Network pharmacology and integrative bioinformatics analyses identify PDE1A as a key target of pirfenidone in idiopathic pulmonary fibrosis

Jing Wu; Haseeb Khaliq; Yanyan Ke; Qudrat Ullah; Sheikh Arslan Sehgal; Xue Yi

doi:10.1371/journal.pone.0342991

. 2026 Apr 10;21(4):e0342991. doi: 10.1371/journal.pone.0342991

Network pharmacology and integrative bioinformatics analyses identify PDE1A as a key target of pirfenidone in idiopathic pulmonary fibrosis

Jing Wu ^1,², Haseeb Khaliq ³, Yanyan Ke ^1,^4,^*, Qudrat Ullah ^5,^*, Sheikh Arslan Sehgal ⁶, Xue Yi ^1,^4,^*

Editor: Mahbub Hasan⁷

PMCID: PMC13068261 PMID: 41961843

Abstract

Pirfenidone, an antifibrotic agent, has been shown to be effective in the treatment of idiopathic pulmonary fibrosis (IPF). However, the exact mechanism of action and clinical efficacy require further investigation and validation. This study commenced by identifying pathogenic genes associated with IPF through the GeneCards database. Potential targets of pirfenidone were subsequently screened through PubChem and Swiss TargetPrediction, and overlapping targets were identified through Venn diagram analysis. Enrichment analysis of potential target genes was performed to identify the key biological processes and pathways involved in the action of pirfenidone. The main target genes were subsequently identified through the GSE10667 and GSE110147 datasets. The affinity of PDE1A to pirfenidone was predicted by molecular docking and MicroScale Thermophoresis (MST). Finally, the expression and antifibrotic effects of pirfenidone on PDE1A were validated through data from the GSE226249 dataset. PDE1A, identified by GeneCards and Swiss TargetPrediction, was found to be an important mediator of the antifibrotic effect of pirfenidone. The enrichment analysis revealed biological processes such as cyclic nucleotide-mediated signaling and cAMP-mediated signaling. KEGG pathway analysis further linked pirfenidone activity to pathways involved in calcium signaling, taste transduction, morphine dependence, renin secretion and purine metabolism. Molecular docking, molecular dynamics (MD) simulations and MST results revealed a strong binding affinity between pirfenidone and PDE1A. MD simulations showed the stability of the complex. It was observed that the RMSD analysis of the complex stabilized between 0.6 to 0.8 nm throughout the simulation, however RMSF showed minimal fluctuation. Data from the GSE226249 dataset confirmed that upregulation of PDE1A promotes fibrosis, whereas pirfenidone downregulates PDE1A, thereby exerting its antifibrotic effect. The inhibition of IPF progression by pirfenidone is mediated by PDE1A, providing insights into its therapeutic mechanism.

Introduction

IPF is a chronic and progressive interstitial lung disease characterized by the destruction of alveolar structures and irreversible fibrotic remodeling, leading to severe deterioration in patients’ quality of life and a poor prognosis [1–3]. Although research into the pathogenesis of IPF has made some progress in recent years, current clinical treatment options remain limited, and their efficacy is suboptimal. Therefore, the exploration of new therapeutic targets and pharmacological intervention strategies is urgently needed. Pirfenidone, an antifibrotic drug approved for the treatment of IPF [4,5], has been shown to slow the progression of fibrosis by modulating several cellular and molecular pathways involved in fibrogenesis. Its mechanisms of action include anti-inflammatory, antioxidant, and antiproliferative effects. However, the exact mechanism by which pirfenidone exerts its antifibrotic effect is not yet fully understood.

PDE1A is an intracellular enzyme responsible for the degradation of cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP), two important intracellular messengers involved in the regulation of various cellular functions and biological processes, such as proliferation, cell differentiation, migration, and inflammation. In the context of IPF, increased PDE1A activity may lead to decreased cAMP and cGMP levels, thus promoting fibroblast activation and fibrosis [6,7]. In this study, we used a comprehensive approach combining pharmacology and bioinformatics [8–10] to investigate the key mechanisms by which pirfenidone treats IPF. Specifically, we focused on the regulation of PDE1A by pirfenidone. By building drug‒target disease networks and performing molecular docking analyses, we aimed to clarify how pirfenidone disrupts the fibrotic process in IPF by targeting PDE1A. The results of this study provide new insights into the mechanism of action of pirfenidone and may provide a theoretical and experimental basis for the personalized treatment of IPF. By further investigating the effect of pirfenidone on PDE1A, we aimed to advance the development of innovative therapeutic strategies for IPF, with the goals of improving patients’ quality of life and improving their prognosis.

Materials and methods

Identification and analysis of differentially expressed genes in IPF

GeneCards database (https://www.genecards.org/) was used to retrieve the disease-related genes by using the keyword “IPF” and 6,896 IPF targets were identified. Additionally, the datasets GSE10667, GSE110147, and GSE226249 from the NCBI GEO database (http://www.ncbi.nlm.nih.gov/geo/) was also retrieved. The following sample groups were used in this study: for the GSE10667 dataset, the normal samples included GSM269749--GSM269763, while the IPF model samples included GSM373881--GSM373888. For the GSE110147 dataset, the normal samples were GSM29787889-GSM2978799, and the IPF model samples were GSM2978752-GSM2978773. For the GSE226249 dataset, the normal samples were GSM7068937-GSM7068939, the IPF samples were GSM7068946-GSM7068948 and GSM7068955-GSM7068957, and the IPF+ pirfenidone samples included GSM7068949-GSM7068950 and GSM7068958-GSM7068960.

Identification of pirfenidone targets

To identify the potential targets of pirfenidone, the chemical information of the identified targets was retrieved from PubChem website (https://pubchem.ncbi.nlm.nih.gov/) and obtained its SMILES structures. The SMILES string was submitted to the SwissTargetPrediction platform (http://swisstargetprediction.ch/), with “Homo sapiens” selected as the target species. The default probability-based prediction model of SwissTargetPrediction 2024 were used and a total of 101 putative pirfenidone-associated targets were obtained. All the predicted targets were exported directly and corresponding probability scores were recorded to ensure the reproducibility. Total 6896-assoociated genes for the disease-related targets were retrieved from the GeneCards database by using default scoring system.

The overlap between IPF-related genes (6896) and pirfenidone-associated targets (101) was determined by using the Venn diagram. 71 shared targets were identified, which were considered the potential therapeutic targets through which pirfenidone may exert its effect in IPF.

Functional enrichment analysis

Functional enrichment analyses of the identified 71 overlapping targets was performed by utilizing Metascape (https://metascape.org/gp/index.html#/main/step1) for protein-protein interaction (PPI) analyses, pathway enrichment and gene annotation. All 71 DEGs were subjected to Metascape platform and enrichment analysis was performed by using the default KEGG, GO molecular Function (MF), Reactome, GO Cellular Component (CC) and GO Biological processes (BP) annotation systems. Enrichment score > 1.5, minimum overlap ≥ 3 genes and Benjamini-Hochberg correction for multiple testing were set as statistical threshold. Moreover, all human genes were set as background set. A functional enrichment analysis was performed on the differentially expressed genes to identify relevant biological processes and pathways.

The human protein atlas

The expression profile of PDE1A across lung-related cell types was obtained from the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/) using the Tissue Expression and Single Cell Type modules. The corresponding normalized expression matrices were downloaded and used to validate the PDE1A expression in lung fibroblasts.

Molecular docking

The binding interaction between PDE1A and pirfenidone was investigated using AutoDock Vina 1.2.5 employing ssemi-flexible docking, where the ligand (pirfenidone) was treated as flexible and the receptor (PDE1A) as rigid. No experimental PDE1A crystal structure was available, the 3D protein structure was predicted using homology modeling, threading and ab initio. The predicted structures were evaluated and the structure predicted through AlphaFold3 (UniProt ID: P5475) was selected for further analyses. The 3D structure of pirfenidone (PubChem CID: 40632) was downloaded from the PubChem d and energy-minimized using MMFF94 force-field minimization.

The compound was reconstructed manually using StoneMIND Collector, followed by 3D optimization. The active binding pockets of PDE1A were predicted using KVFinder [11], with parameters integrating UniProt catalytic annotations. Molecular docking was performed using a grid box fully covering the predicted catalytic pocket. Least binding energy, clustering consistency and highest binding affinity were set for pose selection. The docked complexes were visualized and analyzed using Discovery Studio Visualizer.

Molecular dynamics simulations

Post docking analyses were performed using molecular dynamic (MD) simulation in GROMACS. The top-ranked selected compound was simulated for 200 nanoseconds (ns). Optimization and minimization of the receptor-ligand complex were carried out. The system was prepared with the Transferable Intermolecular Interaction Potential 3 Points (TIP3P) solvent model in an orthorhombic box. CHARMM36 force field was applied. Counter-ions were added to neutralize the model. The simulation was performed under an NPT ensemble at 303 K temperature and 1 atm pressure. The system was relaxed prior to MD simulation. Trajectories were recorded every 100 ps, and the stability was verified by analyzing the root mean square deviation (RMSD) of both protein and ligand over time.

MicroScale Thermophoresis (MST) assay

To experimentally validate PDE1A–pirfenidone binding, MicroScale Thermophoresis (MST) analysis was performed using PDE1A (Cat: P89-30H, SinoBiological, China) and pirfenidone1A (Cat: S2907, Selleck, USA).

Protein labeling.

A total of 100 nM RED-tris-NTA dye was prepared by mixing 2 µL of dye with 98 µL of PBS-T, followed by the addition of 100 µL of 200 nM PDE1A protein. The mixture was incubated at room temperature for 30 minutes in the dark. After incubation, centrifugation was performed at 15,000 × g for 10 minutes at 4 °C, and the supernatant was collected for analysis.

Fluorescence intensity detection.

Following labeling, 200 μL of protein solution was obtained. A 10 μL aliquot was diluted threefold with assay buffer. The sample was transferred into capillaries and analyzed using the MST instrument tray. Instrument settings were configured as follows: excitation power set to Auto, MST power to Medium, and appropriate capillary type selected.

Binding affinity measurement.

Binding affinity was quantified by preparing a two-fold dilution series of pirfenidone in assay buffer. Starting with the highest concentration, 20 μL was placed in tube 1, followed by serial 10 μL transfers into tubes 2–16, each containing 10 μL buffer. 10 μL of labeled protein was added to each tube, mixed, incubated for 15 minutes at room temperature and loaded into capillaries. Measurement was acquired using the MST software, with three biological replicates to ensure reproducibility.

Validation of PDE1A expression in the GSE10667, GSE110147, and GSE226249 datasets

PDE1A expression was validated using the GSE10667, GSE110147 and GSE226249 datasets. Raw data were processed. ROC curve analysis (GraphPad Prism 8.0) was performed for PDE1A in GSE10667 and GSE110147 to evaluate diagonostic performance. Different expression within GSE226249 further validated PDE1A expression pattern.

Statistical analysis

Quantitative data were expressed as means ± standard deviations (SD) after confirming normality. For normally distributed data, group comparisons were made using one-way ANOVA, followed by Tukey’s post hoc test. GraphPad Prism 8.0 was used for all the statistical analyses, with P < 0.05 considered statistically significant.

Results

Identification of potential targets of pirfenidone in IPF

The research workflow presented in Fig 1 outlines the identification of key targets for pirfenidone in the treatment of IPF. A total of 6,867 IPF-related genes were identified through the GeneCards database. Simultaneously, 101 potential drug targets for pirfenidone were identified from the Swiss TargetPrediction website. Overlapping these datasets through a Venn diagram yielded 71 overlapping genes, which were postulated as potential therapeutic targets for IPF (Fig 2A-2B). Functional enrichment analysis of these 71 potential target genes, performed through Metascape, revealed their involvement in several biological processes, including cyclic nucleotide-mediated signaling pathways and cAMP-mediated signaling pathways. In addition, KEGG pathway analysis revealed significant associations with calcium signaling, taste transduction, morphine dependence, renin secretion, and purine metabolism (Fig 2C).

Fig 2 — A: Molecular structure of pirfenidone; B: Vento screened the potential genes of pirfenidone for IPF treatment; C: Metascape enrichment analysis of potential genes of pirfenidone for IPF treatment.

Screening of differential genes and core target identification

Differentially expressed genes associated with IPF were identified through the GSE10667 and GSE110147 datasets together with IPF-related genes from the GeneCards database. When the drug targets of pirfenidone from the Swiss TargetPrediction website were combined, PDE1A emerged as a major target in the treatment of IPF (Fig 3A-3E).

Fig 3 — A: GSE10667 data set box diagram; B: GSE10667 data set volcano map; C: GSE110147 data set box diagram; D; GSE110147 data set volcano map; E Vento screening the core targets of pirfenidone treatment for IPF.

Validation of the core target gene PDE1A in IPF

PDE1A expression was confirmed in several datasets. In the GSE10667 dataset, PDE1A expression was significantly greater in the IPF group than in the control group (Fig 4A). ROC curve analysis further confirmed the diagnostic power of PDE1A, with high AUC values supporting its efficacy in identifying IPF (Fig 4B). Similarly, the GSE110147 dataset revealed increased PDE1A expression in IPF samples, and the ROC curve reflected strong diagnostic performance (Fig 4C-4D). PDE1A is highly expressed in fibroblasts, smooth muscle cells and endothelial cells in lung tissue and is critical for the pathophysiology of pulmonary fibrosis (Fig 4E).

Fig 4 — A: The expression level of PDE1A in GSE10667 dataset; B: ROC curve of PDE1A in GSE10667 data set; C: Expression level of PDE1A in GSE110147 dataset; D: ROC curve of PDE1A in GSE110147 data set. E: The expression of PDE1A in different cells in the HAP database.

Interaction analysis of Pirfenidone and PDE1A: MD and MST

The binding patterns of PDE1A (Fig 5A) and pirfenidone (Fig 3B) were predicted through molecular docking (Fig 5C). The docking results indicated that the binding energy between PDE1A and pirfenidone was −6.4 kcal/mol (Table 1), and they formed extensive interactions. Specifically, the side chains of amino acids H219 and H263 of PDE1A formed hydrogen bonds with pirfenidone. In addition, ligands also have π-π stacking and π- cation interactions with H219 residues. These bonding and non-bonding interactions work in synergy, enhancing the binding stability of the complex (Fig 5D).

Table 1. Molecular docking analyses along with binding affinity.

Combination mode	Binding affinity (kcal/mo1)	RMSD (1ower bound)	RMSD (upper bound)
1	−6.4	0	0
2	−6.3	3.025	3.737
3	−6.3	2.388	5.651
4	−6.3	2.444	5.564
5	−6.3	2.818	4.441
6	−6.3	3.439	4.844
7	−6.2	2.063	5.665
8	−6.1	1.781	2.105
9	−6.0	2.100	3.173

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In this study, we conducted a detailed analysis of the interaction between Pirfenidone and PDE1A through Microscale Thermophoresis (MST) technology. The experimental conditions were as follows: Ligand Concentration: 1000 µM to 0.0305 µM; Excitation Power: 100%; MST Power: 40%; Temperature: 25.0°C; Kd: 1.5097E-05; Kd Confidence: ± 2.5858E-06; Signal to Noise: 19.171163. The MST results showed that a signal-to-noise ratio greater than 5 is acceptable, while valued above 12 indicates high-quality data. The protein mobility slowed with increasing analyte concentration. The fitting curve was S-shaped, exhibiting clear upper and lower plateau phases and strong concentration dependence; indicated specific binding (Fig 5E-5F). The dissociation constant (Kd = 15.1 μM) indicated relatively strong binding affinity in vitro.

Using Discovery Studio Visualizer, hydrogen bond interactions were identified between His219 and His263 of PDE1A and the small molecule, while His219 also formed Pi-Pi and Pi-cation interactions with ligand. Van der Waals forces contributed by surrounding amino acids stabilized the ligand within the binding pocket (Fig 6A). The first frame of the dynamic simulation trajectory was used as a reference to calculate the root mean square deviation (RMSD) of Cα atoms. In the initial stage (0–20 ns), the RMSD of both systems increased rapidly, indicating structural adjustment towards equilibrium. Subsequently, the RMSD fluctuation range of the complex stabilized around 0.6–0.8 nm, showing a stable trend, with minor fluctuations. The protein monomer exhibited slightly wider RMSD fluctuation, approximately 0.6–1.0 nm, and an upward trend in the later stage of the simulation (160–200 ns).

Overall, the RMSD of the complex stabilized earlier and fluctuated less than that of the protein monomer, indicating stable binding (Fig 6B). According to the RMSF analysis, amino acids at positions 260–269, 330–333, 366–367, and 384–388 showed fluctuations particularly in the loop region near the binding pocket. Throughout the 200 ns molecular dynamic simulation, the ligand remained stably positioned near the binding pocket indicating a stable complex (Fig 6C). Higher flexibility in several regions were observed through residue-wise RMSF analysis. Moreover, particularly 260–269, 330–333, 366–367, and 384–388 residues showed predominantly correspond to hinge and loop regions connecting secondary structure elements. These observed residues are structurally significant due to their role in conformational adaptability and also in the proximity of ligand-binding site. A notable reduction in RMSF values observed for the particular regions upon ligand binding through comparative analysis between pirfenidone-bound systems and the apo form. Reduced fluctuations in particular key regions revealed that the pirfenidone binding restricts the local protein dynamics leads to contribute to enhance the structural stability of PDE1A. The averaged values of RMSF were calculated for these regions and lower fluctuations in the ligand-bound system compared to the apo form was observed, with reduced flexibility upon pirfenidone binding.

The radius of gyration (Rg) of the protein-ligand complex was lower than that of the monomer, suggesting increased compactness due to ligand binding (Fig 6D). A reduction in SASA further indicated that ligand binding buried exposed regions of the protein surface, emphasizing hydrophobic interactions in stabilizing the complex (Fig 6E).

Hydrogen bond analysis showed that initial binding was suppoted by hydrogen bonds, which decreased significantly after 100 ns. This decrease may reflect ligand rearrangements, conformational shifts, or change in hydrogen bond geometry. A reduction in close contacts (pairs within 0.35 nm) supported this observation (Fig 6F). The free energy morphology diagram indicated that the blue region corresponding to the lowest free energy state of the complex representing the the most stable conformation during the entire simulation (Fig 6G-6I).

Using gmx_MMPBSA, the average binding free energy of the complex was −14.67 kcal/mol (Table 2) supporting favourable binding.

Table 2. Protein binding free energy with ligand (MM/PBSA analysis).

Energy component	Average
ΔVDWAALS	−25.62 Kcal/mol
ΔEEL	−13.92 Kcal/mol
ΔEPB	27.71 Kcal/mol
ΔENPOLAR	−2.84 Kcal/mol
ΔGGAS	−39.54 Kcal/mol
ΔGSOLV	24.87 Kcal/mol
ΔTOTAL	−14.67 Kcal/mol

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PDE1A expression in lung tissue and the impact of pirfenidone treatment

Validation through the GSE226249 dataset further confirmed the role of pirfenidone in modulating PDE1A expression (Fig 7A, 7C). Volcano plots comparing the normal vs. IPF groups and the IPF vs. IPF+ pirfenidone groups revealed significant changes in gene expression, highlighting the impact of pirfenidone on PDE1A regulation (Fig 7B, 7D). Comparative analysis demonstrated a significant increase in PDE1A expression in the IPF group compared with the normal group (Fig 7E). However, pirfenidone treatment resulted in a marked reduction in PDE1A levels, suggesting a therapeutic mechanism by which pirfenidone alleviates pulmonary fibrosis by downregulating PDE1A (Fig 7E). Taken together, these results suggest that pirfenidone may exert its therapeutic effect on IPF by specifically reducing PDE1A expression.

Discussion

In this study, we used a comprehensive bioinformatics and experimental approach to investigate the molecular mechanisms by which pirfenidone exerts its therapeutic effect in IPF. Pirfenidone is an antifibrotic drug approved for the treatment of IPF, but its precise molecular targets are still not fully understood. By integrating diverse data sources and validation strategies, we sought to identify key targets and molecular interactions relevant to pirfenidone’s mechanism of action. Using the GeneCards database, we identified 6,867 genes associated with IPF, reflecting the complex and multifactorial nature of the disease. At the same time, 101 potential pirfenidone targets were predicted using the SwissTargetPrediction tool. Cross-referencing these datasets revealed 71 overlapping genes, which we propose as important candidates for pirfenidone’s therapeutic activity.

To investigate the functional relevance of these targets, we performed enrichment analysis using the Metascape platform. Overlapping genes were significantly enriched in biological processes including cyclic nucleotide and cAMP-mediated signaling pathways. Both play pivotal roles in regulating cell proliferation, differentiation, and fibrotic responses. These results are consistent with previous studies linking dysregulated cAMP signaling to the pathophysiology of fibrotic lung diseases [5]. Furthermore, KEGG pathway analysis revealed enrichment in calcium signaling, taste transduction, morphine dependence, purine metabolism, and renin secretion; some of these processes are associated with tissue remodeling and fibrosis [12].

To further refine our candidate list, we analyzed two publicly available microarray datasets (GSE10667 and GSE110147) for differentially expressed genes (DEGs) in IPF patients. Based on the overlap of the DEGs with IPF-associated genes and pirfenidone targets, we identified PDE1A as a hub gene of interest. PDE1A was significantly overexpressed in IPF lung tissue, and ROC analysis showed an area under the curve (AUC) greater than 0.8, suggesting high diagnostic potential. These results are in agreement with previous studies reporting increased PDE1A expression in fibrotic diseases, where it was linked to cyclic nucleotide degradation and the promotion of profibrotic signaling cascades [13].

Cell-specific expression analyses using the Human Protein Atlas (HPA) showed that PDE1A is predominantly expressed in lung fibroblasts, smooth muscle cells, and endothelial cells, the main cellular players in IPF. Fibroblasts, upon activation into myofibroblasts, produce excess extracellular matrix and thus directly contribute to the formation of fibrotic tissue. This conversion is known to be regulated by cytokines such as transforming growth factor-β (TGF-β) and platelet-derived growth factor (PDGF), both of which have previously been linked to PDE activity [14–16]. Endothelial cells, in turn, contribute to inflammation, angiogenesis, and the recruitment of immune cells, thus exacerbating fibrotic remodeling [17–20]. Although smooth muscle cells are not critical for the development of fibrosis, they contribute to vascular remodeling and can indirectly worsen tissue stiffness and vascular resistance [21].

To investigate the interaction between pirfenidone and PDE1A, we performed molecular docking using AutoDock Vina. The binding energy of −6.4 kcal/mol indicated a favorable interaction. Pirfenidone forms hydrogen bonds with amino acid residues H219 and H263 of PDE1A and, through H219, also participates in π-cation and π-π stacking interactions. These non-covalent interactions suggest a specific and stable binding mode. Our results are supported by previous studies, such as Yang et al. (2023), which highlighted the role of PDE inhibitors in suppressing fibrotic signaling pathways in lung fibroblasts [22], although pirfenidone has not been previously validated as a specific PDE1A binder.

To validate these in silico results, we used microthermophoresis (MST) to experimentally determine the binding affinity between pirfenidone and PDE1A. The MST experiment yielded a signal-to-noise ratio (SNR) of 19.17, indicating high data quality. The resulting binding curve displayed a characteristic S-shape, confirming specific and saturable binding. The dissociation constant (K_D) calculated to be 15.1 μM, indicating moderate to strong affinity under physiological conditions. These results are consistent with docking predictions and support the proposed interaction. Furthermore, analysis of the GSE226249 dataset showed that pirfenidone treatment was associated with a reduction in PDE1A expression in IPF models. This suggests that the compound may exert its antifibrotic effects partly through PDE1A downregulation. This is consistent with previous pharmacological studies showing that pirfenidone inhibits TGF-β signaling and modulates downstream fibrotic pathways [23,24].

Nevertheless, this study efficiently uncovers potential binding sites and key amino acid residues. However, we acknowledge that this computational approach has certain limitations. First, AutoDock treats proteins primarily as rigid bodies during molecular docking, allowing only the flexible rotational degrees of freedom of ligands. This limitation can make it difficult to accurately capture true binding conformations, especially for protein targets with obvious conformation-inducing effects. Furthermore, the AutoDock scoring function is primarily based on empirical energy terms, including simplified energy calculation models such as van der Waals forces, electrostatic forces, hydrogen bonding, and hydrophobic interactions. While these energy terms are valuable for rapidly screening large numbers of ligands, the scoring function cannot comprehensively account for complex factors such as the effect of solvent on proteins and ligands in aqueous environments, conformational entropy changes, and adjustments in protein flexibility. Therefore, the predicted binding free energy often contains significant errors and does not accurately reflect the actual binding affinity. Using molecular docking technology, we computationally identified potential interactions between PDE1A and pirfenidone. We subsequently performed an MST experiment and determined a K_D value of 15.1 μM for the interaction between PDE1A and pirfenidone. This value was in agreement with the molecular docking results and confirmed the interaction. Further studies, including mutagenesis, structural biology, and in vivo validation, are required to confirm the therapeutic relevance of PDE1A inhibition by pirfenidone.

Conclusion

These findings not only provide a solid experimental basis for the application of pirfenidone in the treatment of IPF but also provide new clues for understanding its mechanism of action. Despite the progress made in this study, there are several limitations and shortcomings: the current study focused mainly on in vitro and computational analyses, and there is a lack of sufficient in vivo experimental data to directly prove the actual effect of pirfenidone through PDE1A in IPF treatment. Although this study revealed that PDE1A may be a target of pirfenidone, the detailed mechanism by which pirfenidone affects downstream signaling pathways and cell function through PDE1A has not been fully elucidated. Overall, these findings not only deepen our understanding of the mechanism of action of pirfenidone but also open new avenues for precision medicine in IPF. Future studies are needed to validate our findings through additional clinical trials and further explore the optimal application strategy of pirfenidone in the treatment of IPF.

Data Availability

All trajectory data and the code used to generate the figures and results presented in this study are publicly available in the Zenodo repository at https://doi.org/10.5281/zenodo.18387883.

Funding Statement

This work was supported by the Natural Science Foundation of Fujian Province of China (Project Number : 2022J011409, 2023J011653). This work was also supported by the Xiamen Medical College Interdisciplinary Innovation Group Project in China (Project Number: K2023-07). Xiamen Science and Technology Bureau Medical and Health Guidance Project (Project Number: 3502Z20199136). Fujian Province Joint Fund for Scientific and Technological Innovation Project (2024Y9728). The above-mentioned grants were jointly received by the following authors: Jing Wu, Yanyan Ke, and Xue Yi.

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PLoS One. doi: 10.1371/journal.pone.0342991.r001

Decision Letter 0

Mahbub Hasan

12 May 2025

Dear Dr. Ullah,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: No

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available??>

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Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: No

Reviewer #2: No

**********

Reviewer #1: After careful consideration of the manuscript titled "Integrated Network Pharmacology and Bioinformatics to identify a novel strategy for Pirfenidone targeting phosphodiesterase 1A in the Treatment of Idiopathic Pulmonary Fibrosis," I regret to inform that the work does not meet the scientific rigor required for publication in PLOS ONE.

I find the following Major Concerns

1.The In silico analysis presented in this manuscript falls short of established standards. While computational approaches are valuable, they require proper validation and detailed methodology to support conclusions.

2.The authors state they downloaded structures of seven differentially expressed genes from the RCSB Protein Data Bank but fail to provide the specific PDB IDs. This omission prevents proper assessment and reproducibility of the work.

3.The manuscript claims that a docking binding energy of less than −5 kcal/mol indicates strong binding affinity. This threshold is an oversimplification of molecular docking. Proper docking studies typically include comprehensive analysis of binding poses and conformations. The assertion that -5 kcal/mol represents strong binding is arbitrary without contextual validation.

4.No positive control was included in the docking experiments. For reliable docking protocols, re-docking of co-crystal ligands (whenever possible) with RMSD calculations below 2Å is typically used to validate methodology.

5.The manuscript lacks critical details regarding amino acid residues involved in the interaction with the ligand and the forms of interactions. A thorough docking analysis should include both bonding and non-bonding interactions and an examination of key contact points between the ligand and receptor.

6.The authors should perform in vitro validation of the binding or conduct molecular dynamics simulations of at least 200ns to assess the stability of the protein-ligand complex over time.

7.Discuss the limitations of computational approaches. The study relies on limited computational evidence without sufficient validation or detailed analysis.

Reviewer #2: Molecular docking was not done correctly!

1. structures of seven differentially expressed genes were obtained from the RCSB Protein Data Bank. Please mention the PDB ids) (PDB, https://www.rcsb.org/) and99

saved in PDB format.

2. CB-Dock2 conducts blind docking. Why not active site specific docking?

3. Before docking the co-crystal ligand should be redocked and rmsd with diagram should be mentioned in the supplementary material.

4. There are some differances between freeing binding energy and binding affinity. Please correct as because at the beginning "Molecular Docking" binding affinity was mentioned!

5. How docking binding energy of less than −5 kcal/mol was considered indicative of strong binding affinity?? is there any control drug used or how this threshold was decided>

6. I suggest to conduct mm-PBSA as because docking based mm-GBSA is not accurate to calculate free binding energy!

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2026 Apr 10;21(4):e0342991. doi: 10.1371/journal.pone.0342991.r002

Author response to Decision Letter 1

3 Jul 2025

Dear Editor,

We are extremely grateful to the editors and reviewers for the opportunity they have given us to revise the manuscript. We believe that through these supplements and revisions, our research can better meet the publication standards of PLOS ONE. We look forward to your further feedback and hope that the revised manuscript can be accepted for publication.

Response to Reviewer’s Comment:

Reviewer #1: Changes made according to reviewer 1 are highlighted in blue in the article.

1. The In-silico analysis presented in this manuscript falls short of established standards. While computational approaches are valuable, they require proper validation and detailed methodology to support conclusions.

Reply: Thank you for your valuable comments on our manuscript. We attach great importance to the issue you pointed out regarding the need for more rigorous validation and detailed methodological support for In silico analysis. We are fully aware of the deficiencies in the methodological description and result verification of the computer simulation part in the original manuscript. For this reason, in the revised draft:

(1) Clarify the technical details of molecular docking, including the acquisition of protein structures, determination of binding sites, and other technical details.

(2) Add experimental verification (MST). To enhance the reliability of the calculation results, we have newly added the micro thermal Migration (MST) experiment to verify the binding relationship between PDE1A and candidate compounds. The experimental results are consistent with the computational predictions, supporting the conclusion of molecular docking at the experimental level.

(3) In the molecular docking results, the analysis of the interaction between proteins and compounds has been added and the interaction has been visualized.

These results suggest that Pirfenidone may exert its biological effects by binding specifically to PDE1A. We have described in detail in the text the parameter Settings of molecular docking, the scoring system and the analysis of binding sites to ensure the transparency and repeatability of this part of the research. The modifications in the article are as follows: Interaction analysis of Pirfenidone and PDE1A: MD and MST

The binding patterns of PDE1A (Fig. 5A) and pirfenidone (Fig. 3B) were predicted through molecular docking (Fig. 5C). The docking results indicated that the binding energy between PDE1A and pirfenidone was -6.4 kcal/mol (Table 1), and they formed extensive interactions. Specifically, the side chains of amino acids H219 and H263 of PDE1A formed hydrogen bonds with pirfenidone. In addition, ligands also have π-π stacking and π- cation interactions with H219 residues. These bonding and non-bonding interactions work in synergy, enhancing the binding stability of the complex (Fig. 5D). In this study, we conducted a detailed analysis of the interaction between Pirfenidone and PDE1A through Microscale Thermophoresis (MST) technology. The experimental conditions are as follows: Ligand Concentration: 1000 µM to 0.0305 µM; Excitation Power: 100%; MST Power: 40%; Temperature: 25.0°C; Kd: 1.5097E-05; Kd Confidence: ± 2.5858E-06; Signal to Noise: 19.171163. The MST results show that a signal-to-noise ratio greater than 5 indicates a cutoff, while a ratio greater than 12 indicates high-quality data. Moreover, the protein mobility slows down with increasing analyte concentration. The fitting curve is S-shaped, exhibiting clear upper and lower plateau phases and strong concentration dependence; this indicates specific binding (Fig. 5E-F). The dissociation constant (Kd) is 15.1 μM, indicating relatively strong binding affinity in the in vitro molecular interaction between the protein and small molecules.

Tanble 1 Molecular docking scoring item

Combination mode Affinity (kcal/mo1) RMSD(1.b.) RMSD(u.b.)

1 -6.4 0 0

2 -6.3 3.025 3.737

3 -6.3 2.388 5.651

4 -6.3 2.444 5.564

5 -6.3 2.818 4.441

6 -6.3 3.439 4.844

7 -6.2 2.063 5.665

8 -6.1 1.781 2.105

9 -6.0 2.100 3.173

Fig. 5 The experimental results of MD and MST. A: Structural diagram of protein PDE1A; B: Pirfenidone structure diagram; C: The docking structure diagram of Pirfenidone and PDE1A; D: 3D diagram of the interaction between Pirfenidone and PDE1A (affinity -6.4 kcal/mol), The green rod-like structure is a small molecule; The brown rod-like structure is the residue of the protein structure, Yellow dotted line: Hydrogen bond Pink dotted line: π-πinteraction; Orange dotted line: π-cation; E: Each curve in the figure is a real-time recording curve of fluorescence intensity in a capillary tube, which records the variation of fluorescence intensity over time in the temperature gradient. Purple represents the initial average signal F0, red represents the average signal F1 of the time period selected for the fitting graph, and the homogenized fluorescence signal Fnorm is the thousandth ratio of F1/F0. As the heating begins, the fluorescently labeled protein surges towards the surrounding low-temperature area. The density of the fluorescent protein per unit space decreases, so the Fnorm value will decrease. After the heating ends, the temperature recovery gradient disappears and the Fnorm value rebounds; F: Fnorm fitting graph.

The methods in the article have been re-supplemented:

Molecular Docking (MD)

Macromolecular docking is one of the important methods of molecular simulation. It is a type of computer simulation. Its essence is the recognition process between two or more molecules. This process involves shape and spatial complementarity as well as energy compatibility between molecules. In the field of molecular modeling, if two molecules can form a stable complex, molecular docking can predict their binding mode. Furthermore, the binding strength can be evaluated using a scoring function based on this mode.

The software adopted this time is AutoDock Vina. The algorithm of AutoDock Vina is the Monte Carlo search algorithm based on local perturbation. The method of ligand docking with PDE1A (phosphodiesterase 1A, an enzyme involved in cellular signaling) is semi-flexible docking, that is, the ligand is flexible (the rotatable bond angle can rotate freely), while the PDE1A protein is rigid (remains unchanged). The 3D structure file of the receptor protein was downloaded from the PDB, and the protein structure was completed and optimized. If there is no crystal structure of the experimental data, AlphaFold3 is used to predict the 3D structure of the receptor protein (PDE1A: UniProt ID P5475). Download the 3D structure file of the small molecule compound from the PubChem database and perform structure optimization on the compound (Pirfenidone: PubChem ID 40632). If the structure file is unavailable, the StoneMIND Collector is used to draw the compound, generate the 3D structure, and then perform energy minimization. The active pockets of proteins are determined by integrating information provided by users, annotations from the UniProt database, and AI predictions. This project uses KVFinder[21] to predict the active pockets of proteins, determines the active sites of the proteins, and performs molecular docking using AutoDock Vina. After processing and optimizing the docking data, the results were analyzed using Discovery Studio Visualizer.

MicroScale Thermophoresis (MST)

MicroScale Thermophoresis (MST) is a powerful technique for quantifying interactions between biomolecules. MST combines precise fluorescence detection with sensitive and flexible thermophoresis to provide a powerful and rapid method for measuring intermolecular interactions. Specifically, during the MST experiment, the sample is heated by an infrared laser to generate a microscopic temperature gradient. Then, the directional movement of molecules is monitored and quantified through covalently bound fluorescent dyes or the autofluorescence of tryptophan. The application scope includes interactions involving small molecules, proteins, and protein complexes.

(1) Protein labeling (NTA dye)

Take a new clean 1.5 mL centrifuge tube, add 2 µL of the newly prepared RED-tris-NTA second-generation dye and 98 µL of PBS-T, and gently pipette and mix evenly with a pipette to obtain a dye solution with a final concentration of 100 nM. Then add 100 µL of 200nM protein sample. After wrapping the centrifuge tubes with tin foil, incubate them at room temperature in the dark for 30 minutes in the dark.

After the protein labeling step is completed, centrifuge at 4 ° C and 15,000 g for 10 minutes, and take the supernatant into a new centrifuge tube.

(2) Protein labeling fluorescence intensity detection (Pretest)

After fluorescence labeling, 200 μL of protein was obtained. 10μL was taken out and diluted 3 times with Assay Buffer. Open the sample chamber, take out the tray, draw the diluted protein sample with a capillary tube and place it in the corresponding slot of tray 1.2. Input protein sample information and Assay Buffer information; Select the capillary type; Excitation Power: Select Auto; MST-Power selects Medium.

(3) Binding Affinity determination

Dilute the analyte to twice the maximum final loading concentration of 100μL using the Assay Buffer. Take two eight-row samples, make labels 1-16, and add 10μL of Assay Buffer to tubes 2-16 respectively; Add 20μL of the prepared analyte to tube 1, and use a pipette to draw 10 μL. Start the 2-fold gradient dilution successively in tubes 2 to 16. Then, starting with a pipette, add 10μL of protein to each tube and pipette to mix well. After the mixed sample is incubated at room temperature for 15 minutes, it is aspirated with a capillary tube and loaded onto the sample tray. Then, click "Start" on the software to begin the experiment.

Data analysis was conducted after repeating the steps of dilution - protein mixing - sample loading for a total of three times.

2. The authors state they downloaded structures of seven differentially expressed genes from the RCSB Protein Data Bank but fail to provide the specific PDB IDs. This omission prevents proper assessment and reproducibility of the work.

Reply: Thank you very much for your attention and valuable suggestions to our manuscript. When we were writing the method section, the information was incorrect due to a typo. This is entirely due to our negligence. Thank you for pointing out this problem. We have made modifications in the method section and supplemented the specific PDB IDs. The following is the revised description: The software adopted this time is AutoDock Vina. The algorithm of AutoDock Vina is the Monte Carlo search algorithm based on local perturbation. The method of ligand docking with PDE1A (phosphodiesterase 1A, an enzyme involved in cellular signaling) is semi-flexible docking, that is, the ligand is flexible (the rotatable bond angle can rotate freely), while the PDE1A protein is rigid (remains unchanged). The 3D structure file of the receptor protein was downloaded from the PDB, and the protein structure was completed and optimized. If there is no crystal structure of the experimental data, AlphaFold3 is used to predict the 3D structure of the receptor protein (PDE1A: UniProt ID P5475). Download the 3D structure file of the small molecule compound from the PubChem database and perform structure optimization on the compound (Pirfenidone: PubChem ID 40632).

3. The manuscript claims that a docking binding energy of less than −5 kcal/mol indicates strong binding affinity. This threshold is an oversimplification of molecular docking. Proper docking studies typically include comprehensive analysis of binding poses and conformations. The assertion that -5 kcal/mol represents strong binding is arbitrary without contextual validation.

Reply: Thank you very much for your attention and valuable suggestions to our manuscript. We have deleted the expression "A binding energy less than -5 kcal/mol is considered strong binding" in the original text and changed it to a more cautious description, such as: "Generally, a binding energy less than -5 kcal/mol can be regarded as having certain binding potential, but the specific binding ability still needs to be verified in combination with other experiments." We supplemented the multi-angle analysis of the docking results, including specific interactions such as hydrogen bonds, hydrophobic interactions, and π-π packing, and combined with visual images to present the binding mode between ligands and targets. The modifications in the article are as follows:

Interaction analysis of Pirfenidone and PDE1A: MD and MST

4. No positive control was included in the docking experiments. For reliable docking protocols, re-docking of co-crystal ligands (whenever possible) with RMSD calculations below 2Å is typically used to validate methodology.

Reply: Thank you very much for your attention and valuable comments on our manuscript. However, due to the lack of co-crystalline data of PDE1A protein, re-docking cannot be carried out. In order to improve the accuracy of the molecular docking method, we have made the following adjustments.

At present, there is no relevant co-crystalline structure data for PDE1A (UniProt number: P54750), nor is there experimental crystal structure information of the protein. Meanwhile, there is no recognized positive control drug yet. Therefore, re-docking verification based on the binding sites of positive compounds cannot be carried out.

2. In this study, AlphaFold3 was used to predict the three-dimensional structure of PDE1A (with a pTM score of 0.75), and KVFinder was utilized to predict potential compound binding sites. Based on this binding site, molecular docking was carried out using AutoDock Vina. Subsequently, the interaction between the protein and the ligand was analyzed in detail with the help of Discovery Studio Client, and the visualization display of the binding mode was achieved by using PyMOL.

5. The manuscript lacks critical details regarding amino acid residues involved in the interaction with the ligand and the forms of interactions. A thorough docking analysis should include both bonding and non-bonding interactions and an examination of key contact points between the ligand and receptor.

Reply: Thank you to the reviewers for your valuable comments. We supplemented the detailed analysis of the molecular docking results and further clarified the key interactions between ligands and receptor proteins. Calculated by AutoDock Vina, the binding free energy of PDE1A to the compound was -6.4 kcal/mol, showing a good binding potential. The specific binding mode analysis indicates that the ligand has formed stable hydrogen bond interactions with the key amino acid residues H219 and H263 of PDE1A. In addition, ligands also have π-π stacking

Attachment

Submitted filename: Authors Response Letter.docx

pone.0342991.s002.docx^{(474.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0342991.r003

Decision Letter 1

Mahbub Hasan

30 Jul 2025

Dear Dr. Ullah,

Please revise the manuscript as reviewer 2's suggestions.-->--> -->-->Please submit your revised manuscript by Sep 12 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Mahbub Hasan, PhD

Academic Editor

PLOS ONE

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: N/A

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: Dear author, Dear author,

The manuscript has been improved, and all my recommendations have been addressed. I recommend the manuscript be accepted in its current form.

Reviewer #2: Revision is not satisfactory. Revision is not satisfactory.

1. Regarding binding energy reviewer 1 also highlighted the same. I suggest that the authors may take 3 months time use good software such as Autodock Vina, SwissDock or any other molecular docking software and perform the docking correctly.

2. As mm-gbsa did not provided good results, question is arising about the authenticity of the data. I suggest to conduct "Consensus molecular docking" using multiple docking software and identify the best pose.

3. Take an extension of 3-4 months and try to conduct 200 to 500 ns Molecular dynamic simulation to understand the Protein-Ligand interaction dynamics.

4. I have a doubt whether the author chosen the right protein target or not! Authors should also think about it and if found true then change it accordingly. With current results it is difficult to agree with the conclusion.

5. Reply to point 5 and 6 is same, whereas the context of the points were different!

**********

what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Reviewer #1: No

Reviewer #2: No

**********

PLoS One. 2026 Apr 10;21(4):e0342991. doi: 10.1371/journal.pone.0342991.r004

Author response to Decision Letter 2

6 Sep 2025

Dear editor,

A proper Response to Reviewer's comments has been provided as attached file.

Reviewer #1

1. Regarding binding energy reviewer 1 also highlighted the same. I suggest that the authors may take 3 months’ time use good software such as Autodock Vina, SwissDock or any other molecular docking software and perform the docking correctly.

Reply: Thank you for your valuable comments on our manuscript.

We used software such as Discovery studio2019, Schrodinger2021, and Swiss-dock to re-perform molecular docking. The connection result is as follows:

Software Binding energy

Autodock vina -6.4 kcal/mol

Discovery studio2019 -8.124 kcal/mol

Schrodinger2021 -4.41349 kcal/mol

Swiss-dock -6.2662 kcal/mol

This is the best conformation given by different software:

Reply: Thank you very much for your attention and valuable suggestions to our manuscript. We have answered this question in Question 1

3. Take an extension of 3-4 months and try to conduct 200 to 500 ns Molecular dynamic simulation to understand the Protein-Ligand interaction dynamics.

Reply: Thank you very much for your attention and valuable suggestions to our manuscript. We conducted a new molecular dynamics simulation. The simulation video is available on PDE1A.mp4 and has been modified as follows in the article:

We conducted the analysis using Discovery Studio Visualizer: Through the analysis of the docking results, hydrogen bond interactions were formed between his219 and HIS263 of the PDE1A protein and the small molecule, while HIS219 and the small molecule formed Pi-Pi and Pi-cation interactions. The van der Waals forces provided by the amino acids around the small molecule jointly stabilized the small molecule in the binding pocket (Figure 6A). The first frame of the dynamic simulation trajectory was selected as a reference to calculate the root mean square deviation (RMSD) of Cα atoms. In the initial stage (0-20 ns), the RMSD of both rapidly increased, indicating that the system had adjusted from the initial conformation to the equilibrium state. Subsequently, the RMSD fluctuation range of the complex was mainly around 0.6-0.8 nm, showing a relatively stable trend, although there were still small fluctuations. The RMSD fluctuation range of the protein monomer is slightly wider, approximately 0.6-1.0 nm, and it shows an upward trend in the later stage of the simulation (160-200 ns). Overall, the RMSD of the complex seems to stabilize earlier than that of the protein monomers, and the fluctuation range is also smaller. The overall fluctuations of the protein monomers and complexes were within the acceptable range, indicating stable binding (Figure 6B). According to the RMSF comparison between wt and complex (amino acids 5A away from the ligand molecule), among the amino acids at positions 260-269, 330-333, 366-367, and 384-388 that constitute the ligand-binding pocket, the loop region composed of 260-269 and 330-333 fluctuates. In the protein-ligand complex, the ligand remained stable near the binding pocket throughout the 200ns kinetic simulation, indicating that the ligand formed a stable complex structure with the protein under these simulation conditions (Figure 6C). In this study, the Rg of the protein-ligand complex was lower than that of the protein monomer, reflecting that the persistent presence of the ligand in the binding pocket enhanced the compactness of the protein through local structural optimization and non-polar interactions (Figure 6D). In this study, the reduction of the complex SASA indicates that ligand binding buried part of the protein surface, reducing solvent accessibility. The stability of the pockets further confirmed that this change was caused by the ligand occupying the exposed area, highlighting the crucial role of hydrophobic interactions in maintaining the stability of the complex (Figure 6E). In this study, the charts show that the initial binding may rely on hydrogen bonds, but the number of hydrogen bonds decreased significantly after 100 ns. The disappearance of hydrogen bonds may be related to the dissociation of ligands at binding sites, conformational changes, or the rearrangement of hydrogen bond donors/acceptors. The reduction of Pairs within 0.35 nm further supports the reduction of close contact between ligands and proteins. The bifurcation of the red line (H-bond) and the blue line (Pairs within 0.35 nm) in the figure indicates that some pairs may still exist but no longer meet the geometric requirements of hydrogen bonds (Figure 6F). As shown in the figure, the free energy morphology diagram was drawn with the RMSD and Rg trajectories at the last 10ns of the 200ns simulation. In the 2D diagram, the blue area indicates the lowest free energy point of protein-ligand binding, meaning that the protein-ligand complex at this site is the most stable conformation with the lowest free energy point throughout the simulation process (Figure 6G-I).

Fig. 6 The experimental results of MD and MST. A: Proteins dock with small molecules; B: RMSD result graph; C: RMSF Fluctuation analysis; D: Circumferential radius result; E: Solvent accessible surface area; F: Number of hydrogen bonds

; G-I: Free energy topography.

In this study, gmx_MMPBSA was used to calculate the free energy with Gromacs trajectories and topological files. The total binding energy of each complex was contributed by different components, including VDWAALS, EEL, EPB, ENPOLAR, GGAS and GSOLV. According to the results of RMSD, The trajectory of the last 10ns period after ligand stabilization was selected for MM/PBSA analysis. From the calculation results, it can be known that the average binding free energy of the two is -14.67 kcal/mol (Table 2).

Table 2 Protein binding free energy with small molecules (MM/PBSA analysis)

Energy Component Average

ΔVDWAALS -25.62

ΔEEL -13.92

ΔEPB 27.71

ΔENPOLAR -2.84

ΔGGAS -39.54

ΔGSOLV 24.87

ΔTOTAL -14.67

Corrections are added in page14, line 252.

Reply: Thank you very much for your attention and valuable comments on our manuscript. We have re-conducted the molecular dynamics experiment, and the results have been replied in question 3.

5. Reply to point 5 and 6 is same, whereas the context of the points were different!

Reply: Thank you to the reviewers for your valuable comments. We conducted molecular dynamics simulations of at least 200 nanoseconds to evaluate the temporal stability of protein-ligand complexes. The simulation results can be seen in the video file PDE1A.mp4.

Attachment

Submitted filename: Authors Response Letter- PONE-D-24-60633R2.docx

pone.0342991.s003.docx^{(575.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0342991.r005

Decision Letter 2

Mahbub Hasan

9 Oct 2025

Dear Dr. Ullah,

Please submit your revised manuscript by Nov 23 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
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We look forward to receiving your revised manuscript.

Kind regards,

Mahbub Hasan, PhD

Academic Editor

PLOS ONE

Journal Requirements:

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: No

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: No

Reviewer #3: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

Reviewer #2: No

Reviewer #3: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: No

Reviewer #3: Yes

**********

Reviewer #2: 1. MD Simulation procedure is not correct. Discovery Studio Visualizer is just a platform, NAMD available in Discovery Studio Visualizer can be used for MD Simulation.

2. Fig 6 F suggest after 140 ns there are no hydrogen bond interaction between Protein and ligand, which means the ligand is coming out from the binding pocket of the protein. This result undermine the conclusion.

3. Line 111 Molecular Docking (MD) is not correct. MD always refers to Molecular Dynamics Simulation.

4. In reply authors mentioned about Swiss-dock, but in whole paper authors did not mentioned anything about Swiss-dock or other software!

Reviewer #3: (No Response)

**********

what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Reviewer #2: No

Reviewer #3: No

**********

PLoS One. 2026 Apr 10;21(4):e0342991. doi: 10.1371/journal.pone.0342991.r006

Author response to Decision Letter 3

18 Dec 2025

We sincerely thank the reviewer for the constructive and detailed comments. All observations have significantly improved the quality, clarity, and reproducibility of our manuscript. We have carefully revised the manuscript as per suggestion and tried our best to incorporate all recommended changes.

Reviewer Comment:

The study is relevant but lacks reproducibility and methodological rigor. Additional methodological detail, MD validation, improved references, identifiers, and data/code availability are required.

Response: We have now substantially expanded the methodological descriptions, added detailed molecular dynamics (MD) analyses, corrected references, and included explicit dataset identifiers. All the generated data is available in the public repositories and the accession numbers are mentioned in the manuscript. These modifications have been fully incorporated into the revised manuscript.

Reviewer Comment: Provide step-by-step methodological detail.

Response: A comprehensive workflow description has been added, covering gene identification, target prediction, enrichment analysis, docking procedures, and validation steps. This has been incorporated throughout the Methods section.

DEG criteria

We have now specified thresholds, adjusted p-values, and the Benjamini–Hochberg method for multiple testing. This information is added.

GEO processing

Platform IDs and normalization approach covariates have been described in detail. Implemented in Methods.

Tools/versions

Exact software, versions, and packages (where applicable) used for analysis and data processing have been added to ensure reproducibility. Incorporated in Methods.

Justification for PDE1A selection

We expanded the explanation for prioritizing PDE1A among the seven DEGs based on functional importance, enrichment relevance, and reported links with fibrosis. Added where applicable.

Pose validation

Pose clustering method, key residues, and selection criteria have been added. Incorporated at appropriate position in the manuscript.

Functional Enrichment

We now specify ontology categories (GO BP/CC/MF), KEGG and Reactome usage, significance cutoffs, background gene set, and multiple-testing corrections. Included in manuscript.

Statistics

Sample sizes, test types, normality checks, and two-sided assumptions are now clearly stated. Incorporated in manuscript.

Reviewer Comment: Some references were not aligned and formatting needed correction.

Response: All references have been rechecked and reformatted.

Reviewer Comment: Docking alone is insufficient; MD simulations must be performed and reported.

Response: We fully agree. We have now performed molecular dynamics simulations using GROMACS and Ligand RMSD, Protein RMSF, Hydrogen-bond and hydrophobic interaction occupancies, Water-bridge analysis, MM/PBSA binding energies (mean ± SD) and other necessary analyses have been performed. All MD methodology and results have been integrated into the manuscript, including new figures and tables.

Regulatory approval

We have now specified the agency and year of pirfenidone approval (FDA 2014; EMA 2011) with primary citations.

MD integration

The MD findings are now integrated at appropriate positions in the manuscript.

We have added PDB IDs and UniProt IDs for PDE1A, GEO dataset IDs with GSM groupings, platform IDs, and Links of the utilized public repositories.

All identifiers are now clearly listed in the manuscript.

Response: In compliance with the reviewer’s request, we have explained the GEO preprocessing docking protocols with tool names, parameters and versions; analyze MD trajectories and mentioned the results in the manuscript at appropriate positions, topologies, and analysis code. DOI included in manuscript where needed. This ensures full transparency and reproducibility.

All requested analyses have been performed and added, including RMSD, RMSF, MM/PBSA binding energies, and other necessary analyses.

These results are presented in the revised Results.

We are grateful for the reviewer’s detailed and rigorous comments. All recommended revisions have been thoroughly implemented, and the manuscript has been significantly strengthened in terms of methodology, reproducibility, scientific depth, and clarity. We have incorporated each modification into the revised manuscript as requested.

Thank you for your consideration.

Attachment

Submitted filename: Reviewer Response Letter- R3- 10-12-2025.docx

pone.0342991.s004.docx^{(16.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0342991.r007

Decision Letter 3

Mahbub Hasan

13 Jan 2026

Dear Dr. Ullah,

plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Mahbub Hasan, PhD

Academic Editor

PLOS One

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

Reviewer #3: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #3: No

**********

Reviewer #3: analyses are generally satisfactory with the addition of simulation part, though all the mentioned recommendations were not done! However, as a minor point, the RMSF analysis could be further strengthened by a more residue-specific interpretation. In particular, the authors may consider explicitly highlighting and discussing the functional relevance of the residues showing elevated fluctuations (e.g., regions 260–269, 330–333, 366–367, and 384–388) in relation to secondary structure elements and proximity to the binding pocket. Additionally, a brief quantitative comparison of RMSF values between the apo and ligand-bound systems for these regions (e.g., ΔRMSF or averaged RMSF over the binding-site residues) would help clarify whether pirfenidone binding locally stabilizes PDE1A dynamics. These additions would improve clarity without requiring additional simulations.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Reviewer #3: No

**********

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PLoS One. 2026 Apr 10;21(4):e0342991. doi: 10.1371/journal.pone.0342991.r008

Author response to Decision Letter 4

21 Jan 2026

Response to Reviewer Comments

Reviewer Comment:

The study is relevant but lacks reproducibility and methodological rigor. Additional methodological detail, MD validation, improved references, identifiers, and data/code availability are required.

Reviewer Comment: Provide step-by-step methodological detail.

DEG criteria

We have now specified thresholds, adjusted p-values, and the Benjamini–Hochberg method for multiple testing. This information is added.

GEO processing

Platform IDs and normalization approach covariates have been described in detail. Implemented in Methods.

Tools/versions

Exact software, versions, and packages (where applicable) used for analysis and data processing have been added to ensure reproducibility. Incorporated in Methods.

Justification for PDE1A selection

We expanded the explanation for prioritizing PDE1A among the seven DEGs based on functional importance, enrichment relevance, and reported links with fibrosis. Added where applicable.

Pose validation

Pose clustering method, key residues, and selection criteria have been added. Incorporated at appropriate position in the manuscript.

Functional Enrichment

We now specify ontology categories (GO BP/CC/MF), KEGG and Reactome usage, significance cutoffs, background gene set, and multiple-testing corrections. Included in manuscript.

Statistics

Sample sizes, test types, normality checks, and two-sided assumptions are now clearly stated. Incorporated in manuscript.

Reviewer Comment: Some references were not aligned and formatting needed correction.

Response: All references have been rechecked and reformatted.

Reviewer Comment: Docking alone is insufficient; MD simulations must be performed and reported.

Regulatory approval

We have now specified the agency and year of pirfenidone approval (FDA 2014; EMA 2011) with primary citations.

MD integration

The MD findings are now integrated at appropriate positions in the manuscript.

We have added PDB IDs and UniProt IDs for PDE1A, GEO dataset IDs with GSM groupings, platform IDs, and Links of the utilized public repositories.

All identifiers are now clearly listed in the manuscript.

All requested analyses have been performed and added, including RMSD, RMSF, MM/PBSA binding energies, and other necessary analyses.

These results are presented in the revised Results.

Reviewer #3 Comment:

The RMSF analysis could be strengthened by a residue-specific interpretation and quantitative comparison between apo and ligand-bound systems.

Response: The RMSF analysis has been revised to include residue-specific interpretation of regions 260–269, 330–333, 366–367, and 384–388, with explicit discussion of their secondary structure context and proximity to the binding pocket. Additionally, a comparison of RMSF values between apo and pirfenidone-bound systems performed and reduced fluctuation was observed in pirfenidone-bound systems. These revisions clarify the ligand-induced stabilization of PDE1A dynamics without requiring additional simulations, as suggested. The results have been mentioned in the manuscript at appropriate positions.

Thank you for your consideration.

Attachment

Submitted filename: Reviewer Response Letter- R4- 16-01-2026.docx

pone.0342991.s005.docx^{(18.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0342991.r009

Decision Letter 4

Mahbub Hasan

26 Jan 2026

Dear Dr. Ullah,

A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Mahbub Hasan, PhD

Academic Editor

PLOS One

Journal Requirements:

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #3: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

Reviewer #3: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #3: No

**********

Reviewer #3: The manuscript can be accepted once all trajectory data and the code used to generate the related figures are made publicly available. For example, the complete trajectory dataset may be uploaded to Zenodo or a similar repository, with the corresponding access link included in the Data Availability section.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Reviewer #3: No

**********

To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

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NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

PLoS One. 2026 Apr 10;21(4):e0342991. doi: 10.1371/journal.pone.0342991.r010

Author response to Decision Letter 5

28 Jan 2026

Response to Reviewer Comments- R5

We are very grateful for the valuable time and constructive comments provided by both the editor and the reviewers. All observations have significantly improved the quality, clarity, and reproducibility of our manuscript. We have carefully addressed all reviewers’ comments and made every effort to incorporate all recommended changes.

Reviewer Comment: The manuscript can be accepted once all trajectory data and the code used to generate the related figures are made publicly available. For example, the complete trajectory dataset may be uploaded to Zenodo or a similar repository, with the corresponding access link included in the Data Availability section.

Response: We thank the reviewer for this valuable suggestion. In response, all trajectory data and the complete code used to generate the related figures have now been made publicly available. The full dataset has been deposited in the Zenodo repository and can be accessed via the following DOI: https://doi.org/10.5281/zenodo.18387883. The corresponding access link has also been included in the Data Availability section of the online portal as well as revised manuscript.

Attachment

Submitted filename: Reviewer Response Letter- R5- 28-01-2026.docx

pone.0342991.s006.docx^{(15.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0342991.r011

Decision Letter 5

Mahbub Hasan

1 Feb 2026

Network Pharmacology and Integrative Bioinformatics analyses identify PDE1A as a key target of Pirfenidone in Idiopathic Pulmonary Fibrosis

PONE-D-24-60633R5

Dear Dr. Ullah,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0342991.r012

Acceptance letter

Mahbub Hasan

PONE-D-24-60633R5

PLOS One

Dear Dr. Ullah,

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Attachment

Submitted filename: Authors Response Letter.docx

pone.0342991.s002.docx^{(474.6KB, docx)}

Attachment

Submitted filename: Authors Response Letter- PONE-D-24-60633R2.docx

pone.0342991.s003.docx^{(575.8KB, docx)}

Attachment

Submitted filename: Reviewer Response Letter- R3- 10-12-2025.docx

pone.0342991.s004.docx^{(16.7KB, docx)}

Attachment

Submitted filename: Reviewer Response Letter- R4- 16-01-2026.docx

pone.0342991.s005.docx^{(18.2KB, docx)}

Attachment

Submitted filename: Reviewer Response Letter- R5- 28-01-2026.docx

pone.0342991.s006.docx^{(15.6KB, docx)}

Data Availability Statement

All trajectory data and the code used to generate the figures and results presented in this study are publicly available in the Zenodo repository at https://doi.org/10.5281/zenodo.18387883.

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PERMALINK

Network pharmacology and integrative bioinformatics analyses identify PDE1A as a key target of pirfenidone in idiopathic pulmonary fibrosis

Jing Wu

Haseeb Khaliq

Yanyan Ke

Qudrat Ullah

Sheikh Arslan Sehgal

Xue Yi

Roles

Abstract

Introduction

Materials and methods

Identification and analysis of differentially expressed genes in IPF

Identification of pirfenidone targets

Functional enrichment analysis

The human protein atlas

Molecular docking

Molecular dynamics simulations

MicroScale Thermophoresis (MST) assay

Protein labeling.

Fluorescence intensity detection.

Binding affinity measurement.

Validation of PDE1A expression in the GSE10667, GSE110147, and GSE226249 datasets

Statistical analysis

Results

Identification of potential targets of pirfenidone in IPF

Fig 1. Flowchart.

Fig 2. Screening of differential genes.

Screening of differential genes and core target identification

Fig 3. Screening of core target genes.

Validation of the core target gene PDE1A in IPF

Fig 4. Verification of core target genes.

Interaction analysis of Pirfenidone and PDE1A: MD and MST

Fig 5. The experimental results of MD and MST.

Table 1. Molecular docking analyses along with binding affinity.

Fig 6. The experimental results of MD and MST.

Table 2. Protein binding free energy with ligand (MM/PBSA analysis).

PDE1A expression in lung tissue and the impact of pirfenidone treatment

Fig 7. Validation of PDE1A in the GSE226249 dataset.

Discussion

Conclusion

Data Availability

Funding Statement

References

Decision Letter 0

Mahbub Hasan

Roles

Author response to Decision Letter 1

Decision Letter 1

Mahbub Hasan

Roles

Author response to Decision Letter 2

Decision Letter 2

Mahbub Hasan

Roles

Author response to Decision Letter 3

Decision Letter 3

Mahbub Hasan

Roles

Author response to Decision Letter 4

Decision Letter 4

Mahbub Hasan

Roles

Author response to Decision Letter 5

Decision Letter 5

Mahbub Hasan

Roles

Acceptance letter

Mahbub Hasan

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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RESOURCES

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