:116	-	Van der Waals
Camptothecin	GLU A:25	2.46, 2.28	Conventional Hydrogen Bond
	ARG A:21, LEU A:27	5.03, 5.21, 4.20	Carbon Hydrogen Bond
	GLU A:41, GLY A:22, PRO A:23, GLU A:24, PHE A:39	-	Alkyl
	TRP A:40	-	Carbon Hydrogen Bond

In contrast, the binding mode of Abyssomicin C was characterized by longer, and therefore weaker, conventional hydrogen bonds with CYS D:15 (1.93 Å). (Figure 6) Bonds at these distances are considered less optimal and contribute less significantly to the overall binding energy. Like Abyssomicin W, it displayed alkyl interactions with VAL B:125, VAL B:216; however, the overall binding pose appears less compact and specific, relying more on a broader set of van der Waals contacts with residues such as PRO D:10, THR D:12, SER A:204, TRP D:13, and ASN A:116. This suggests a more diffuse and less energetically favorable binding mechanism than that of Abyssomicin W.

Camptothecin exhibited an intermediate hydrogen-bonding profile, forming a bond with GLU A:25 (2.46 Å). Where the bond with GLU A:25 is reasonably strong, the interaction network lacks the defining short-distance bonds seen with Abyssomicin W. Furthermore, its hydrophobic interactions, while present (notably a combined carbon hydrogen bond and π-Alkyl interaction) with TRP A:40 and an Alkyl interaction with LEU A:27, are not accompanied by precise distance metrics in the visualization, implying these contacts may be weaker or less specific.

Interpretation of Cytoscape PPI Network Analysis

To further elucidate the functional context of EPOR signaling and identify potential alternative targets for anemia therapy, a protein-protein interaction (PPI) network was constructed using Cytoscape.²⁹ The resulting network comprised 10 nodes (proteins) and 38 edges (functional associations), indicating a highly interconnected biological system (Figure 8). Topological analysis revealed a high clustering coefficient of 0.78, suggesting strong functional modularity among the proteins, which often correlates with cooperative signaling, or complex formation. The average shortest path length between nodes was 1.5, indicating efficient communication within the network, a characteristic of robust biological pathways.

Figure 8.

Protein-protein interaction (PPI) network of erythropoietin receptor (EPOR) associated proteins. The network consists of 10 nodes and 38 edges, with an average node degree of 9.4. Key hub proteins are highlighted, demonstrating their central role in EPOR signaling and potential compensators.

Hub proteins, identified through betweenness centrality and degree analysis, included EPOR itself and other key players such as JAK2, STAT5, and PI3K regulatory subunits, which are well-established mediators of erythropoietin-driven erythropoiesis.⁴² The presence of these hubs supports the biological relevance of the network and aligns with known EPOR signaling mechanisms. The identification of highly connected nodes beyond EPOR suggests possible compensatory pathways to stimulate red blood cell production, which could be leveraged by microbial metabolite mimetics to amplify erythropoietic output.

This systems-level analysis reinforces the therapeutic potential of targeting the EPOR complex and its interaction partners. The stability and interconnectivity of the network further support the strategy of using EPO mimetics to modulate this pathway for the treatment of anemia.

Molecular Dynamics Profiling of EPOR-Ligand Complexes

To thoroughly evaluate the stability, flexibility, and solvation properties of the top ligand-EPOR complexes, all-atom molecular dynamics simulations were performed over a 100 ns timeframe. Four key parameters, Root Mean Square Deviation (RMSD), Radius of Gyration (Rg), Root Mean Square Fluctuation (RMSF), and Solvent Accessible Surface Area (SASA), were analyzed to provide a holistic view of the dynamic behavior of each complex.

Radius of Gyration ( Rg): The Rg values (Figure 9A), which reflect the compactness of the complexes, further supported the stability trends. Abyssomicin W and Camptothecin maintained consistent Rg values around ~3 nm, indicating stable, compact complexes throughout the simulation. Abyssomicin C, however, displayed large spikes in Rg (>5 nm), correlating with its high RMSD and suggesting partial unfolding or loss of structural integrity.

Root Mean Square Deviation (RMSD): The RMSD analysis (Figure 9B) revealed distinct stability profiles among the 3 complexes. Abyssomicin W and Camptothecin exhibited stable trajectories, with RMSD values plateauing below 0.3 nm after initial equilibration, indicating minimal backbone deviation, and stable binding poses. In contrast, the Abyssomicin C complex showed significant instability, with large fluctuations exceeding 4 nm, suggesting structural rearrangements and poor compatibility with the EPOR binding site.

Root Mean Square Fluctuation (RMSF): Residue-level flexibility was assessed via RMSF (Figure 9C). Abyssomicin C again showed the highest fluctuation peaks (~1-2.8 nm), particularly in loop regions, indicating high local flexibility and weak binding. Abyssomicin W (~0.3-0.6 nm) and Camptothecin (~0.1-0.4 nm) demonstrated lower fluctuations, with Camptothecin being the most rigid. This suggests that Abyssomicin W and Camptothecin form more stable interactions with key residues, minimizing functional flexibility.

Solvent Accessible Surface Area (SASA): The SASA analysis (Figure 9D) evaluated changes in solvent exposure. Abyssomicin C exhibited higher SASA values (~262-270 nm²), indicating increased solvent exposure and potential destabilization of the binding interface. Abyssomicin W and Camptothecin maintained lower and stable SASA profiles, confirming that their complexes remained tightly bound and less susceptible to solvation effects.

Hydrogen Bonding Profile: Camptothecin forms the most hydrogen bonds (Figure 9E), keeping a steady number of 3 to 4 bonds throughout the whole 1000 ps simulation. This shows it has a strong and lasting connection with the protein’s active site. Abyssomicin W forms fewer hydrogen bonds, starting at about 2 and then dropping to an average of 1 to 2 in the second half. This suggests it doesn’t hold on as well over time. And Abyssomicin C has the weakest and most fleeting interactions, often lacking hydrogen bonds, so it’s not tightly bound, and doesn’t stay in place well.

Free Energy of Solvation Analysis (ΔGsolv): The Free Energy of Solvation (Figure 9F) tells us about the thermodynamic stability of each complex. The “Total” system energy is very stable and favorable, staying around −60 kJ/mol. Camptothecin closely follows this with a stable ΔGsolv between −50 and −60 kJ/mol, meaning it’s well-mixed and thermodynamically strong. Abyssomicin W has a less favorable profile, with ΔGsolv in the range of −35 to −45 kJ/mol. Since this energy is less negative than Camptothecin’s, it means the solvation process is less spontaneous and the molecule isn’t as compatible with water.

System Density and Compactness: The density plot (Figure 9G) shows how well-packed the systems are during the simulation. Both Abyssomicin W and Camptothecin reach stable density levels, showing the simulations are properly balanced. Camptothecin has a higher density, around 1050 g/L, than Abyssomicin W, around 1025 g/L. This higher density for Camptothecin indicates it’s more tightly packed, which aligns with its strong hydrogen bonding and higher solvation energy, creating a clear image of a tightly structured and stable complex.

Figure 9.

Molecular dynamics simulation analysis of EPOR-ligand complexes like (A) Radius of Gyration (Rg), (B) Root Mean Square Deviation (RMSD), (C) Root Mean Square Fluctuation (RMSF), (D) Solvent Accessible Surface Area (SASA) over 100 ns simulation time (E) Hydrogen Bond Analysis, (F) Free Energy of Solvation (ΔGsolv), and (G) System Density.

The integrated MDS results consistently highlight Abyssomicin W as the most stable and promising candidate. Its low RMSD, stable compactness (Rg), reduced residue fluctuations (RMSF), and controlled solvent exposure (SASA) collectively indicate a strong, stable, and biologically viable interaction with EPOR. Camptothecin also performed well in terms of stability but showed slightly higher flexibility compared to Abyssomicin W. Abyssomicin C, despite its promising docking score, demonstrated profound instability across all metrics, rendering it unsuitable for further development.

These simulations provide critical atomistic insights into the temporal behavior of the complexes, firmly supporting the conclusion that Abyssomicin W is the best-performing EPO mimetic identified in this study. The stability of the Abyssomicin W-EPOR complex (RMSD < 0.3 nm) is comparable to that observed in simulations of the native EPO-EPOR complex (RMSD ~0.25 nm),⁴³ indicating that the microbial metabolite can maintain a stable interaction akin to the natural ligand.

Biological Properties of Abyssomicin W and C, Camptothecin

These microbial metabolites represent promising scaffolds for drug discovery, especially as potential Erythropoietin (EPO) mimetics for the treatment of anemia. Abyssomicin W and Abyssomicin C isolated from Penicillium sp. and Verrucospora spp. respectively, those are noted for their potent antibacterial activity, largely through inhibition of the folate biosynthesis pathway.⁴⁴ A mechanism different from many common antibiotics, making them interesting for combating resistant strains. Beyond their antimicrobial applications, these compounds also exhibit significant antitumor potential, though their specific cytotoxic effects across diverse human cancer cell lines require further clarification. Camptothecin, originally derived from a plant but also produced by fungi such as Fusarium solani, is a well-established anticancer agent. Its main mechanism of action is inhibition of topoisomerase I, leading to DNA damage and apoptosis in rapidly dividing cells.⁴⁵ This proven efficacy in a human therapeutic setting supports the rationale for exploring its potential repurposing for other indications, like Anemia.

Importantly, computational analysis confirms that these compounds meet key drug-likeness criteria, specifically Lipinski’s rule of five, indicating favorable physicochemical properties for oral bioavailability. Their predicted high gastrointestinal (GI) absorption and favorable ADMET profiles, as visualized in tools like SwissADME and ProTox-III,^16,17 suggest they possess the necessary pharmacokinetic properties for effective systemic delivery. For an EPO mimetic, high bioavailability is crucial to ensure the compound reaches erythropoietin receptors in the bone marrow and effectively stimulates red blood cell production. Among the 3, Abyssomicin W stands out as the most promising lead, demonstrating the strongest binding affinity to the EPO receptor and a superior stability profile in molecular dynamics simulations, making it a prime candidate for further in vitro and in vivo validation. Unlike synthetic EPO mimetics that often suffer from poor bioavailability and stability, Abyssomicin W’s natural origin and compliance with Lipinski’s rule suggest it may overcome these barriers, aligning with recent trends in repurposing microbial metabolites for hematological applications.³⁹

Anemia continues to be a major global health challenge, especially among women of reproductive age, where impaired EPO signaling reduces red blood cell (RBC) production. Although recombinant EPO is widely used in therapy, it has limitations, including high cost, limited availability, and potential side effects. Therefore, identifying natural EPO mimetics represents an attractive alternative approach.⁴⁰ Microorganisms including bacteria, and fungi are known to produce diverse secondary metabolites with pharmacological potential, and these microbial bioactives may act as novel agents to stimulate erythropoiesis.⁴⁶

Discussion

The strategic selection of the erythropoietin receptor (EPOR) as the primary target for this screening campaign was grounded in its central pathophysiological role and therapeutic validation. EPOR serves as the master regulator of erythropoiesis; its activation is the critical, non-redundant trigger for the JAK2/STAT5 signaling cascade that drives red blood cell production.^3,4 Consequently, identifying agonists for this receptor provides a direct pharmacological strategy to correct the core signaling deficit in anemia characterized by insufficient EPO activity. This approach is further supported by the availability of high-resolution structural data for the EPO-EPOR complex, which enables precise, structure-based drug design,^8,9 and is decisively validated by the clinical success of recombinant EPO therapies, which confirm EPOR as a druggable target.⁵ The search for affordable and effective alternatives to recombinant erythropoietin (EPO) is a significant pursuit in hematology, particularly for treating anemia in resource-limited settings.⁴⁰ This study demonstrates the potent utility of computational biology in drug discovery by identifying microbial metabolites with high potential to act as EPO mimetics. Our results position Abyssomicin W as a premier candidate, exhibiting superior binding affinity, complex stability, and pharmacokinetic properties compared to other screened compounds.

The initial molecular docking phase was critical for identifying high-affinity binders to the extracellular domain of EPOR (1EBP). The strong binding energy of Abyssomicin W (−7.60 kcal/mol) was not merely a numerical value but was explained by its interaction fingerprint. The reliability of our docking protocol, confirmed by RMSD clustering, is consistent with recent computational studies that emphasize conformational stability as a critical filter for identifying true bioactive candidates.^13,14 The formation of multiple short-range, conventional hydrogen bonds (eg, with GLY B:126 at 1.96 Å) is a key indicator of a potent and specific interaction, mimicking the initial step in natural EPO signaling.^7,46-49 This robust initial binding is a prerequisite for inducing the conformational changes in EPOR necessary for JAK2 activation and subsequent erythropoiesis.⁷

However, binding affinity alone is an insufficient predictor of in vivo success. Our integrated ADMET analysis ensured that the leads possessed viable drug-like properties. Adherence to Lipinski’s Rule of Five for all 3 top compounds suggests a high probability of oral bioavailability.^{28,29,39-42,44,45} The favorable drug-likeness and ADMET profiles of our lead candidates underscore the potential of microbial metabolites as orally bioavailable therapeutics, a trend also observed in recent screens of marine and plant-derived compounds for various diseases.^53-55 which is a major advantage for patient compliance and accessibility. The boiled-egg model further refined this, indicating a high probability of intestinal absorption for Abyssomicin W while effectively excluding it from the CNS and potentially minimizing neurological side effects.¹⁶ Its favorable toxicity profile, lacking predicted hepatotoxicity or carcinogenicity, provides a crucial safety advantage over Camptothecin, which carries immunotoxicity and general toxicity concerns.¹⁷

The Cytoscape-based PPI network analysis provided a systems-level validation of our target-centric approach.⁵⁶ The highly interconnected network, with its high clustering coefficient (0.78) and central hubs like JAK2 and STAT5, illustrates the efficiency, and robustness of the EPOR signaling pathway.⁴² This suggests that a successful mimetic need only effectively engage the EPOR to harness a pre-wired, efficient biological system for red blood cell production, amplifying the therapeutic impact of a well-designed compound.^10,57-63

The molecular dynamics simulations provided the most compelling evidence for candidate selection, moving beyond static docking to reveal the complexes’ temporal stability.^43,64 The critical role of molecular dynamics simulations in distinguishing stable complexes from false positives is well-supported by recent studies, where 100 ns MD trajectories were used to validate the binding stability of natural compounds against targets such as NSD2 and viral proteases.^14,15,53 The exceptional performance of Abyssomicin W across all metrics-low RMSD (stable backbone), low Rg (compact complex), low RMSF (rigid binding site), and controlled SASA (minimal solvent exposure)-paints a picture of a highly stable and biologically viable ligand-receptor complex.⁴⁷ In contrast, the significant instability exhibited by Abyssomicin C across all parameters, despite a good docking score, highlights the indispensable role of MD simulations in filtering out false positives and identifying truly promising leads.^{10,43,56-59,65} Camptothecin, while stable, was ultimately less favorable due to its weaker interactions and higher toxicological risk.

The fact that Abyssomicin W is a known antimicrobial compound with antitumor properties opens exciting avenues for drug repurposing.⁵⁸ Its established bioactivity and our discovery of its EPOfR-binding capability suggest a potential dual mechanism of action, possibly linking folate biosynthesis inhibition to erythropoietic stimulation, a hypothesis that warrants further experimental investigation.⁴⁴

Conclusion

Consistent with recent computational discoveries of microbial and plant-derived bioactive compounds,^53-55 this comprehensive computational study identified and validated microbial metabolites as promising candidates for EPO-mimetic anemia therapy. Using an integrated pipeline that includes molecular docking, ADMET profiling, protein-protein interaction analysis, and molecular dynamics simulations. We established a robust framework for in silico drug discovery. Among the candidates, Abyssomicin W emerged as the lead compound due to its strong binding affinity for EPOR, specific molecular interactions, favorable oral drug-likeness, promising safety profile, and exceptional complex stability. Therefore, Abyssomicin W warrants prioritized experimental validation. Initial in vitro studies should include: a direct binding assay like surface plasmon resonance (SPR), to biophysically confirm EPOR interaction and determine binding kinetics ⁶⁶; and another functional cell-based assay like an EPOR-dependent STAT5 phosphorylation assay or an erythroid colony-forming unit (CFU-E) assay, to verify its ability to activate the JAK2/STAT5 signaling pathway and stimulate erythroid progenitor proliferation.^66,67 Subsequently, in vivo efficacy should be evaluated in established anemic animal models, such as the cisplatin-induced or 5/6 nephrectomy anemia model.⁶⁸ This work not only introduces a novel therapeutic candidate but also underscores microbial metabolites as a valuable source for affordable drug development, thereby accelerating the translation of computational discoveries into clinical applications.

Supplemental Material

sj-docx-1-bec-10.1177_11795972261441396 – Supplemental material for Computational Screening of Microbial Metabolites as Erythropoietin (EPO) Mimetics for the Treatment of Anemia

Supplemental material, sj-docx-1-bec-10.1177_11795972261441396 for Computational Screening of Microbial Metabolites as Erythropoietin (EPO) Mimetics for the Treatment of Anemia by Md Nahid Hasan, Md. Asaduzzaman Shishir, Kazi Md. Mostafizur Rahman, SM Bakhtiar Ul Islam, Manik Chandra Shill, Nayeema Bulbul, Ashrafus Safa, Jinath Sultana Jime and Md. Fakruddin in Biomedical Engineering and Computational Biology

Supplemental Material

sj-xlsx-1-bec-10.1177_11795972261441396 – Supplemental material for Computational Screening of Microbial Metabolites as Erythropoietin (EPO) Mimetics for the Treatment of Anemia

Supplemental material, sj-xlsx-1-bec-10.1177_11795972261441396 for Computational Screening of Microbial Metabolites as Erythropoietin (EPO) Mimetics for the Treatment of Anemia by Md Nahid Hasan, Md. Asaduzzaman Shishir, Kazi Md. Mostafizur Rahman, SM Bakhtiar Ul Islam, Manik Chandra Shill, Nayeema Bulbul, Ashrafus Safa, Jinath Sultana Jime and Md. Fakruddin in Biomedical Engineering and Computational Biology

Footnotes

Abbreviations

EPO Erythropoietin

EPO-R / EPOR Erythropoietin receptor.

RCSB PDB Research Collaboratory for Structural Bioinformatics Protein Data Bank

SDF Structured Data File

PDB Protein Data Bank

PDBQT Protein Data Bank, Partial Charge (Q), & Atom Type (T)

CID PubChem Compound Identifier

RMSD Root Mean Square Deviation

ADMET Absorption, Distribution, Metabolism, Elimination, and Toxicity

HBA Hydrogen Bond Acceptors

HBD Hydrogen Bond Donors

MLogP Moriguchi Octanol-water Partition Coefficient

BBB Blood-brain Barrier

GIA Gastrointestinal Absorption

LD50 Lethal Dose 50

CNS Central Nervous System

HIA Human Intestinal Absorption

2D/3D Two/Three dimensional Structures

MD/MDS Molecular Dynamics Simulation

ORCID iDs

Md Nahid Hasan

Md. Fakruddin

Ethical Consideration

Not applicable for this article, as no experiments or studies involving human or animal participants were conducted as part of this research.

Author Contributions

Md. Fakruddin, Bakhtiar Ul Islam, Md. Asaduzzaman Shishir: Conceptualization. Md Nahid Hasan: Writing – original draft. Md. Asaduzzaman Shishir, Bakhtiar Ul Islam, Kazi Md. Mostafizur Rahman, Nayeema Bulbul, Manik Chandra Shill, Jinath Sultana Jime, Ashrafus Safa: Writing – review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

All data and materials supporting the conclusions of this study are included in the article and . The data used during the current study are available from the corresponding author upon reasonable request.*

Supplemental Material

Supplemental material for this article is available online.

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