Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer

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It exhibits unique clinical and pathological features, demonstrates high aggressiveness, and has a relatively poor prognosis and clinical outcome. Objective To identify a novel drug target protein against TNBC and potential phytochemical lead molecules against the identified target. Methods In this study, we retrieved TNBC samples from NGS and microarray datasets in the Gene Expression Omnibus database. We employed a combination of differential gene expression studies, protein-protein interaction analysis, and network topology investigation to identify the target protein. Additionally, the molecular docking and molecular dynamics (MD) simulation studies followed by Molecular Mechanics with Generalised Born Surface Area salvation was used to identify potential lead molecule. Result The upregulated genes with LogFC > 1.25 and P-value < 0.05 from the TNBC gene expression dataset were identified. Androgen receptor (AR) was found to be an appropriate hub target in the protein-protein interaction network. Phytochemicals that inhibit breast cancer target were retrieved from the PubChem database and virtual screening was performed using PyRx against the AR protein. Thereby, the AR was found to be the target protein and 2-hydroxynaringenin was discovered to be a possible phytochemical lead molecule for combating TNBC. Moreover, the AR and the 2-hydroxynaringenin complex showed structural stability and higher binding affinity through molecular dynamics and MM-GBSA studies. Conclusion AR was identified as a hub protein that is highly expressed in breast cancer and 2-hydroxynaringenin efficacy of counter TNBC requires further investigation both in vitro and in vivo. 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F1000Research 2025, 13 :1271 ( https://doi.org/10.12688/f1000research.155657.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] Pranaya Sankaranarayanan https://orcid.org/0000-0001-8076-6994 1 , Dicky John Davis G https://orcid.org/0000-0003-2325-7952 1 , Abhinand PA 1 , M Manikandan 2 , Arabinda Ghosh 3 Pranaya Sankaranarayanan https://orcid.org/0000-0001-8076-6994 1 , Dicky John Davis G https://orcid.org/0000-0003-2325-7952 1 , [...] Abhinand PA 1 , M Manikandan 2 , Arabinda Ghosh 3 PUBLISHED 18 Mar 2025 Author details Author details 1 Department of Bioinformatics, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, Tamil Nadu, 600116, India 2 Department of Medical Genetics, Manipal Hospitals, Bengaluru, Karnataka, 560 017, India 3 Department of Botany, Gauhati University, Guwahati, Assam, India Pranaya Sankaranarayanan Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation Dicky John Davis G Roles: Investigation, Project Administration, Supervision, Validation, Visualization, Writing – Review & Editing Abhinand PA Roles: Conceptualization, Formal Analysis, Software, Validation, Writing – Review & Editing M Manikandan Roles: Data Curation, Formal Analysis, Software, Visualization Arabinda Ghosh Roles: Formal Analysis, Resources, Software, Writing – Original Draft Preparation OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Bioinformatics gateway. Abstract Background Triple-negative breast cancers (TNBC) are defined as tumors that lack the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It exhibits unique clinical and pathological features, demonstrates high aggressiveness, and has a relatively poor prognosis and clinical outcome. Objective To identify a novel drug target protein against TNBC and potential phytochemical lead molecules against the identified target. Methods In this study, we retrieved TNBC samples from NGS and microarray datasets in the Gene Expression Omnibus database. We employed a combination of differential gene expression studies, protein-protein interaction analysis, and network topology investigation to identify the target protein. Additionally, the molecular docking and molecular dynamics (MD) simulation studies followed by Molecular Mechanics with Generalised Born Surface Area salvation was used to identify potential lead molecule. Result The upregulated genes with LogFC > 1.25 and P-value < 0.05 from the TNBC gene expression dataset were identified. Androgen receptor (AR) was found to be an appropriate hub target in the protein-protein interaction network. Phytochemicals that inhibit breast cancer target were retrieved from the PubChem database and virtual screening was performed using PyRx against the AR protein. Thereby, the AR was found to be the target protein and 2-hydroxynaringenin was discovered to be a possible phytochemical lead molecule for combating TNBC. Moreover, the AR and the 2-hydroxynaringenin complex showed structural stability and higher binding affinity through molecular dynamics and MM-GBSA studies. Conclusion AR was identified as a hub protein that is highly expressed in breast cancer and 2-hydroxynaringenin efficacy of counter TNBC requires further investigation both in vitro and in vivo. READ ALL READ LESS Keywords Triple Negative Breast Cancer, AR target, Phytochemicals, 2–hydroxy naringenin, Virtual screening, Molecular Docking, Molecular dynamics simulation. Corresponding Author(s) Dicky John Davis G ( [email protected] ) Close Corresponding author: Dicky John Davis G Competing interests: No competing interests were disclosed. Grant information: This work was supported by the Founder-Chancellor Shri. N. P. V. Ramasamy Udayar Research Fellowship (U02B160480), Sri Ramachandra Institute of Higher Education and Research. The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation. Copyright: © 2025 Sankaranarayanan P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Sankaranarayanan P, G DJD, PA A et al. Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.12688/f1000research.155657.2 ) First published: 24 Oct 2024, 13 :1271 ( https://doi.org/10.12688/f1000research.155657.1 ) Latest published: 18 Mar 2025, 13 :1271 ( https://doi.org/10.12688/f1000research.155657.2 ) Revised Amendments from Version 1 The major differences between the two versions of the article primarily involve refinements to the text, improved clarity, and potentially additional references. However, both versions maintain the same core objectives, methodology, and results. The major differences between the two versions of the article primarily involve refinements to the text, improved clarity, and potentially additional references. However, both versions maintain the same core objectives, methodology, and results. See the authors' detailed response to the review by Shiek S S J Ahmed READ REVIEWER RESPONSES Introduction Breast cancer is the most common type of cancer worldwide, as reported by the World Health Organization (WHO) in 2020 with over 7.8 million women living in the last five years diagnosed with breast cancer. 1 It is responsible for 685,000 deaths worldwide. However, it should be noted that breast cancer is a non-homogenous condition that can be classified into several significant subtypes based on the expression of their genes. Triple-negative breast cancers (TNBC) are characterized by the absence of estrogen, progesterone, and ERBB2 receptors, and are specifically identified as estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and human epidermal growth factor receptor 2 (HER2). TNBC accounts for 12%–17% of all breast cancers. 2 Sandhu et al. revealed a considerably greater prevalence of TNBC in India than in Western populations. Approximately one in three women diagnosed with breast cancer in India was found to have triple-negative disease. TNBC exhibits unique clinical and pathologic features, is highly aggressive, and has a relatively poor prognosis and clinical outcome. 3 Currently, there is no recognized targeted treatment for TNBC. The primary treatment options for TNBC involve chemotherapy utilizing anthracyclines, taxanes, and/or platinum compounds as the major treatment modalities. A significant proportion of TNBC patients fail to attain Pathological Complete Response (pCR) with standard chemotherapy, prompting concerns about the effectiveness and safety of the chosen chemotherapy. 4 A better understanding of the pathological mechanisms of TNBC onset and progression and the molecular interactions underlying the etiology of the condition can help improve the prophylaxis and design of novel targeted treatment against this cancer type. 5 Gene expression profiling can be invaluable for detecting transcriptional variations between normal and malignant cells and can be extensively used to study gene phenotype associations in breast neoplasms. 6 Protein interaction networks potentially signify patterns in network connectivity between proteins, which can differ between breast cancer subtypes. 7 Phytochemicals are natural, non-toxic compounds found in plants that possess disease-protective or preventive properties. 8 They modulate the molecular pathways associated with cancer growth and progression. 9 The present study aimed to identify a novel therapeutic target protein for TNBC by integrating differential gene expression studies with protein-protein interactions and network topology analysis. Subsequently, phytochemicals with reported anti-breast cancer activities will be subjected to virtual screening by molecular docking against the identified novel target. To validate these findings, Molecular Mechanics with Generalised Born Surface Area solvation and Molecular Dynamics simulations were performed. Based on their binding affinity to the target protein, novel therapeutic phytochemical lead molecules with anti-TNBC activity were identified. Method Gene expression profiling of TNBC microarray datasets A thorough literature mining effort encompassing all eligible studies on gene expression in TNBC was conducted. The search involved querying the Gene Expression Omnibus (GEO) datasets. Gene expression profiling was performed using GEO2R to identify significantly upregulated genes. Figure 1 presents an overview of the methodology. Figure 1. Overview of methodology. During the literature mining process, a microarray dataset was obtained from the NCBI GEO repository using the accession number GSE45498 annotated in the GPL16299 platform. This dataset encompasses 40 samples from healthy normal tissues, 160 from individuals with cancer, and 54 from metastatic cases. NGS datasets were obtained from the NCBI GEO repository using accession number GSE214101 annotated in the GPL20301 platform. This dataset included 24 samples derived from the MDA-MB-231 and MDA-MB-436 cell lines. Gene expression profiling values underwent log (base2) transformation and percentage shift normalization was applied. To assess the differences in gene expression between normal and diseased samples, the fold change for each gene was individually calculated. A threshold of 1.25-fold change was used to categorize genes as being upregulated. Gene expression profiling followed the protocol reported previously. 10 Study of protein–protein interactions The selected genes were subjected to the Bisogenet plug-in of Cytoscape to identify protein-protein interactions of all genes differentially regulated in TNBC. STRING is an open-source bioinformatics platform integrated in Cytoscape, designed for the study of both predicted and known protein-protein interactions. This database gathers, evaluates, and integrates information on protein-protein interactions from all publicly available sources. Additionally, it augments these data with computational predictions. 11 These interactions encompass both indirect (functional) and direct (physical) associations. 12 The genes were uploaded and a string network was built. Molecular Complex Detection (MCODE) detects Protein-Protein Interactions subnetworks and highly interconnected clusters within the PPI network. 13 PPI networks were broken down into top-ranked dense cliques (sub-clusters) using the MCODE plugin. The top-ranked dense clique was selected for further analysis. Building a library of phytochemicals with anti-breast cancer activity Phytochemicals are naturally occurring biologically active chemical compounds found in plants that serve as medicinal ingredients and nutrients, offering health benefits to humans. 14 Many natural products and their analogs have been identified as potent anticancer agents and the anticancer properties of various plants and phytochemicals. 15 Phytochemicals were identified through a systematic literature search indicating anti-breast cancer activity were selected, and their 3D structures in SDF format were retrieved from PubChem database. Subsequently, phytochemicals that did not conform to Lipinski’s rule of five were excluded, and the remaining compounds were subjected to further analyses. Virtual screening Understanding the fundamental principles governing how protein receptors recognize, interact, and form associations with molecular substrates and inhibitors is crucial for drug discovery. PyRx v0.8 software 16 with an inbuilt AutoDock Vina 1.2.5 17 for molecular docking was used to scan phytochemicals conforming to Lipinski’s rule of 5. AutoDock Vina uses a semi-empirical free-energy force field to predict the binding free energies of small molecules to macromolecular targets. The human Androgen Receptor (PDB ID: 1E3G) was sourced from the RCSB Protein Data Bank. Initially, the protein structure underwent a curation process to remove any crystallographic water molecules and heteroatoms that might interfere with docking simulations. Subsequently, energy minimization was performed using UCSF Chimera vs 1.54 ( https://www.cgl.ucsf.edu/chimera/ ) to optimize the geometry of the protein. The steepest descent algorithm was applied for 100 steps, which is a common approach to relieve steric clashes and achieve a more stable conformation. Partial charges were then assigned to the protein using the AMBER ff14SB force field, which is well known for accurately modeling protein dynamics and interactions. The co-crystallized ligand metribolone (R18) was used as the control, and the ligands were docked at its active site. ADMET - ProTox II The development of high-quality in silico ADMET models will enable compound efficacy and druggability features to be optimized concurrently, thereby improving the overall quality of drug candidates. 18 ProTox-II was used to experimentally validate the chemical toxicity and their combination. It uses machine learning models, the most common features, pharmacophore-based, fragment propensities, and chemical similarity to forecast different toxicity endpoints. 19 Based on the virtual screening results, the top ten phytochemical compounds were chosen for ADMET analysis. Induced fit docking Induced fit docking was carried out using Schrodinger vs. 2020.3, which takes into account the flexibility of both the protein receptor and ligand, allowing for conformational changes to occur upon binding. The energy-minimized ligands were saved in PDB format for compatibility with the Schrodinger software, and the partial charges of the ligands were assigned, such as Gasteiger charges, which estimate the distribution of charges on the molecule based on its structure. Similarly, the protein charges may also be assigned using OPLS_2005 force fields to accurately capture its electrostatic properties. The grid box is a crucial parameter in docking simulations, as it defines the search space where the ligand can orient itself around the protein receptor. The dimensions of the grid box are typically specified in terms of the number of grid points along each axis (nx, ny, nz) and the grid spacing (Å) around the binding cavity residues LEU701, LEU707, MET742, MET745, ARG752, MET780, MET787, ALA748, LEU880, LEU873, PHE876, MET895, ILE899, THR877, GLN774, PHE764, LEU746, GLY708, GLN711, TRP741, ASN705. The dimensions were set to (58, 64, and 52 Å), providing a sufficient volume to explore potential binding modes of the ligand within the protein’s active site with a charge cutoff polarity set for a charge cutoff of 0.25 Å. Molecular dynamics simulation Molecular dynamics (MD) simulations were conducted for the docked complex of the human Androgen Receptor with the best-docked molecule, employing Schrodinger Desmond 2020.1. 20 The OPLS-2005 force field, 21 along with an explicit solvent model using SPC water molecules, 22 were employed in this system. The simulation was performed in a periodic boundary solvation box with dimensions of 10 × 10 × 10 Å. To neutralize the charge, Na+ ions were added, and a 0.15 M NaCl solution was added to mimic the physiological environment. The initial equilibration was carried out using an NVT ensemble for 10 ns to allow the system to relax over the protein-ligand complexes. Subsequently, a short run of equilibration and minimization was conducted using an NPT ensemble for 12 ns. The NPT ensemble utilized the Nose-Hoover chain coupling scheme 23 with a temperature set at 37 °C, relaxation time of 1.0 ps, and pressure maintained at 1 bar in all simulations. A time step of 2 fs was used. Pressure control was achieved using the Martyna-Tuckerman-Klein chain coupling scheme 24 with a relaxation time of 2 ps. The long-range electrostatic interactions were calculated using the particle mesh Ewald method, 25 and the Coulomb interaction radius was fixed at 9 Å. A RESPA integrator with a time step of 2 fs was used for each trajectory to calculate the bonded forces. The final production run was extended for 100 ns for the Human Androgen Receptor with the best-docked molecule complex. To track the stability of the MD simulations, a variety of parameters were computed, including the number of hydrogen bonds, radius of gyration (Rg), root-mean-square fluctuation (RMSF), and root-mean-square deviation (RMSD). Binding free energy analysis Molecular Mechanics Generalized Born Surface Area (MM-GBSA) approaches are less computationally intensive than biochemical free energy methods and more precise than most molecular docking scoring systems. This method is useful for predicting the binding free energy in molecular systems. MM-GBSA is a useful technique for comprehending the impact of mutations on large biomolecular systems. 26 Biomolecular research has been utilized in investigations of protein folding, protein-ligand binding, protein-protein interactions etc. 27 The MM-GBSA approach was used to determine the binding free energies of the ligand-protein complexes. The MM-GBSA binding free energy was computed using the Python script thermal mmgbsa.py in the simulation trajectory with the VSGB solvation model and OPLS5 force field over the last 50 frames with a 1 step sampling size. The binding free energy of MM-GBSA (kcal/mol) was calculated using the additivity principle, wherein the differences in free energies, GBSA solvation energies, and surface area energies of ligand-protein complexes compared to their respective total energies of them individually were calculated. Results Differentially expressed genes (DEGs) analysis Gene expression in TNBC and normal microarray datasets was compared to assess the underlying molecular pathways driving TNBC, and further network analysis was performed. Boolean operators and relevant filters were used to filter the microarray datasets using the GEO2R. The Benjamini-Hochberg-Yekutieli approach was used to adjust the P-value for the DEGs, and only the top 10% of the upregulated genes (P-value 1.25 and P-value 1.25 and P-value <0.05. Gene ID Description log 2 FC p-Value ESR1 Estrogen Receptor 1 3.45098 8.51E-14 IGFBP6 Insulin-like growth factors binding protein-6 3.115311 1.71E-14 NGFR Nerve growth factor receptor 3.069617 3.26E-10 DLC1 Deleted in liver cancer 1 2.833933 1.03E-12 TGFBR3 Transforming Growth Factor Beta Receptor 3 2.631049 2.84E-10 EGR1 Early growth response factor 1 2.31673 5.84E-11 NTRK2 Neurotrophic Tyrosine Receptor Kinase 2.19261 1.77E-06 PPARG Peroxisome proliferator-activated receptor gamma 2.151492 3.32E-10 CD34 CD34 1.887035 5.93E-09 IGF1 Insulin-Like Growth Factor-1 1.870246 1.53E-10 FOS FOS 1.734574 5.27E-08 CAV1 Caveolin 1 1.694425 6.72E-07 FGF2 Fibroblast Growth Factor 2 1.61343 4.41E-04 KIT KIT 1.547563 2.93E-05 AR Androgen Receptor 1.381295 2.51E-04 Table 2. The list of upregulated genes in dataset GSE214101 with LogFC > 1.25 and P-value <0.05. Gene ID Description log 2 FC p-value CDH4 cadherin 4 2.805 2.26E-06 MAP 2K6 mitogen-activated protein kinase kinase 6 2.659 2.16E-16 SHANK2 SH3 and multiple ankyrin repeat domains 2 2.62 7.80E-08 NEGR1 neuronal growth regulator 1 2.388 2.80E-03 AKAP6 A-kinase anchoring protein 6 2.26 7.91E-04 AR androgen receptor 2.15 7.05E-02 MAP 2 microtubule associated protein 2 2.116 3.90E-08 NCAM2 neural cell adhesion molecule 2 2.091 2.00E-03 NLGN1 neuroligin 1 2.074 1.92E-04 ADGRL3 adhesion G protein-coupled receptor L3 2.049 1.37E-03 PRKG1 protein kinase cGMP-dependent 1 1.976 7.03E-05 PDE11A phosphodiesterase 11A 1.895 1.30E-04 FAM78B family with sequence similarity 78 member B 1.705 1.30E-04 PLXDC2 plexin domain containing 2 1.685 3.61E-11 SEMA3D semaphorin 3D 1.657 2.45E-06 ID1 inhibitor of DNA binding 1 1.637 3.42E-03 The STRING tool was used to identify potential connections between DEGs in different tissues. 12 To build PPI networks, active interaction sources such as databases, co-expression, text mining, experiments, and species restricted to “Homo sapiens” were used, along with an interaction score greater than 0.4. The PPI network was displayed using Cytoscape v3.6.1 software as depicted in Figure 2 . Figure 2. Protein–protein interaction network where Androgen receptor (AR) is the central hub gene. Table 3. List of compounds used for Tox prediction. Compound name Docking score Predicted LD 50 Hepatotoxicity Carcinogenicity Immunotoxicity Mutagenicity Cytotoxicity Chrysin 7-O-beta-D-glucopyranuronoside -7.2 5000 mg/kg Inactive Inactive Inactive Inactive Inactive 0.73 0.51 0.96 0.74 0.81 Atalantoflavone -7.1 2570 mg/kg Inactive Inactive Inactive Inactive Inactive 0.77 0.5 0.51 0.62 0.83 8-Prenyldaidzein -6.8 2500 mg/kg Inactive Inactive Inactive Inactive - 0.7 0.66 0.8 0.65 6-Prenylnaringenin -6.7 2000 mg/kg Inactive Inactive Inactive Inactive Inactive 0.69 0.69 0.5 0.64 0.79 alpha-Isowighteone -6.6 2875 mg/kg Inactive Inactive Inactive Inactive Inactive 0.71 0.61 0.85 0.55 0.81 2-Hydroxynaringenin -6.5 2000 mg/kg Inactive Inactive Inactive Inactive Inactive 0.71 0.57 0.8 0.77 0.55 Carpachromene -6.5 4000 mg/kg Inactive Inactive Inactive Inactive Inactive 0.77 0.5 0.61 0.62 0.83 8-Demethyleucalyptin -6.3 3919 mg/kg Inactive Inactive Inactive Inactive Inactive 0.71 0.54 0.83 0.73 0.93 5-Hydroxy-7-acetoxy-8-methoxyflavone -6.3 5000 mg/kg Inactive Inactive Inactive Inactive Inactive 0.76 0.54 0.87 0.7 0.83 Apigenin -6.3 2500 mg/kg Inactive Inactive Inactive Inactive Inactive 0.68 0.62 0.99 0.57 0.87 The MCode plugin was employed to identify the highly linked regions inside the PPI network, while the CentiScape plugin was utilized to calculate the network topology parameters. Using degree and betweenness as the primary parameters, hub genes were identified. A complete set of algorithms, called CentiScape, was used to analyze the centrality of the network nodes. It can calculate multiple centralities for weighted, directed, and undirected networks. 28 The human Androgen Receptor was determined to be an appropriate hub gene in the protein-protein interaction network consisting of DEG genes. Virtual screening of phytochemical library The human Androgen Receptor (hAR), covering the C-terminal amino acid residues (1E3G) with the co-crystallized ligand metribolone (R18), consists of 250 amino acid residues arranged in a three-layered α-helical sandwich structure. The ligand-binding pocket is located within the hydrophobic cavity formed by helices. A total of 1358 compounds were initially identified through systematic literature search, and their structures were retrieved from the PubChem database. Of these, only 543 compounds met the criteria outlined by Lipinski’s rule of five. These 543 compounds were then selected for the initial virtual screening against human Androgen Receptor using PyRx, and their binding affinities were tabulated 29 (refer to extended data Table S1). The top 50 ranked compounds were subjected to ADMET analysis on the ProTox II server. Only the top 10 ranked compounds that showed favorable binding affinity towards hAR based on their docking interaction and ideal ADMET properties were chosen for further analysis. The initial docking results and ADMET properties are shown in extended data. Induced fit docking and the molecular interactions Molecular interaction studies of the binding cavity of the human Androgen Receptor and molecules are listed in extended data This was compared with the co-crystallized ligand associated with hAR protein R18 and analyzed by Schrodinger-induced fit docking. The ligand 2-hydroxynaringenin demonstrated high affinity for flexible residues within the binding pocket of the Human Androgen receptor protein. The calculated free energy of binding (ΔG) was determined to be -8.59 kcal/mol, indicating a strong binding interaction. While couple of other molecules 8-Prenyldaidzein and 5-Hydroxy-7-acetoxy-8-methoxyflavone also exhibited significant binding with hAR having ΔG = -8.54 kcal/mol and -8.26 kcal/mol, respectively. The highest affinity with a low negative binding energy was observed for 2-hydroxynaringenin, where the ligand formed conventional hydrogen bonds with Leu704, Asn705, Gln711, Met745, Arg752, and Thr877. Leu707, Met780, Leu873, and Phe876 were found to be involved in pi-alkyl and alkyl interactions with the 2-Hydroxynaringenin ligand. The binding energies of 2-Hydroxynaringenin and protein-ligand interactions are displayed in Figure 4 and the binding energies of other molecules are depicted in extended data. Figure 3. Role of Androgen receptor (Source modified from Ref. 30 ). Figure 4. Induced fit docking pose of the ligand (A) 2-Hydroxynaringenin and co-crystallized (B) R18 molecules with hAR (PDB ID: 1E3G) displaying the ribbon shaped 3D protein and ligand interaction, 3D image of binding cavity residues and 2D interaction profile of bidning cavity residues with the respective ligands. Molecular dynamics simulation studies Molecular dynamics simulation (MD) investigations were performed to ascertain the convergence and stability of 1E3G-Apo (no ligand hAR protein), 1E3G+R18 (R18 co-crystallized ligand) and 1E3G+2-Hydroxynaringenin complexes. When comparing the root mean square deviation (RMSD) measurements, the 100 ns simulation showed a stable conformation. The Apo protein’s Cα-backbone’s RMSD showed a 3.0 Å divergence ( Figure 5A ). While 1E3G+R18 and 1E3G+2-Hydroxynaringenin both showed 2.9 Å, the overall RMSD is shown to be 2.9 Å ( Figure 5A ). The root mean square fluctuations (RMSF) plot of the 1E3G+2-Hydroxynaringenin complex protein revealed notable variations at residues 60–70, 110–120, and 180–185, which may have been caused by the residues’ increased flexibility. The rest of the residues fluctuated less during the course of the 100 ns simulation ( Figure 5B ). Radius of gyration (Rg) in this study, 1E3G Cα-backbone bound to Apo protein displayed increment of Rg values indicating lesser compactness while stable Rg was observed from 20.2 to 20.3 Å in 1E3G+R18 ( Figure 5C ). The number of hydrogen bonds was significantly different between 1E3G+2-Hydroxynaringenin, throughout the simulation time of 100 ns ( Figure 5D ). The average number of hydrogen bonds observed in 1E3G+2-Hydroxynaringenin was two on average in MD simulation studies ( Figure 5D , red color). Figure 5. MD simulation analysis of 100 ns trajectories of (A) Cα backbone RMSD of 1E3G+2-Hydroxynaringenin (red), RMSD of 1E3GApo (black), and 1E3G+R18 (blue) (B) RMSF of Cα backbone RMSD of 1E3G+2-Hydroxynaringenin (red), RMSD of 1E3GApo (black), and 1E3G+R18 (blue) (C) Radius of gyration (Rg) of Cα backbone of Cα backbone RMSD of 1E3G+2-Hydroxynaringenin (red), RMSD of 1E3GApo (black), and 1E3G+R18 (blue) (D) Formation of hydrogen bonds in 1E3G+2-hydroxynaringenin (red) and R18 (black). Mechanics generalized born surface area (MM-GBSA) calculations Utilizing the MD simulation trajectory, the binding free energy along with other contributing energy in form of MM-GBSA were determined for hAR+2-hydroxynaringenin. The results ( Table 4 ) suggested that the maximum contribution to ΔG bind in the stability of the simulated complexes were due to ΔG bind Coulomb, ΔG bind vdW and ΔG bind Lipo, while, ΔG bind Covalent and ΔG bind SolvGB contributed to the instability of the corresponding complexes. The hAR+2-hydroxynaringenin complex showed significantly higher binding free energies. These results supported the potential of 2-hydroxynaringenin, showed the efficiency in binding to the selected protein and the ability to form stable protein-ligand complexes. Table 4. Binding free energy components for the 1E3G+2-hydroxynaringenin and 1E3G+R18 calculated by MM-GBSA. Energies (kcal/mol) 1E3G+2-hydroxynaringenin 1E3G+R18 ΔG bind -31.53±5.3 -29.95±4.1 ΔG bind Lipo -29.83±3.2 -23.51±3.2 ΔG bind vdW -22.68±3.22 -16.27±1.21 ΔG bind Coulomb -5.22±2.11 -7.45±2.8 ΔG bind H bond -0.9±0.1 -0.6±0.2 ΔG bind SolvGB 33.91±1.27 41.27±1.76 ΔG bind Covalent 0.79±0.3 1.24±0.23 Discussion The integrated analysis of gene expression and protein-protein interactions (PPI) would help to identify candidates that could serve as therapeutic targets. In this study, we compared TNBC datasets to normal datasets to assess the underlying molecular pathways that drive TNBC. Differential gene expression profiling of the selected datasets using the Benjamini-Hochberg-Yekutieli approach was used to adjust the P-value, which controls the rate of false discovery under positive dependence assumptions. Then, using STRING, which incorporates both known and anticipated PPIs, the protein-protein interactions between the previously mentioned genes were investigated using Cytoscape. CentiScape was used to analyze the centrality of network nodes, and the Human Androgen Receptor was determined to be an appropriate hub gene in the protein-protein interaction network consisting of DEG genes. The Androgen Receptor (AR) pathway is becoming a viable therapeutic target in breast cancer. 31 12-55% of TNBC cases, which provides a chance for targeted treatment. The “Luminal AR (LAR)” molecular subtype of TNBC is where AR is most prevalent. 32 The LAR subtype exhibits the highest amount of AR expression amongst the many molecular subtypes of TNBC in which it is present. All AR+ TNBC primary tumors that were evaluated showed nuclear localization of AR, a sign of transcriptionally active receptors. Many investigations have shown that AR expression in breast cancer, particularly in the TNBC subtype, has been linked to an overall better outcome. Considering that > 70% of AR expression is consistent between primary and metastatic breast cancers, AR may be a novel diagnostic and therapeutic target for patients with AR-positive breast cancer. 33 In luminal mammary carcinomas, a high percentage of cases express androgen receptors (AR), and the ratio of AR to estrogen receptors (ER) or progesterone receptors (PR) is considered a potential prognostic factor. However, in estrogen receptor-negative (ER-) tumors, AR expression is associated with a poorer prognosis. Androgen receptor (AR) expression has demonstrated predictive value for potential response to adjuvant hormonal therapy in estrogen receptor-positive (ER+) breast cancers. Additionally, AR expression has been associated with predicting responses to neoadjuvant chemotherapy in triple-negative breast cancer (TNBC). The role of the AR is shown in Figure 3 . The human Androgen Receptor, which has 920 amino acid residues, was identified as the primary therapeutic target for TNBC. The 3D structure of Androgen Receptor (PDB ID: 1E3G) does not cover the entire protein and contains only the amino acids 671-920. This region encompasses the nuclear receptor ligand-binding domain (NR LBD) of human AR. It consists of 250 amino acid residues, arranged in a three-layered α-helical sandwich structure. The ligand-binding pocket is located within the hydrophobic cavity formed by helices. Virtual screening of 543 ligands against human AR was performed using PyRx at the co-crystallized ligand-binding site. The top 10 ranked compounds that showed favorable binding affinity towards human AR and ideal ADMET properties were chosen for induced fit docking. Unlike rigid docking, induced fit docking treats the ligand and protein as typically flexible entities allowing for conformational changes to occur upon binding. The ligand 2-hydroxynaringenin demonstrated a high affinity for the flexible residues within the binding pocket of human AR, with an interaction binding energy of-8.59 kcal/mol with six conventional hydrogen bonds, indicating a strong binding interaction. Interestingly, the interaction binding energy of the human AR protein with R18 was observed to be -7.8 kcal/mol and only one conventional hydrogen bond formed between R18 and Arg752 ( Figure 4 ). No other potential interactions were observed, except for van der Waal’s instructions. For both 2-Hydroxynaringenin and R18, it was observed that Arg752 is the key residue for ligand binding and could play an active role in protein function. Molecular dynamics (MD) simulation studies of 100 ns showed stable conformations with 1E3G+2-Hydroxynaringenin complexes. The RMSD of the Cα-backbone of the Apo protein exhibited a deviation of 3.0 Å. While 1E3G+R18 exhibited 2.9 Å and simlarly 1E3G+2-Hydroxynaringenin also exhibited the total RMSD is depicted to be 2.9 Å ( Figure 5A ). All RMSD values were below the acceptable range of 3 Å. 34 Stable RMSD plots of apo-1E3G, 1E3G+R18 and 1E3G+2-Hydroxynaringenin were observed to be less than 3 Å. Therefore, it can be suggested that apo-1E3G, 1E3G+R18 and 1E3G+2-Hydroxynaringenin complexes are well converged and equilibrated. The RMSF of the 1E3G+2-Hydroxynaringenin complex protein exhibited notable fluctuation spikes at residues 60–70, 110–120, and 180–185, which may have been brought on by the residues’ increased flexibility. During the course of the 100 ns simulation, the remaining residues fluctuated less. A more rigid conformation with fewer fluctuations was observed in the Apo-protein and 1E3G+R18 complex. Therefore, from the RMSF plots, it can be suggested that the structures of 1E3G+2-Hydroxynaringenin are more flexible during simulation in ligand-bound conformations. The radius of gyration (Rg) is a measure of protein compactness. Lowering and stable of radius of gyration (Rg) from 20.0 to 20.02 Å in 1E3G+2-Hydroxynaringenin was observed. The quantity of hydrogen bonds forming between the ligand and protein indicates a strong connection and stability of the complex. Over the course of the 100 ns simulation, there was a considerable difference in the amount of hydrogen bonds between 1E3G+2-Hydroxynaringenin ( Figure 5D ). The average number of hydrogen bonds observed in 1E3G+2-Hydroxynaringenin was two on average in MD simulation studies ( Figure 5D , red). Using the MD simulation trajectory, the binding free energy and additional contributing energies in the form of MM-GBSA were found for hAR+2-hydroxynaringenin. The findings ( Table 4 ) show that ΔGbindCoulomb, ΔGbindvdW, and ΔGbindLipo were the main contributors to ΔGbind in the simulated complexes’ stability, whereas ΔGbindCovalent and ΔGbindSolvGB were responsible for the corresponding complexes’ instability. hAR+2-hydroxynaringenin complex showed significantly higher binding free energies. The capacity of 2-hydroxynaringenin to bind to the chosen protein efficiently and form stable protein-ligand complexes was demonstrated by these data, which further validated the compound’s potential. Conclusion In recent years, bioinformatic analysis has become essential for studying the pathogenesis of human diseases. Differential gene expression studies, protein–protein interactions, and network topology analyses were performed. The current study identified the human Androgen Receptor (AR) as a potential drug target to combat TNBC. This was concluded based on gene expression profiling, protein-protein interaction, and network topology analysis. The specific role of the Androgen Receptor in breast cancer growth and progression remains uncertain, although the AR is expressed in approximately 77% of all breast cancers, even higher than Estrogen Receptors (ERs). 31 A more luminal, well-differentiated, and less aggressive tumor may be indicated by high expression of Androgen Receptor in breast cancer, which could improve prognosis. 32 AR inhibition tends to be well-tolerated, and patients with TNBC may benefit from it when paired with other medications, as its toxicity is much lower than that of chemotherapy. Combinations involving mTOR inhibitors, EGFR and other ErbB inhibitors, PIK3 inhibitors, anti-PDL1 antibodies, paclitaxel, and other chemotherapeutic drugs are supported by preclinical results. Randomized clinical trials would be required to ascertain the clinical utility of AR inhibitors. 32 , 36 , 37 Flavonoids are a class of natural compounds found in various fruits, vegetables, and plants and have been extensively studied for their potential therapeutic effects, including their ability to combat cancer. Naringenin, specifically categorized as a flavanone, is a flavonoid present in grapefruit and tomatoes, among other dietary sources. 38 The antioxidant and anti-inflammatory properties of naringenin have led to its exploration for various potential use in the pharmaceutical industry. 39 Limitations of the study The current study identified the human Androgen Receptor as a potential candidate drug target to combat TNBC and recognized 2-hydroxynaringenin as a potential lead molecule. The in vitro and in vivo efficacies of 2-hydroxynaringenin require further investigation. Safety, pharmacokinetics, and pharmacodynamics tests need to be performed to further develop hydroxynaringenin for clinical use. Data availability statement Underlying data 1. GEO DATASET 1 - Accession number- GSE45498 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45498 Platform–GPL16299 2. GEO DATASET 2 - Accession number- GSE214101 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE214101 Extended data Supplementary data 1. Figshare: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer - Supplementary Figures.docx - DOI: 10.6084/m9.figshare.26967880.v1 38 Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). 2. Figshare: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer - Supplementary Table.docx - DOI: 10.6084/m9.figshare.26967733.v1 39 Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). References 1. Arnold M, Morgan E, Rumgay H, et al. : Current and future burden of breast cancer: Global statistics for 2020 and 2040. The Breast. 2022 Dec 1; 66 : 15–23. PubMed Abstract | Publisher Full Text | Free Full Text 2. Bergin AR, Loi S: Triple-negative breast cancer: recent treatment advances. F1000Research. 2019; 8 : 1342. PubMed Abstract | Publisher Full Text | Free Full Text 3. Kalimutho M, Parsons K, Mittal D, et al. : Targeted therapies for triple-negative breast cancer: combating a stubborn disease. Trends in Pharmacological Sciences. 2015 Dec 1; 36 (12): 822–846. PubMed Abstract | Publisher Full Text 4. 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Reference Source Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 24 Oct 2024 ADD YOUR COMMENT Comment Author details Author details 1 Department of Bioinformatics, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, Tamil Nadu, 600116, India 2 Department of Medical Genetics, Manipal Hospitals, Bengaluru, Karnataka, 560 017, India 3 Department of Botany, Gauhati University, Guwahati, Assam, India Pranaya Sankaranarayanan Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation Dicky John Davis G Roles: Investigation, Project Administration, Supervision, Validation, Visualization, Writing – Review & Editing Abhinand PA Roles: Conceptualization, Formal Analysis, Software, Validation, Writing – Review & Editing M Manikandan Roles: Data Curation, Formal Analysis, Software, Visualization Arabinda Ghosh Roles: Formal Analysis, Resources, Software, Writing – Original Draft Preparation Competing interests No competing interests were disclosed. Grant information This work was supported by the Founder-Chancellor Shri. N. P. V. Ramasamy Udayar Research Fellowship (U02B160480), Sri Ramachandra Institute of Higher Education and Research. The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation. Article Versions (2) version 2 Revised Published: 18 Mar 2025, 13:1271 https://doi.org/10.12688/f1000research.155657.2 version 1 Published: 24 Oct 2024, 13:1271 https://doi.org/10.12688/f1000research.155657.1 Copyright © 2025 Sankaranarayanan P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Sankaranarayanan P, G DJD, PA A et al. Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.12688/f1000research.155657.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 18 Mar 2025 Revised Views 0 Cite How to cite this report: Ahmed SSSJ. Reviewer Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.178643.r371506 ) The direct URL for this report is: https://f1000research.com/articles/13-1271/v2#referee-response-371506 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 24 Aug 2025 Shiek S S J Ahmed , Chettinad Academy of Research and Education, Kelambakkam 603103, Tamil Nadu,, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.178643.r371506 The author responded to all comments. If there is no negative ... Continue reading READ ALL The author responded to all comments. If there is no negative report from other reviewers, it can be accepted Competing Interests: No competing interests were disclosed. Reviewer Expertise: Neuroscience, Cancer research, immunoinformatic, systems biology, NGS, artificial intelligence, omics research, Big data analytics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ahmed SSSJ. Reviewer Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.178643.r371506 ) The direct URL for this report is: https://f1000research.com/articles/13-1271/v2#referee-response-371506 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Muthiah I. Reviewer Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.178643.r393020 ) The direct URL for this report is: https://f1000research.com/articles/13-1271/v2#referee-response-393020 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Jul 2025 Indiraleka Muthiah , Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.178643.r393020 The research titled “Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer” effectively presents an in silico strategy to identify potential phytochemical ligands targeting TNBC molecular markers. The study concludes that the ... Continue reading READ ALL The research titled “Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer” effectively presents an in silico strategy to identify potential phytochemical ligands targeting TNBC molecular markers. The study concludes that the androgen receptor (AR) is a promising target and highlights 2-Hydroxynaringenin as a potential lead molecule for AR. The manuscript is well-written, with a clearly defined methodology that effectively addresses the research objective. However, the following suggestions could enhance the manuscript further: The significance of the current research should be emphasised in the background section of the abstract to convey its impact and relevance. Numerous molecular targets and phytochemical ligands for TNBC have been explored through in silico studies. In the introduction, it would strengthen the manuscript to differentiate this study from existing research by outlining its novelty and contribution, supported by up-to-date literature. Additionally, comparing the current results with previously reported studies would provide valuable context. The ligand selection process requires more detailed explanation, including the specific criteria and rationale for choosing the phytochemicals investigated. Since many phytochemicals have been studied against breast cancer, clarifying why only a limited subset was selected here will add clarity. The docking results section could benefit from identifying and discussing the binding sites, particularly in comparison to standard AR inhibitors. The explanation of molecular docking outcomes is currently insufficient, making it challenging to interpret and compare the ligands presented in Table 3. Clarification on whether the ligands are ranked by binding affinity and the reasoning behind selecting 2-Hydroxynaringenin for further study should be included. The discussion section should be expanded to provide a more comprehensive interpretation of the findings, leading to clearer conclusions. It is important to note that while this in silico approach can propose potential lead molecules for AR, confirmation of their efficacy and potential requires further validation through in vitro and in vivo assays. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Research on the design and delivery of drugs for TNBC I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Muthiah I. Reviewer Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.178643.r393020 ) The direct URL for this report is: https://f1000research.com/articles/13-1271/v2#referee-response-393020 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Zou Y. Reviewer Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.178643.r372224 ) The direct URL for this report is: https://f1000research.com/articles/13-1271/v2#referee-response-372224 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Apr 2025 Yutian Zou , Sun Yat-sen University Cancer Center, Guangzhou, China Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.178643.r372224 The manuscript titled “Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer” presents an in silico approach to identify novel therapeutic candidates for triple-negative breast cancer (TNBC). Using differential gene expression analysis ... Continue reading READ ALL The manuscript titled “Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer” presents an in silico approach to identify novel therapeutic candidates for triple-negative breast cancer (TNBC). Using differential gene expression analysis and protein-protein interaction networks, the androgen receptor (AR) was identified as a potential target. Subsequent virtual screening and molecular dynamics simulations suggested that 2-hydroxynaringenin may serve as a promising phytochemical inhibitor of AR. The study demonstrates the structural stability and favorable binding affinity of the 2-hydroxynaringenin–AR complex through MM-GBSA calculations. However, the following issues are required for explaining: The computational identification of 2-hydroxynaringenin as a potential AR inhibitor is interesting, but it is essential to experimentally validate the binding affinity and functional relevance of this interaction in vitro. Cell-based assays using AR-expressing TNBC cell lines should be conducted to confirm the direct binding and biological effect of 2-hydroxynaringenin. The inhibitory effect of 2-hydroxynaringenin on AR activity should be quantified using standard biochemical assays to determine its IC₅₀ value. This quantitative measurement is crucial to evaluate its potency compared to known AR inhibitors. The authors should consider validating their docking and simulation workflow by applying it to known AR inhibitors such as enzalutamide, bicalutamide, and apalutamide. Comparing the binding affinity and stability of these compounds with 2-hydroxynaringenin using the same computational pipeline is important to demonstrate the predictive power of the proposed approach. The manuscript would benefit from a more detailed discussion on how the computational findings could inform future drug development. Specifically, how can the identified phytochemical be optimized or modified for enhanced efficacy and bioavailability? What are the steps for translating this lead compound into preclinical or clinical testing? Essential details of data and analysis in this study remain unclear. For example, there is a lack of details on how docking data were processed and normalized? A reproducible detail should be provided. Some similar studies regarding the docking and cancer should be cited and discussed. For example, PMID: 39912365, 39081282. The current study lacks a discussion on the limitations of in silico-only studies. The authors should acknowledge the absence of experimental validation and outline clear next steps, including in vitro assays, pharmacokinetic evaluation, and possible structural modification of the lead compound to improve drug-like properties. The authors are recommended to consider engaging a professional language editing service to ensure the clarity and coherence of the manuscript. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly References 1. Sultan S, Tawfik SS, Selim KB, Nasr MNA: Design, Synthesis and Molecular Docking of New Thieno[2,3‑d]Pyrimidin-4-One Derivatives as Dual EGFR and FGFR Inhibitors. Drug Dev Res . 2025; 86 (1): e70061 PubMed Abstract | Publisher Full Text 2. Dong XD, Lu Q, Li YD, Cai CY, et al.: RN486, a Bruton's Tyrosine Kinase inhibitor, antagonizes multidrug resistance in ABCG2-overexpressing cancer cells. J Transl Int Med . 2024; 12 (3): 288-298 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: breast cancer I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Zou Y. Reviewer Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.178643.r372224 ) The direct URL for this report is: https://f1000research.com/articles/13-1271/v2#referee-response-372224 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 24 Oct 2024 Views 0 Cite How to cite this report: Ahmed SSSJ. Reviewer Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.170851.r335168 ) The direct URL for this report is: https://f1000research.com/articles/13-1271/v1#referee-response-335168 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 10 Dec 2024 Shiek S S J Ahmed , Chettinad Academy of Research and Education, Kelambakkam 603103, Tamil Nadu,, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.170851.r335168 This manuscript provides valuable insights into drug discovery in the field of oncology. The authors have done excellent work in exploring breast cancer treatment by utilizing efficient computational methods such as differential gene expression, protein network, molecular docking, molecular dynamics ... Continue reading READ ALL This manuscript provides valuable insights into drug discovery in the field of oncology. The authors have done excellent work in exploring breast cancer treatment by utilizing efficient computational methods such as differential gene expression, protein network, molecular docking, molecular dynamics simulations, and MM-GBSA. The study aims to identify potential lead compounds against a candidate target for triple-negative breast cancer. However, the following issues need to be addressed before indexing. 1. Abstract needs to reframed { ( #Note : Can be re-constructed based to authors preference) Example: Background Triple-negative breast cancers (TNBC) are defined as tumors that lack the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It exhibits unique clinical and pathological features, high aggressiveness, and has a relatively poor prognosis and clinical outcome. Objective To identify a novel drug target protein against TNBC and potential phytochemical lead molecules against the identified targets. Methods In this study, we retrieved TNBC samples from NGS and microarray datasets in the Gene Expression Omnibus database. We employed a combination of differential gene expression studies, protein-protein interaction analysis, and network topology investigation to identify the target protein. Additionally, the molecular docking and molecular dynamics (MD) simulation studies followed by Molecular Mechanics with Generalised Born Surface Area salvation was used to identify potential lead molecule. Results The upregulated genes with LogFC > 1.25 and P-value < 0.05 from the TNBC gene expression dataset were identified. Androgen receptor (AR) was found to be an appropriate hub target in the protein-protein interaction network. Phytochemicals that inhibit breast cancer target were retrieved from the PubChem database and virtual screening was performed using PyRx against the AR protein. Thereby, the AR was found to be the target protein and 2-hydroxynaringenin was discovered to be a possible phytochemical lead molecule for combating TNBC. Moreover, the AR and the 2-hydroxynaringenin complex showed structural stability and higher binding affinity through molecular dynamics and MM-GBSA studies. Conclusion AR was identified as a hub protein that is highly expressed in breast cancer and 2-hydroxynaringenin efficacy of counter TNBC requires further investigation both in vitro and in vivo.} please refer to following link for more details https://f1000research.s3.amazonaws.com/linked/695061.155657-F1000_review_1_.docx 2. Please do not use scientific term multiple times. rather use the abbreviation. 3. In the methodology section, the calculation of MD trajectories is unclear. Was the Desmond plug-in employed, or manually? Additionally, there appears to be a contrast between the described MD simulation methodology and the reported results. The MD simulation results might have been obtained using GROMACS rather than Desmond. This assumption arises because Desmond typically calculates parameters such as the Rg and intramolecular hydrogen bonds (intraHB) for the ligand's extendness and internal hydrogen bonding, but not for the entire complex. 4. The authors should clearly define the criteria used for selecting those two datasets (gene expression) in the methodology section. The datasets seems heterogeneous one consists of human sample (expression array) while the other consists of cell line samples (RNA-sequencing). How the authors address and account for batch effects arising from these differences? 4a. The basis for the adopting the LogFC > 1.25 as a threshold while in gene expression analysis should be explained 5. How the 2D interaction of protein and ligand was visualized after docking. 6. In Fig. 5B, the RMSF does not cover the entire protein (263 amino acids). 7. Some important references are missing in the discussion section, particularly in AR association with Breast cancer (second paragraph). 8. In Fig 2, highlighting the hub target protein or constructing the sub-network by centering AR would enhance visualization and helpful for the new readers to understand. 9. The following sentence needs referencing “All RMSD values were below the acceptable range of 3Å”. 10. The interpretation of the MM-GBSA results needs to be clarified. Specifically, the outcomes of the MM-GBSA analysis comparing the test and control compounds should be explained in detail. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Neuroscience, Cancer research, immunoinformatic, systems biology, NGS, artificial intelligence, omics research, Big data analytics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ahmed SSSJ. Reviewer Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.170851.r335168 ) The direct URL for this report is: https://f1000research.com/articles/13-1271/v1#referee-response-335168 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 18 Mar 2025 Pranaya Sankaranarayanan , Department of Bioinformatics, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, 600116, India 18 Mar 2025 Author Response 1. Abstract needs to reframed Response: Thank you for your suggestion. The abstract has been restructured. Background Triple-negative breast cancers (TNBC) are defined as tumors that lack the expression of ... Continue reading 1. Abstract needs to reframed Response: Thank you for your suggestion. The abstract has been restructured. Background Triple-negative breast cancers (TNBC) are defined as tumors that lack the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It exhibits unique clinical and pathological features, demonstrates high aggressiveness, and has a relatively poor prognosis and clinical outcome. Objective To identify a novel drug target protein against TNBC and potential phytochemical lead molecules against the identified targets. Methods In this study, we retrieved TNBC samples from NGS and microarray datasets in the Gene Expression Omnibus database. We employed a combination of differential gene expression studies, protein-protein interaction analysis, and network topology investigation to identify the target protein. Additionally, the molecular docking and molecular dynamics (MD) simulation studies followed by Molecular Mechanics with Generalised Born Surface Area salvation was used to identify potential lead molecule. Results The upregulated genes with LogFC > 1.25 and P-value < 0.05 from the TNBC gene expression dataset were identified. Androgen receptor (AR) was found to be an appropriate hub target in the protein-protein interaction network. Phytochemicals that inhibit breast cancer target were retrieved from the PubChem database and virtual screening was performed using PyRx against the AR protein. Thereby, the AR was found to be the target protein and 2-hydroxynaringenin was discovered to be a possible phytochemical lead molecule for combating TNBC. Moreover, the AR and the 2-hydroxynaringenin complex showed structural stability and higher binding affinity through molecular dynamics and MM-GBSA studies. Conclusion AR was identified as a hub protein that is highly expressed in breast cancer and 2-hydroxynaringenin efficacy of counter TNBC requires further investigation both in vitro and in vivo. 2. Please do not use scientific term multiple times. rather use the abbreviation. Response: Thank you for the input. The changes have been made. 3. In the methodology section, the calculation of MD trajectories is unclear. Was the Desmond plug-in employed, or manually? Additionally, there appears to be a contrast between the described MD simulation methodology and the reported results. The MD simulation results might have been obtained using GROMACS rather than Desmond. This assumption arises because Desmond typically calculates parameters such as the Rg and intramolecular hydrogen bonds (intraHB) for the ligand's extendness and internal hydrogen bonding, but not for the entire complex. Response: In the methodology section it was quite clearly mentioned that Desmond 2020.1 engine was used manually for the study. Moreover, the concern was raised the superiority of GROMACS over Desmond is somehow confusing and as per the suggestion in order to obtain the exact parameters there are options such as simulation event analysis where the hydrogen bonding calculates between the protein and ligand complex. Desmond is also well documented and parameterized simulation engine like GROMACS and there are many reports and previous literatures successfully attributed the specificity of the tool. Fig. 5B, RMSF extracted from the tool outcome and regions having spikes with high and moderate significance exhibited in the figure. The outcome of MMGBSA is compared with the control R18 compound which was co-crytallized inhibitor in the pdb structure of the receptor. 4. The authors should clearly define the criteria used for selecting those two datasets (gene expression) in the methodology section. The datasets seem heterogeneous one consists of human sample (expression array) while the other consists of cell line samples (RNA-sequencing). How the authors address and account for batch effects arising from these differences? Response: We appreciate your insightful comments regarding the dataset selection and batch effect considerations. In our study, we selected the two datasets—GSE45498 (microarray from human tissue samples) and GSE214101 (RNA-seq from cell lines)—based on their relevance to triple-negative breast cancer (TNBC) and their comprehensive gene expression profiles. These datasets were chosen to enable a broader investigation of TNBC-related gene expression variations across different biological contexts. To address potential batch effects arising from differences in platform technology and sample types, we implemented standard normalization and transformation techniques. 4a. The basis for the adopting the LogFC > 1.25 as a threshold while in gene expression analysis should be explained Response: A log₂FC of 1.25 corresponds to a 2.4-fold change in expression, meaning the gene expression is increased by ~140% (if positive) or decreased to ~42% of its original level (if negative). Many biological processes, such as regulatory networks and signalling pathways, involve moderate gene expression changes rather than extreme shifts. Traditional thresholds like log₂FC ≥ 2 (4-fold change) are quite conservative and may exclude relevant genes, especially for genes with small but functional changes. A log₂FC threshold of 1.25 provides a balance—capturing biologically significant changes while avoiding detection of genes with minor fluctuations. Maintaining a balance between sensitivity and specificity : Setting too high a threshold (e.g., log₂FC ≥ 2) might miss key regulatory genes with moderate but meaningful expression changes. A threshold of log₂FC ≥ 1.25, combined with an adjusted p-value (e.g., FDR ≤ 0.05), ensures that identified genes are statistically reliable and biologically relevant. Reducing false positives while capturing meaningful expression shifts : Log₂FC values <1 (e.g., 0.5, which is ~1.4-fold change) might capture noise or small fluctuations due to experimental variability. Using 1.25 as a threshold ensures changes are more likely due to biological regulation rather than technical variation. 5. How the 2D interaction of protein and ligand was visualized after docking Response: By using discovery studio, the 2D interaction of protein and ligand was visualized. 6. In Fig. 5B, the RMSF does not cover the entire protein (263 amino acids). Response: The 3D structure of Androgen Receptor (PDB ID: 1E3G) does not cover the entire protein and contains only the amino acids 671-920 (comprising of 250 amino acids). The manuscript also has been modified to reflect this. 7. Some important references are missing in the discussion section, particularly in AR association with Breast cancer (second paragraph). Response: References have been added 8. In Fig 2, highlighting the hub target protein or constructing the sub-network by centering AR would enhance visualization and helpful for the new readers to understand. Response: Thank you for your valuable suggestion. We have incorporated the changes. https://f1000research.s3.amazonaws.com/linked/707287.155657-Comment_to_reviewer_Sheik_by_Author_Pranaya.pdf 9. The following sentence needs referencing “All RMSD values were below the acceptable range of 3Å”. Response: Reference has been added 10. The interpretation of the MM-GBSA results needs to be clarified. Specifically, the outcomes of the MM-GBSA analysis comparing the test and control compounds should be explained in detail. Response: Utilizing the MD simulation trajectory, the binding free energy along with other contributing energy in form of MM-GBSA were determined for HAR+2-hydroxynaringenin. The results (Table 4) suggested that the maximum contribution to ΔG bind in the stability of the simulated complexes were due to ΔG bind Coulomb, ΔG bind vdW and ΔG bind Lipo, while, ΔG bind Covalent and ΔG bind SolvGB contributed to the instability of the corresponding complexes. The HAR+2-hydroxynaringenin complex showed significantly higher binding free energies. These results supported the potential of 2-hydroxynaringenin, showed the efficiency in binding to the selected protein and the ability to form stable protein-ligand complexes. Binding free energy components for the 1E3G+2-hydroxynaringenin and 1E3G+R18 calculated by MM-GBSA. Table is attached in the manuscript. 1. Abstract needs to reframed Response: Thank you for your suggestion. The abstract has been restructured. Background Triple-negative breast cancers (TNBC) are defined as tumors that lack the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It exhibits unique clinical and pathological features, demonstrates high aggressiveness, and has a relatively poor prognosis and clinical outcome. Objective To identify a novel drug target protein against TNBC and potential phytochemical lead molecules against the identified targets. Methods In this study, we retrieved TNBC samples from NGS and microarray datasets in the Gene Expression Omnibus database. We employed a combination of differential gene expression studies, protein-protein interaction analysis, and network topology investigation to identify the target protein. Additionally, the molecular docking and molecular dynamics (MD) simulation studies followed by Molecular Mechanics with Generalised Born Surface Area salvation was used to identify potential lead molecule. Results The upregulated genes with LogFC > 1.25 and P-value < 0.05 from the TNBC gene expression dataset were identified. Androgen receptor (AR) was found to be an appropriate hub target in the protein-protein interaction network. Phytochemicals that inhibit breast cancer target were retrieved from the PubChem database and virtual screening was performed using PyRx against the AR protein. Thereby, the AR was found to be the target protein and 2-hydroxynaringenin was discovered to be a possible phytochemical lead molecule for combating TNBC. Moreover, the AR and the 2-hydroxynaringenin complex showed structural stability and higher binding affinity through molecular dynamics and MM-GBSA studies. Conclusion AR was identified as a hub protein that is highly expressed in breast cancer and 2-hydroxynaringenin efficacy of counter TNBC requires further investigation both in vitro and in vivo. 2. Please do not use scientific term multiple times. rather use the abbreviation. Response: Thank you for the input. The changes have been made. 3. In the methodology section, the calculation of MD trajectories is unclear. Was the Desmond plug-in employed, or manually? Additionally, there appears to be a contrast between the described MD simulation methodology and the reported results. The MD simulation results might have been obtained using GROMACS rather than Desmond. This assumption arises because Desmond typically calculates parameters such as the Rg and intramolecular hydrogen bonds (intraHB) for the ligand's extendness and internal hydrogen bonding, but not for the entire complex. Response: In the methodology section it was quite clearly mentioned that Desmond 2020.1 engine was used manually for the study. Moreover, the concern was raised the superiority of GROMACS over Desmond is somehow confusing and as per the suggestion in order to obtain the exact parameters there are options such as simulation event analysis where the hydrogen bonding calculates between the protein and ligand complex. Desmond is also well documented and parameterized simulation engine like GROMACS and there are many reports and previous literatures successfully attributed the specificity of the tool. Fig. 5B, RMSF extracted from the tool outcome and regions having spikes with high and moderate significance exhibited in the figure. The outcome of MMGBSA is compared with the control R18 compound which was co-crytallized inhibitor in the pdb structure of the receptor. 4. The authors should clearly define the criteria used for selecting those two datasets (gene expression) in the methodology section. The datasets seem heterogeneous one consists of human sample (expression array) while the other consists of cell line samples (RNA-sequencing). How the authors address and account for batch effects arising from these differences? Response: We appreciate your insightful comments regarding the dataset selection and batch effect considerations. In our study, we selected the two datasets—GSE45498 (microarray from human tissue samples) and GSE214101 (RNA-seq from cell lines)—based on their relevance to triple-negative breast cancer (TNBC) and their comprehensive gene expression profiles. These datasets were chosen to enable a broader investigation of TNBC-related gene expression variations across different biological contexts. To address potential batch effects arising from differences in platform technology and sample types, we implemented standard normalization and transformation techniques. 4a. The basis for the adopting the LogFC > 1.25 as a threshold while in gene expression analysis should be explained Response: A log₂FC of 1.25 corresponds to a 2.4-fold change in expression, meaning the gene expression is increased by ~140% (if positive) or decreased to ~42% of its original level (if negative). Many biological processes, such as regulatory networks and signalling pathways, involve moderate gene expression changes rather than extreme shifts. Traditional thresholds like log₂FC ≥ 2 (4-fold change) are quite conservative and may exclude relevant genes, especially for genes with small but functional changes. A log₂FC threshold of 1.25 provides a balance—capturing biologically significant changes while avoiding detection of genes with minor fluctuations. Maintaining a balance between sensitivity and specificity : Setting too high a threshold (e.g., log₂FC ≥ 2) might miss key regulatory genes with moderate but meaningful expression changes. A threshold of log₂FC ≥ 1.25, combined with an adjusted p-value (e.g., FDR ≤ 0.05), ensures that identified genes are statistically reliable and biologically relevant. Reducing false positives while capturing meaningful expression shifts : Log₂FC values <1 (e.g., 0.5, which is ~1.4-fold change) might capture noise or small fluctuations due to experimental variability. Using 1.25 as a threshold ensures changes are more likely due to biological regulation rather than technical variation. 5. How the 2D interaction of protein and ligand was visualized after docking Response: By using discovery studio, the 2D interaction of protein and ligand was visualized. 6. In Fig. 5B, the RMSF does not cover the entire protein (263 amino acids). Response: The 3D structure of Androgen Receptor (PDB ID: 1E3G) does not cover the entire protein and contains only the amino acids 671-920 (comprising of 250 amino acids). The manuscript also has been modified to reflect this. 7. Some important references are missing in the discussion section, particularly in AR association with Breast cancer (second paragraph). Response: References have been added 8. In Fig 2, highlighting the hub target protein or constructing the sub-network by centering AR would enhance visualization and helpful for the new readers to understand. Response: Thank you for your valuable suggestion. We have incorporated the changes. https://f1000research.s3.amazonaws.com/linked/707287.155657-Comment_to_reviewer_Sheik_by_Author_Pranaya.pdf 9. The following sentence needs referencing “All RMSD values were below the acceptable range of 3Å”. Response: Reference has been added 10. The interpretation of the MM-GBSA results needs to be clarified. Specifically, the outcomes of the MM-GBSA analysis comparing the test and control compounds should be explained in detail. Response: Utilizing the MD simulation trajectory, the binding free energy along with other contributing energy in form of MM-GBSA were determined for HAR+2-hydroxynaringenin. The results (Table 4) suggested that the maximum contribution to ΔG bind in the stability of the simulated complexes were due to ΔG bind Coulomb, ΔG bind vdW and ΔG bind Lipo, while, ΔG bind Covalent and ΔG bind SolvGB contributed to the instability of the corresponding complexes. The HAR+2-hydroxynaringenin complex showed significantly higher binding free energies. These results supported the potential of 2-hydroxynaringenin, showed the efficiency in binding to the selected protein and the ability to form stable protein-ligand complexes. Binding free energy components for the 1E3G+2-hydroxynaringenin and 1E3G+R18 calculated by MM-GBSA. Table is attached in the manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 18 Mar 2025 Pranaya Sankaranarayanan , Department of Bioinformatics, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, 600116, India 18 Mar 2025 Author Response 1. Abstract needs to reframed Response: Thank you for your suggestion. The abstract has been restructured. Background Triple-negative breast cancers (TNBC) are defined as tumors that lack the expression of ... Continue reading 1. Abstract needs to reframed Response: Thank you for your suggestion. The abstract has been restructured. Background Triple-negative breast cancers (TNBC) are defined as tumors that lack the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It exhibits unique clinical and pathological features, demonstrates high aggressiveness, and has a relatively poor prognosis and clinical outcome. Objective To identify a novel drug target protein against TNBC and potential phytochemical lead molecules against the identified targets. Methods In this study, we retrieved TNBC samples from NGS and microarray datasets in the Gene Expression Omnibus database. We employed a combination of differential gene expression studies, protein-protein interaction analysis, and network topology investigation to identify the target protein. Additionally, the molecular docking and molecular dynamics (MD) simulation studies followed by Molecular Mechanics with Generalised Born Surface Area salvation was used to identify potential lead molecule. Results The upregulated genes with LogFC > 1.25 and P-value < 0.05 from the TNBC gene expression dataset were identified. Androgen receptor (AR) was found to be an appropriate hub target in the protein-protein interaction network. Phytochemicals that inhibit breast cancer target were retrieved from the PubChem database and virtual screening was performed using PyRx against the AR protein. Thereby, the AR was found to be the target protein and 2-hydroxynaringenin was discovered to be a possible phytochemical lead molecule for combating TNBC. Moreover, the AR and the 2-hydroxynaringenin complex showed structural stability and higher binding affinity through molecular dynamics and MM-GBSA studies. Conclusion AR was identified as a hub protein that is highly expressed in breast cancer and 2-hydroxynaringenin efficacy of counter TNBC requires further investigation both in vitro and in vivo. 2. Please do not use scientific term multiple times. rather use the abbreviation. Response: Thank you for the input. The changes have been made. 3. In the methodology section, the calculation of MD trajectories is unclear. Was the Desmond plug-in employed, or manually? Additionally, there appears to be a contrast between the described MD simulation methodology and the reported results. The MD simulation results might have been obtained using GROMACS rather than Desmond. This assumption arises because Desmond typically calculates parameters such as the Rg and intramolecular hydrogen bonds (intraHB) for the ligand's extendness and internal hydrogen bonding, but not for the entire complex. Response: In the methodology section it was quite clearly mentioned that Desmond 2020.1 engine was used manually for the study. Moreover, the concern was raised the superiority of GROMACS over Desmond is somehow confusing and as per the suggestion in order to obtain the exact parameters there are options such as simulation event analysis where the hydrogen bonding calculates between the protein and ligand complex. Desmond is also well documented and parameterized simulation engine like GROMACS and there are many reports and previous literatures successfully attributed the specificity of the tool. Fig. 5B, RMSF extracted from the tool outcome and regions having spikes with high and moderate significance exhibited in the figure. The outcome of MMGBSA is compared with the control R18 compound which was co-crytallized inhibitor in the pdb structure of the receptor. 4. The authors should clearly define the criteria used for selecting those two datasets (gene expression) in the methodology section. The datasets seem heterogeneous one consists of human sample (expression array) while the other consists of cell line samples (RNA-sequencing). How the authors address and account for batch effects arising from these differences? Response: We appreciate your insightful comments regarding the dataset selection and batch effect considerations. In our study, we selected the two datasets—GSE45498 (microarray from human tissue samples) and GSE214101 (RNA-seq from cell lines)—based on their relevance to triple-negative breast cancer (TNBC) and their comprehensive gene expression profiles. These datasets were chosen to enable a broader investigation of TNBC-related gene expression variations across different biological contexts. To address potential batch effects arising from differences in platform technology and sample types, we implemented standard normalization and transformation techniques. 4a. The basis for the adopting the LogFC > 1.25 as a threshold while in gene expression analysis should be explained Response: A log₂FC of 1.25 corresponds to a 2.4-fold change in expression, meaning the gene expression is increased by ~140% (if positive) or decreased to ~42% of its original level (if negative). Many biological processes, such as regulatory networks and signalling pathways, involve moderate gene expression changes rather than extreme shifts. Traditional thresholds like log₂FC ≥ 2 (4-fold change) are quite conservative and may exclude relevant genes, especially for genes with small but functional changes. A log₂FC threshold of 1.25 provides a balance—capturing biologically significant changes while avoiding detection of genes with minor fluctuations. Maintaining a balance between sensitivity and specificity : Setting too high a threshold (e.g., log₂FC ≥ 2) might miss key regulatory genes with moderate but meaningful expression changes. A threshold of log₂FC ≥ 1.25, combined with an adjusted p-value (e.g., FDR ≤ 0.05), ensures that identified genes are statistically reliable and biologically relevant. Reducing false positives while capturing meaningful expression shifts : Log₂FC values <1 (e.g., 0.5, which is ~1.4-fold change) might capture noise or small fluctuations due to experimental variability. Using 1.25 as a threshold ensures changes are more likely due to biological regulation rather than technical variation. 5. How the 2D interaction of protein and ligand was visualized after docking Response: By using discovery studio, the 2D interaction of protein and ligand was visualized. 6. In Fig. 5B, the RMSF does not cover the entire protein (263 amino acids). Response: The 3D structure of Androgen Receptor (PDB ID: 1E3G) does not cover the entire protein and contains only the amino acids 671-920 (comprising of 250 amino acids). The manuscript also has been modified to reflect this. 7. Some important references are missing in the discussion section, particularly in AR association with Breast cancer (second paragraph). Response: References have been added 8. In Fig 2, highlighting the hub target protein or constructing the sub-network by centering AR would enhance visualization and helpful for the new readers to understand. Response: Thank you for your valuable suggestion. We have incorporated the changes. https://f1000research.s3.amazonaws.com/linked/707287.155657-Comment_to_reviewer_Sheik_by_Author_Pranaya.pdf 9. The following sentence needs referencing “All RMSD values were below the acceptable range of 3Å”. Response: Reference has been added 10. The interpretation of the MM-GBSA results needs to be clarified. Specifically, the outcomes of the MM-GBSA analysis comparing the test and control compounds should be explained in detail. Response: Utilizing the MD simulation trajectory, the binding free energy along with other contributing energy in form of MM-GBSA were determined for HAR+2-hydroxynaringenin. The results (Table 4) suggested that the maximum contribution to ΔG bind in the stability of the simulated complexes were due to ΔG bind Coulomb, ΔG bind vdW and ΔG bind Lipo, while, ΔG bind Covalent and ΔG bind SolvGB contributed to the instability of the corresponding complexes. The HAR+2-hydroxynaringenin complex showed significantly higher binding free energies. These results supported the potential of 2-hydroxynaringenin, showed the efficiency in binding to the selected protein and the ability to form stable protein-ligand complexes. Binding free energy components for the 1E3G+2-hydroxynaringenin and 1E3G+R18 calculated by MM-GBSA. Table is attached in the manuscript. 1. Abstract needs to reframed Response: Thank you for your suggestion. The abstract has been restructured. Background Triple-negative breast cancers (TNBC) are defined as tumors that lack the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It exhibits unique clinical and pathological features, demonstrates high aggressiveness, and has a relatively poor prognosis and clinical outcome. Objective To identify a novel drug target protein against TNBC and potential phytochemical lead molecules against the identified targets. Methods In this study, we retrieved TNBC samples from NGS and microarray datasets in the Gene Expression Omnibus database. We employed a combination of differential gene expression studies, protein-protein interaction analysis, and network topology investigation to identify the target protein. Additionally, the molecular docking and molecular dynamics (MD) simulation studies followed by Molecular Mechanics with Generalised Born Surface Area salvation was used to identify potential lead molecule. Results The upregulated genes with LogFC > 1.25 and P-value < 0.05 from the TNBC gene expression dataset were identified. Androgen receptor (AR) was found to be an appropriate hub target in the protein-protein interaction network. Phytochemicals that inhibit breast cancer target were retrieved from the PubChem database and virtual screening was performed using PyRx against the AR protein. Thereby, the AR was found to be the target protein and 2-hydroxynaringenin was discovered to be a possible phytochemical lead molecule for combating TNBC. Moreover, the AR and the 2-hydroxynaringenin complex showed structural stability and higher binding affinity through molecular dynamics and MM-GBSA studies. Conclusion AR was identified as a hub protein that is highly expressed in breast cancer and 2-hydroxynaringenin efficacy of counter TNBC requires further investigation both in vitro and in vivo. 2. Please do not use scientific term multiple times. rather use the abbreviation. Response: Thank you for the input. The changes have been made. 3. In the methodology section, the calculation of MD trajectories is unclear. Was the Desmond plug-in employed, or manually? Additionally, there appears to be a contrast between the described MD simulation methodology and the reported results. The MD simulation results might have been obtained using GROMACS rather than Desmond. This assumption arises because Desmond typically calculates parameters such as the Rg and intramolecular hydrogen bonds (intraHB) for the ligand's extendness and internal hydrogen bonding, but not for the entire complex. Response: In the methodology section it was quite clearly mentioned that Desmond 2020.1 engine was used manually for the study. Moreover, the concern was raised the superiority of GROMACS over Desmond is somehow confusing and as per the suggestion in order to obtain the exact parameters there are options such as simulation event analysis where the hydrogen bonding calculates between the protein and ligand complex. Desmond is also well documented and parameterized simulation engine like GROMACS and there are many reports and previous literatures successfully attributed the specificity of the tool. Fig. 5B, RMSF extracted from the tool outcome and regions having spikes with high and moderate significance exhibited in the figure. The outcome of MMGBSA is compared with the control R18 compound which was co-crytallized inhibitor in the pdb structure of the receptor. 4. The authors should clearly define the criteria used for selecting those two datasets (gene expression) in the methodology section. The datasets seem heterogeneous one consists of human sample (expression array) while the other consists of cell line samples (RNA-sequencing). How the authors address and account for batch effects arising from these differences? Response: We appreciate your insightful comments regarding the dataset selection and batch effect considerations. In our study, we selected the two datasets—GSE45498 (microarray from human tissue samples) and GSE214101 (RNA-seq from cell lines)—based on their relevance to triple-negative breast cancer (TNBC) and their comprehensive gene expression profiles. These datasets were chosen to enable a broader investigation of TNBC-related gene expression variations across different biological contexts. To address potential batch effects arising from differences in platform technology and sample types, we implemented standard normalization and transformation techniques. 4a. The basis for the adopting the LogFC > 1.25 as a threshold while in gene expression analysis should be explained Response: A log₂FC of 1.25 corresponds to a 2.4-fold change in expression, meaning the gene expression is increased by ~140% (if positive) or decreased to ~42% of its original level (if negative). Many biological processes, such as regulatory networks and signalling pathways, involve moderate gene expression changes rather than extreme shifts. Traditional thresholds like log₂FC ≥ 2 (4-fold change) are quite conservative and may exclude relevant genes, especially for genes with small but functional changes. A log₂FC threshold of 1.25 provides a balance—capturing biologically significant changes while avoiding detection of genes with minor fluctuations. Maintaining a balance between sensitivity and specificity : Setting too high a threshold (e.g., log₂FC ≥ 2) might miss key regulatory genes with moderate but meaningful expression changes. A threshold of log₂FC ≥ 1.25, combined with an adjusted p-value (e.g., FDR ≤ 0.05), ensures that identified genes are statistically reliable and biologically relevant. Reducing false positives while capturing meaningful expression shifts : Log₂FC values <1 (e.g., 0.5, which is ~1.4-fold change) might capture noise or small fluctuations due to experimental variability. Using 1.25 as a threshold ensures changes are more likely due to biological regulation rather than technical variation. 5. How the 2D interaction of protein and ligand was visualized after docking Response: By using discovery studio, the 2D interaction of protein and ligand was visualized. 6. In Fig. 5B, the RMSF does not cover the entire protein (263 amino acids). Response: The 3D structure of Androgen Receptor (PDB ID: 1E3G) does not cover the entire protein and contains only the amino acids 671-920 (comprising of 250 amino acids). The manuscript also has been modified to reflect this. 7. Some important references are missing in the discussion section, particularly in AR association with Breast cancer (second paragraph). Response: References have been added 8. In Fig 2, highlighting the hub target protein or constructing the sub-network by centering AR would enhance visualization and helpful for the new readers to understand. Response: Thank you for your valuable suggestion. We have incorporated the changes. https://f1000research.s3.amazonaws.com/linked/707287.155657-Comment_to_reviewer_Sheik_by_Author_Pranaya.pdf 9. The following sentence needs referencing “All RMSD values were below the acceptable range of 3Å”. Response: Reference has been added 10. The interpretation of the MM-GBSA results needs to be clarified. Specifically, the outcomes of the MM-GBSA analysis comparing the test and control compounds should be explained in detail. Response: Utilizing the MD simulation trajectory, the binding free energy along with other contributing energy in form of MM-GBSA were determined for HAR+2-hydroxynaringenin. The results (Table 4) suggested that the maximum contribution to ΔG bind in the stability of the simulated complexes were due to ΔG bind Coulomb, ΔG bind vdW and ΔG bind Lipo, while, ΔG bind Covalent and ΔG bind SolvGB contributed to the instability of the corresponding complexes. The HAR+2-hydroxynaringenin complex showed significantly higher binding free energies. These results supported the potential of 2-hydroxynaringenin, showed the efficiency in binding to the selected protein and the ability to form stable protein-ligand complexes. Binding free energy components for the 1E3G+2-hydroxynaringenin and 1E3G+R18 calculated by MM-GBSA. Table is attached in the manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 24 Oct 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 2 (revision) 18 Mar 25 read read read Version 1 24 Oct 24 read Shiek S S J Ahmed , Chettinad Academy of Research and Education, Kelambakkam 603103, Tamil Nadu,, India Yutian Zou , Sun Yat-sen University Cancer Center, Guangzhou, China Indiraleka Muthiah , Mepco Schlenk Engineering College, Sivakasi, India Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Ahmed S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 24 Aug 2025 | for Version 2 Shiek S S J Ahmed , Chettinad Academy of Research and Education, Kelambakkam 603103, Tamil Nadu,, India 0 Views copyright © 2025 Ahmed S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The author responded to all comments. If there is no negative report from other reviewers, it can be accepted Competing Interests No competing interests were disclosed. Reviewer Expertise Neuroscience, Cancer research, immunoinformatic, systems biology, NGS, artificial intelligence, omics research, Big data analytics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Ahmed SSSJ. Peer Review Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.178643.r371506) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1271/v2#referee-response-371506 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Muthiah I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Jul 2025 | for Version 2 Indiraleka Muthiah , Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India 0 Views copyright © 2025 Muthiah I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The research titled “Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer” effectively presents an in silico strategy to identify potential phytochemical ligands targeting TNBC molecular markers. The study concludes that the androgen receptor (AR) is a promising target and highlights 2-Hydroxynaringenin as a potential lead molecule for AR. The manuscript is well-written, with a clearly defined methodology that effectively addresses the research objective. However, the following suggestions could enhance the manuscript further: The significance of the current research should be emphasised in the background section of the abstract to convey its impact and relevance. Numerous molecular targets and phytochemical ligands for TNBC have been explored through in silico studies. In the introduction, it would strengthen the manuscript to differentiate this study from existing research by outlining its novelty and contribution, supported by up-to-date literature. Additionally, comparing the current results with previously reported studies would provide valuable context. The ligand selection process requires more detailed explanation, including the specific criteria and rationale for choosing the phytochemicals investigated. Since many phytochemicals have been studied against breast cancer, clarifying why only a limited subset was selected here will add clarity. The docking results section could benefit from identifying and discussing the binding sites, particularly in comparison to standard AR inhibitors. The explanation of molecular docking outcomes is currently insufficient, making it challenging to interpret and compare the ligands presented in Table 3. Clarification on whether the ligands are ranked by binding affinity and the reasoning behind selecting 2-Hydroxynaringenin for further study should be included. The discussion section should be expanded to provide a more comprehensive interpretation of the findings, leading to clearer conclusions. It is important to note that while this in silico approach can propose potential lead molecules for AR, confirmation of their efficacy and potential requires further validation through in vitro and in vivo assays. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Research on the design and delivery of drugs for TNBC I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Muthiah I. Peer Review Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.178643.r393020) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1271/v2#referee-response-393020 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Zou Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Apr 2025 | for Version 2 Yutian Zou , Sun Yat-sen University Cancer Center, Guangzhou, China 0 Views copyright © 2025 Zou Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript titled “Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer” presents an in silico approach to identify novel therapeutic candidates for triple-negative breast cancer (TNBC). Using differential gene expression analysis and protein-protein interaction networks, the androgen receptor (AR) was identified as a potential target. Subsequent virtual screening and molecular dynamics simulations suggested that 2-hydroxynaringenin may serve as a promising phytochemical inhibitor of AR. The study demonstrates the structural stability and favorable binding affinity of the 2-hydroxynaringenin–AR complex through MM-GBSA calculations. However, the following issues are required for explaining: The computational identification of 2-hydroxynaringenin as a potential AR inhibitor is interesting, but it is essential to experimentally validate the binding affinity and functional relevance of this interaction in vitro. Cell-based assays using AR-expressing TNBC cell lines should be conducted to confirm the direct binding and biological effect of 2-hydroxynaringenin. The inhibitory effect of 2-hydroxynaringenin on AR activity should be quantified using standard biochemical assays to determine its IC₅₀ value. This quantitative measurement is crucial to evaluate its potency compared to known AR inhibitors. The authors should consider validating their docking and simulation workflow by applying it to known AR inhibitors such as enzalutamide, bicalutamide, and apalutamide. Comparing the binding affinity and stability of these compounds with 2-hydroxynaringenin using the same computational pipeline is important to demonstrate the predictive power of the proposed approach. The manuscript would benefit from a more detailed discussion on how the computational findings could inform future drug development. Specifically, how can the identified phytochemical be optimized or modified for enhanced efficacy and bioavailability? What are the steps for translating this lead compound into preclinical or clinical testing? Essential details of data and analysis in this study remain unclear. For example, there is a lack of details on how docking data were processed and normalized? A reproducible detail should be provided. Some similar studies regarding the docking and cancer should be cited and discussed. For example, PMID: 39912365, 39081282. The current study lacks a discussion on the limitations of in silico-only studies. The authors should acknowledge the absence of experimental validation and outline clear next steps, including in vitro assays, pharmacokinetic evaluation, and possible structural modification of the lead compound to improve drug-like properties. The authors are recommended to consider engaging a professional language editing service to ensure the clarity and coherence of the manuscript. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly References 1. Sultan S, Tawfik SS, Selim KB, Nasr MNA: Design, Synthesis and Molecular Docking of New Thieno[2,3‑d]Pyrimidin-4-One Derivatives as Dual EGFR and FGFR Inhibitors. Drug Dev Res . 2025; 86 (1): e70061 PubMed Abstract | Publisher Full Text 2. Dong XD, Lu Q, Li YD, Cai CY, et al.: RN486, a Bruton's Tyrosine Kinase inhibitor, antagonizes multidrug resistance in ABCG2-overexpressing cancer cells. J Transl Int Med . 2024; 12 (3): 288-298 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise breast cancer I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Zou Y. Peer Review Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.178643.r372224) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1271/v2#referee-response-372224 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Ahmed S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 Dec 2024 | for Version 1 Shiek S S J Ahmed , Chettinad Academy of Research and Education, Kelambakkam 603103, Tamil Nadu,, India 0 Views copyright © 2024 Ahmed S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript provides valuable insights into drug discovery in the field of oncology. The authors have done excellent work in exploring breast cancer treatment by utilizing efficient computational methods such as differential gene expression, protein network, molecular docking, molecular dynamics simulations, and MM-GBSA. The study aims to identify potential lead compounds against a candidate target for triple-negative breast cancer. However, the following issues need to be addressed before indexing. 1. Abstract needs to reframed { ( #Note : Can be re-constructed based to authors preference) Example: Background Triple-negative breast cancers (TNBC) are defined as tumors that lack the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It exhibits unique clinical and pathological features, high aggressiveness, and has a relatively poor prognosis and clinical outcome. Objective To identify a novel drug target protein against TNBC and potential phytochemical lead molecules against the identified targets. Methods In this study, we retrieved TNBC samples from NGS and microarray datasets in the Gene Expression Omnibus database. We employed a combination of differential gene expression studies, protein-protein interaction analysis, and network topology investigation to identify the target protein. Additionally, the molecular docking and molecular dynamics (MD) simulation studies followed by Molecular Mechanics with Generalised Born Surface Area salvation was used to identify potential lead molecule. Results The upregulated genes with LogFC > 1.25 and P-value < 0.05 from the TNBC gene expression dataset were identified. Androgen receptor (AR) was found to be an appropriate hub target in the protein-protein interaction network. Phytochemicals that inhibit breast cancer target were retrieved from the PubChem database and virtual screening was performed using PyRx against the AR protein. Thereby, the AR was found to be the target protein and 2-hydroxynaringenin was discovered to be a possible phytochemical lead molecule for combating TNBC. Moreover, the AR and the 2-hydroxynaringenin complex showed structural stability and higher binding affinity through molecular dynamics and MM-GBSA studies. Conclusion AR was identified as a hub protein that is highly expressed in breast cancer and 2-hydroxynaringenin efficacy of counter TNBC requires further investigation both in vitro and in vivo.} please refer to following link for more details https://f1000research.s3.amazonaws.com/linked/695061.155657-F1000_review_1_.docx 2. Please do not use scientific term multiple times. rather use the abbreviation. 3. In the methodology section, the calculation of MD trajectories is unclear. Was the Desmond plug-in employed, or manually? Additionally, there appears to be a contrast between the described MD simulation methodology and the reported results. The MD simulation results might have been obtained using GROMACS rather than Desmond. This assumption arises because Desmond typically calculates parameters such as the Rg and intramolecular hydrogen bonds (intraHB) for the ligand's extendness and internal hydrogen bonding, but not for the entire complex. 4. The authors should clearly define the criteria used for selecting those two datasets (gene expression) in the methodology section. The datasets seems heterogeneous one consists of human sample (expression array) while the other consists of cell line samples (RNA-sequencing). How the authors address and account for batch effects arising from these differences? 4a. The basis for the adopting the LogFC > 1.25 as a threshold while in gene expression analysis should be explained 5. How the 2D interaction of protein and ligand was visualized after docking. 6. In Fig. 5B, the RMSF does not cover the entire protein (263 amino acids). 7. Some important references are missing in the discussion section, particularly in AR association with Breast cancer (second paragraph). 8. In Fig 2, highlighting the hub target protein or constructing the sub-network by centering AR would enhance visualization and helpful for the new readers to understand. 9. The following sentence needs referencing “All RMSD values were below the acceptable range of 3Å”. 10. The interpretation of the MM-GBSA results needs to be clarified. Specifically, the outcomes of the MM-GBSA analysis comparing the test and control compounds should be explained in detail. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Neuroscience, Cancer research, immunoinformatic, systems biology, NGS, artificial intelligence, omics research, Big data analytics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 18 Mar 2025 Pranaya Sankaranarayanan , Department of Bioinformatics, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, 600116, India 1. Abstract needs to reframed Response: Thank you for your suggestion. The abstract has been restructured. Background Triple-negative breast cancers (TNBC) are defined as tumors that lack the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It exhibits unique clinical and pathological features, demonstrates high aggressiveness, and has a relatively poor prognosis and clinical outcome. Objective To identify a novel drug target protein against TNBC and potential phytochemical lead molecules against the identified targets. Methods In this study, we retrieved TNBC samples from NGS and microarray datasets in the Gene Expression Omnibus database. We employed a combination of differential gene expression studies, protein-protein interaction analysis, and network topology investigation to identify the target protein. Additionally, the molecular docking and molecular dynamics (MD) simulation studies followed by Molecular Mechanics with Generalised Born Surface Area salvation was used to identify potential lead molecule. Results The upregulated genes with LogFC > 1.25 and P-value < 0.05 from the TNBC gene expression dataset were identified. Androgen receptor (AR) was found to be an appropriate hub target in the protein-protein interaction network. Phytochemicals that inhibit breast cancer target were retrieved from the PubChem database and virtual screening was performed using PyRx against the AR protein. Thereby, the AR was found to be the target protein and 2-hydroxynaringenin was discovered to be a possible phytochemical lead molecule for combating TNBC. Moreover, the AR and the 2-hydroxynaringenin complex showed structural stability and higher binding affinity through molecular dynamics and MM-GBSA studies. Conclusion AR was identified as a hub protein that is highly expressed in breast cancer and 2-hydroxynaringenin efficacy of counter TNBC requires further investigation both in vitro and in vivo. 2. Please do not use scientific term multiple times. rather use the abbreviation. Response: Thank you for the input. The changes have been made. 3. In the methodology section, the calculation of MD trajectories is unclear. Was the Desmond plug-in employed, or manually? Additionally, there appears to be a contrast between the described MD simulation methodology and the reported results. The MD simulation results might have been obtained using GROMACS rather than Desmond. This assumption arises because Desmond typically calculates parameters such as the Rg and intramolecular hydrogen bonds (intraHB) for the ligand's extendness and internal hydrogen bonding, but not for the entire complex. Response: In the methodology section it was quite clearly mentioned that Desmond 2020.1 engine was used manually for the study. Moreover, the concern was raised the superiority of GROMACS over Desmond is somehow confusing and as per the suggestion in order to obtain the exact parameters there are options such as simulation event analysis where the hydrogen bonding calculates between the protein and ligand complex. Desmond is also well documented and parameterized simulation engine like GROMACS and there are many reports and previous literatures successfully attributed the specificity of the tool. Fig. 5B, RMSF extracted from the tool outcome and regions having spikes with high and moderate significance exhibited in the figure. The outcome of MMGBSA is compared with the control R18 compound which was co-crytallized inhibitor in the pdb structure of the receptor. 4. The authors should clearly define the criteria used for selecting those two datasets (gene expression) in the methodology section. The datasets seem heterogeneous one consists of human sample (expression array) while the other consists of cell line samples (RNA-sequencing). How the authors address and account for batch effects arising from these differences? Response: We appreciate your insightful comments regarding the dataset selection and batch effect considerations. In our study, we selected the two datasets—GSE45498 (microarray from human tissue samples) and GSE214101 (RNA-seq from cell lines)—based on their relevance to triple-negative breast cancer (TNBC) and their comprehensive gene expression profiles. These datasets were chosen to enable a broader investigation of TNBC-related gene expression variations across different biological contexts. To address potential batch effects arising from differences in platform technology and sample types, we implemented standard normalization and transformation techniques. 4a. The basis for the adopting the LogFC > 1.25 as a threshold while in gene expression analysis should be explained Response: A log₂FC of 1.25 corresponds to a 2.4-fold change in expression, meaning the gene expression is increased by ~140% (if positive) or decreased to ~42% of its original level (if negative). Many biological processes, such as regulatory networks and signalling pathways, involve moderate gene expression changes rather than extreme shifts. Traditional thresholds like log₂FC ≥ 2 (4-fold change) are quite conservative and may exclude relevant genes, especially for genes with small but functional changes. A log₂FC threshold of 1.25 provides a balance—capturing biologically significant changes while avoiding detection of genes with minor fluctuations. Maintaining a balance between sensitivity and specificity : Setting too high a threshold (e.g., log₂FC ≥ 2) might miss key regulatory genes with moderate but meaningful expression changes. A threshold of log₂FC ≥ 1.25, combined with an adjusted p-value (e.g., FDR ≤ 0.05), ensures that identified genes are statistically reliable and biologically relevant. Reducing false positives while capturing meaningful expression shifts : Log₂FC values <1 (e.g., 0.5, which is ~1.4-fold change) might capture noise or small fluctuations due to experimental variability. Using 1.25 as a threshold ensures changes are more likely due to biological regulation rather than technical variation. 5. How the 2D interaction of protein and ligand was visualized after docking Response: By using discovery studio, the 2D interaction of protein and ligand was visualized. 6. In Fig. 5B, the RMSF does not cover the entire protein (263 amino acids). Response: The 3D structure of Androgen Receptor (PDB ID: 1E3G) does not cover the entire protein and contains only the amino acids 671-920 (comprising of 250 amino acids). The manuscript also has been modified to reflect this. 7. Some important references are missing in the discussion section, particularly in AR association with Breast cancer (second paragraph). Response: References have been added 8. In Fig 2, highlighting the hub target protein or constructing the sub-network by centering AR would enhance visualization and helpful for the new readers to understand. Response: Thank you for your valuable suggestion. We have incorporated the changes. https://f1000research.s3.amazonaws.com/linked/707287.155657-Comment_to_reviewer_Sheik_by_Author_Pranaya.pdf 9. The following sentence needs referencing “All RMSD values were below the acceptable range of 3Å”. Response: Reference has been added 10. The interpretation of the MM-GBSA results needs to be clarified. Specifically, the outcomes of the MM-GBSA analysis comparing the test and control compounds should be explained in detail. Response: Utilizing the MD simulation trajectory, the binding free energy along with other contributing energy in form of MM-GBSA were determined for HAR+2-hydroxynaringenin. The results (Table 4) suggested that the maximum contribution to ΔG bind in the stability of the simulated complexes were due to ΔG bind Coulomb, ΔG bind vdW and ΔG bind Lipo, while, ΔG bind Covalent and ΔG bind SolvGB contributed to the instability of the corresponding complexes. The HAR+2-hydroxynaringenin complex showed significantly higher binding free energies. These results supported the potential of 2-hydroxynaringenin, showed the efficiency in binding to the selected protein and the ability to form stable protein-ligand complexes. Binding free energy components for the 1E3G+2-hydroxynaringenin and 1E3G+R18 calculated by MM-GBSA. Table is attached in the manuscript. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Ahmed SSSJ. Peer Review Report For: Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer [version 2; peer review: 1 approved, 2 approved with reservations] . F1000Research 2025, 13 :1271 ( https://doi.org/10.5256/f1000research.170851.r335168) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1271/v1#referee-response-335168 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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