Structural Analysis and Functional Prediction of the Target Protein AR of Medicagol, an Active Component of Millettia reticulata

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Active components of Millettia reticulata were screened using the TCMSP database. Multiple sequence alignment and phylogenetic tree construction of AR were performed with MEGA X (10.0.5). The primary structure was analyzed using ProtParam, hydrophobicity predicted by Protscale, and conserved domains identified via Prosite. Secondary structure was analyzed using NPS@SOPMA, while tertiary structure was predicted by homology modeling from the RCSB PDB database. Subcellular localization was predicted using DeepLoc-2.0, and molecular docking was performed with SwissDock to verify the interaction between Medicagol and AR. The AR protein consists of 920 amino acids with a molecular weight of 99.19 kDa and a theoretical isoelectric point of 6.00, classifying it as a hydrophilic and unstable protein. Its secondary structure is predominantly random coil (74.13%), with the presence of a nuclear receptor ligand-binding domain (NR LBD) and C4-type zinc finger motifs. Medicagol anchors into the AR ligand-binding pocket through hydrophobic interactions and hydrogen bonds, with key residues including Pro, Glu, Lys, and Ala. AR is highly conserved among primates, clustering closely with Pongo abelii and Gorilla gorilla gorilla. Molecular docking confirmed that Medicagol forms a stable complex with AR. This study elucidates the molecular binding mechanism of Medicagol with AR, providing theoretical support for the development of AR-targeted immunoregulatory drugs and the precise utilization of medicinal-food homologous resources. Biological sciences/Biochemistry Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Biological sciences/Structural biology Homology of medicine and food Millettia reticulata Medicagol Androgen receptor Structural analysis Functional prediction Bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction The concept of homology of medicine and food refers to natural resources that possess both medicinal and dietary values, and through dietary means, regulate bodily functions and prevent diseases. This embodies the traditional wisdom of Chinese medicine, which advocates that medicine and food share the same origin 1 . Millettia reticulata (chicken blood vine), a perennial woody vine from the Fabaceae family, is commonly used in traditional Chinese medicine and medicinal diets, possessing high medicinal value and health benefits. In China, M. reticulata is predominantly distributed in the southern and southwestern regions, particularly in Guangxi, Yunnan, Guizhou, Sichuan, and Guangdong provinces 2 .In traditional Chinese medicinal diets, M. reticulata is often used in preparations such as stewed chicken with chicken blood vine or chicken blood vine soup, which are prescribed to invigorate blood and qi. These formulations are primarily utilized for regulating menstrual disorders, postpartum weakness, and rheumatic pains in women3. The main active components of M. reticulata include isoflavones, triterpenes, and sterols, which exhibit pharmacological properties such as blood toning, blood circulation enhancement, antioxidant effects, anti-inflammatory activities, and immunomodulation 4 . Modern pharmacological studies have demonstrated that M. reticulata extracts promote hematopoiesis, vasodilation, and improved blood circulation, showing beneficial therapeutic effects for conditions such as anemia, menstrual irregularities, and rheumatoid arthritis 5 . Androgen receptor (AR), a ligand-dependent transcription factor belonging to the nuclear receptor superfamily, primarily mediates the biological effects of androgens such as testosterone and dihydrotestosterone (DHT) 6 . AR is widely expressed in tissues such as the prostate, muscles, bones, testes, and central nervous system, playing a pivotal role in male sexual differentiation, maintenance of sexual function, skeletal homeostasis, and muscle synthesis 7 . The mechanism of androgen receptor (AR) action includes both classical and non-classical pathways. In the classical pathway, androgens bind to cytoplasmic AR, inducing a conformational change, dimerization, and nuclear translocation. Once in the nucleus, AR binds to androgen response elements (AREs), thereby regulating the expression of target genes 8,9 . In contrast, the non-classical pathway involves cross-talk with signaling pathways such as MAPK and PI3K/Akt, leading to rapid modulation of cellular functions. This pathway is particularly relevant in the context of cancer initiation and progression 10 . Dysregulation of AR has been closely linked to diseases such as prostate cancer, androgen insensitivity syndrome, and polycystic ovary syndrome, making AR a critical target for various therapeutic strategies 11 . A comprehensive application of bioinformatics analysis 12 , evolutionary analysis 13 , and molecular docking 14 techniques was employed to systematically investigate the interaction between Medicagol, a sterol component of Millettia reticulata, and the androgen receptor (AR). Compared to traditional experimental methods, bioinformatics approaches can efficiently and accurately predict the potential targets of natural products and analyze their binding mechanisms, thus providing a theoretical basis for experimental validation and overcoming the limitations of high costs and long timelines. Building upon this, molecular docking was further utilized to validate the mode of receptor-ligand binding. In comparison with similar studies, this study reveals the mechanisms of natural product-nuclear receptor interactions through multi-dimensional data analysis, offering a new perspective for research on sterol components from Millettia reticulata and providing a theoretical foundation for drug development based on natural products. It also lays the groundwork for the development of novel immunoregulatory drugs targeting AR proteins. Materials and Methods Data Source The data sources are shown in Table 1 . Table 1 Data sources Database Website TCMSP https://old.tcmsp-e.com/tcmsp.php DeepLoc-2.0 https://services.healthtech.dtu.dk/services/DeepLoc-2.0/ protparam https://web.expasy.org/protparam protscale https://www.expasy.org/protscale Prosite https://prosite.expasy.org NPS@SOPMA https://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html RCSB PDB:Homepage https://www.rcsb.org Methods Prediction of Active Ingredients and Targets of Millettia reticulata TCMSP is a systems pharmacology platform specifically designed for the research and development of traditional Chinese medicine. It helps researchers explore the mechanisms of drug action within specific signaling pathways, different cell types, and under personalized conditions, thereby facilitating the screening of new drugs and the development of combination therapy strategies. Additionally, the database can be used to construct networks of drug-target-disease associations, revealing potential therapeutic mechanisms 15 . The screening criteria were set as oral bioavailability (OB) ≥ 30% 16 and drug-likeness (DL) ≥ 0.18 17 . Protein Sequence Alignment and Evolutionary Analysis Protein sequences of the androgen receptor (AR) from Homo sapiens, Pan troglodytes, Theropithecus gelada, Pan paniscus, Gorilla gorilla gorilla, Pongo abelii, Piliocolobus tephrosceles, Papio anubis, Rhinopithecus roxellana, and Symphalangus syndactylus were subjected to multiple sequence alignment and phylogenetic tree construction using MEGA X (version 10.0.5). The analysis aimed to examine the conservation of AR family proteins. The sequences, including HsaAR, PtrAR-1, PpaAR, PabAR-3, GgoAR-X1, PteAR-X1, TgeAR-X1, PanAR-X1, and RroAR, were retrieved from the UniProt database. A phylogenetic tree was constructed using the Neighbor-Joining (NJ) method 18 , with branch values representing the percentage of overlapping trees for related taxa (1000 bootstrap replicates). Larger branch values indicate closer evolutionary relationships, whereas smaller values suggest more distant relationships. Physicochemical Property Analysis and Structure Prediction of Proteins To predict the physicochemical properties of the target protein, the ProtParam tool was used for analysis, providing information such as molecular weight, theoretical isoelectric point, half-life, extinction coefficient, charge characteristics, and amino acid composition 19 . Additionally, the Protscale platform was employed to assess the hydrophobicity of the protein, outputting a graphical representation of the hydrophobic index for each amino acid residue as well as the overall hydrophobic trend of the protein. For functional structure prediction, the Prosite database was used to scan the protein's amino acid sequence for conserved domains, allowing for the identification of functionally relevant structural domains. The NPS@SOPMA website was utilized for secondary structure analysis, with its core algorithm, SOPMA, achieving a prediction accuracy rate of over 80%. This tool outputs the proportions of various secondary structures such as α-helices and β-sheets. The tertiary structure model was constructed based on the RCSB PDB database, providing a visual reference of the protein’s three-dimensional spatial conformation. Protein Subcellular Localization The biological function of a protein within a cell depends on its subcellular environment, making its localization information crucial for understanding the protein's function and structural characteristics 20 . In this study, DeepLoc-2.0 21 was employed, a next-generation protein subcellular localization prediction platform that significantly enhances prediction accuracy compared to traditional tools like PSORT II. Molecular Docking Molecular docking is a key technique in computational drug design, which involves constructing a three-dimensional model of the interaction between a small molecule and its target (such as proteins or DNA) to predict their binding mode and affinity 22 . This method is widely used in drug discovery, target validation, and protein structure research 23 . In this study, SwissDock was utilized to perform docking calculations, and PyMOL software was used for preprocessing structural data and visualizing the results. The selected protein structures required relatively low resolution and were determined through X-ray crystallography. Results and Discussion Potential Active Compounds in Millettia reticulata A total of 68 compounds were retrieved from the TCMSP database for Millettia reticulata. Among them, 24 compounds met the criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18, as shown in Table 2 . Since the present study focused on the structural and functional characteristics of proteins, DL was used as the primary screening index. Based on this criterion, Medicagol (DL = 0.6, OB = 57.49%) was selected as the compound of interest. Notably, Medicagol has so far been identified exclusively in Millettia reticulata. The TCMSP database was further queried for potential targets of Medicagol, which included the androgen receptor (AR), heat shock protein HSP90, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma isoform, serine/threonine-protein kinase Chk1, and the mRNA of cAMP-dependent protein kinase catalytic subunit alpha (PKA C-α). Among these, the androgen receptor was chosen for subsequent analysis. Table 2 Effective components of Poria cocos ID Molecule Name Molecular Weight OB (%) DL MOL000469 3-Hydroxystigmast-5-en-7-one 428.77 40.93 0.78 MOL000033 (3S,8S,9S,10R,13R,14S,17R)-10,13-dimethyl-17-[(2R,5S)-5-propan-2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol 428.82 36.23 0.78 MOL000449 Stigmasterol 412.77 43.83 0.76 MOL000296 hederagenin 414.79 36.91 0.75 MOL000358 beta-sitosterol 414.79 36.91 0.75 MOL000493 campesterol 400.76 37.58 0.71 MOL000491 Augelicin 426.5 37.5 0.66 MOL000470 8-C-α-L-arabinosylluteolin 418.38 35.54 0.66 MOL000503 Medicagol 296.24 57.49 0.6 MOL000507 Psi-Baptigenin 282.26 70.12 0.31 MOL000490 petunidin 317.29 30.05 0.31 MOL000497 licochalcone a 338.43 40.79 0.29 MOL000468 8-o-Methylreyusi 298.31 70.32 0.27 MOL000502 Cajinin 300.28 68.8 0.27 MOL000501 Consume close grain 302.3 68.12 0.27 MOL000483 (Z)-3-(4-hydroxy-3-methoxy-phenyl)-N-[2-(4-hydroxyphenyl)ethyl]acrylamide 313.38 118.35 0.26 MOL000461 3,7-dihydroxy-6-methoxy-dihydroflavonol 302.3 43.8 0.26 MOL000006 luteolin 286.25 36.16 0.25 MOL000471 aloe-emodin 270.25 83.38 0.24 MOL000492 (+)-catechin 290.29 54.83 0.24 MOL000417 Calycosin 284.28 47.75 0.24 MOL000500 Vestitol 272.32 74.66 0.21 MOL000392 formononetin 268.28 69.67 0.21 MOL000506 Lupinidine 234.43 61.89 0.21 Protein Sequence Alignment and Evolutionary Analysis To investigate the evolutionary characteristics of the androgen receptor (AR) protein, multiple sequence alignments of AR proteins from Homo sapiens (human), Pan troglodytes (chimpanzee), Theropithecus gelada (gelada baboon), Pan paniscus (bonobo), Gorilla gorilla gorilla (Western lowland gorilla), Pongo abelii (Sumatran orangutan), Piliocolobus tephrosceles (gray-cheeked mangabey), Papio anubis (Anubis baboon), Rhinopithecus roxellana (Golden snub-nosed monkey), and Symphalangus syndactylus (siamangs) were performed using MEGA X (version 10.0.5) to analyze the conservation of the AR protein family (Fig. 2). The phylogenetic tree was constructed using the Neighbor-Joining (NJ) method, where the branch values represent the similarity between taxonomic groups. Higher branch values indicate closer evolutionary relationships, whereas lower values suggest more distant connections. The alignment results show that AR proteins across different species are highly conserved, with only a few amino acid differences, indicating that AR is highly conserved among primates. The phylogenetic tree analysis revealed that Homo sapiens (HsaAR) shares a close evolutionary relationship with Pongo abelii (PabAR-3) and Gorilla gorilla gorilla (GgoAR-X1), supporting the hypothesis of a common origin for humans and great apes 24 . Note Hsa - Homo sapiens, Ptr - Pan troglodytes, Ppa - Pan paniscus, Ggo - Gorilla gorilla gorilla, Pab - Pongo abelii, Pte - Piliocolobus tephrosceles, Tge - Theropithecus gelada, Pan - Papio anubis, Rro - Rhinopithecus roxellana, Ssy - Symphalangus syndactylus. a : Multiple sequence comparison of AR gene-encoded proteins across different species. : Phylogenetic tree construction of AR gene-encoded proteins across different species. Figure 2. Multiple comparisons and buildup of AR gene encoded proteins with proteins from different species AR Gene-Encoded Protein Primary Structure Prediction Prediction of the Physicochemical Properties of AR Gene-Encoded Protein The primary structure of the AR protein was analyzed using ProtParam. The results indicated that the molecular weight of the protein is 99.19 kDa, and the theoretical isoelectric point (pI) is 6.00. The protein is composed of 920 amino acids, representing 20 different amino acid types, as detailed in Table 3 . Among these, glycine (10.40%) is the most abundant, while tryptophan is the least abundant at 0.9%. The total number of negatively charged residues (Asp + Glu) in the AR protein is 92, accounting for 10%, while positively charged residues (Arg + Lys) total 81, representing 8.8%. These characteristics directly influence the hydrophilicity of the protein. In mammals, the AR protein has a half-life of 30 hours and an instability index (II) of 56.78, classifying it as an unstable protein. Moreover, the aliphatic index 25 is 68.04, indicating a high thermal stability of the protein. The AR protein's GRAVY value is -0.439, suggesting it is hydrophilic. Table 3 Number and percentage of amino acids in each category Name Quantity Percentage Gly 96 10.40% Leu 87 9.50% Ala 81 8.80% Ser 81 8.80% Pro 74 8.00% Gln 71 7.70% Glu 55 6.00% Arg 41 4.50% Lys 40 4.30% Val 40 4.30% Asp 37 4.00% Thr 37 4.00% Tyr 33 3.60% Phe 28 3.00% Cys 27 2.90% Ile 23 2.50% Met 22 2.40% Asn 20 2.20% His 19 2.10% Trp 8 0.9% Hydrophobicity Prediction of the AR Gene-Encoded Protein The hydrophobicity of the AR protein was predicted using the Protscale tool, employing the Kyte & Doolittle algorithm 26 . The results are shown in Fig. 3 . A score greater than 0 indicates hydrophobicity, while a score less than 0 indicates hydrophilicity. The most hydrophobic amino acid, located at position 814, has a score of 2.778, whereas the lowest score of -3.500 occurs between positions 61 and 76. The average hydrophobicity of the AR protein was calculated to be -0.441, which is less than zero, indicating that it is a hydrophilic protein. This result is consistent with the previous ProtParam analysis. Based on the combined analysis of its physicochemical properties and hydrophobicity prediction, AR is identified as a hydrophilic and unstable protein. Prediction of Conserved Domains in the AR Gene-Encoded Protein The amino acid sequence of the AR protein was analyzed using Prosite, and the results are shown in Fig. 4 . The analysis revealed that the C-terminal region of the AR protein (positions 669–900) contains the Nuclear Receptor Ligand Binding Domain (NR LBD). The NR LBD belongs to the nuclear receptor superfamily, which can activate transcription factors and is widely involved in regulating various physiological functions, including individual development, reproductive processes, metabolic balance, and homeostasis 27 . Therefore, the activity of AR is highly dependent on the mechanism of action of the nuclear receptor-related structural domains. Additionally, AR contains two typical C4-type zinc finger domains within the 557–632 region. These domains typically consist of three or more cysteine residues and stabilize their conformation by binding to zinc ions. However, there is no high conservation between their sequences 28 . Zinc finger structures play a key regulatory role in DNA or RNA binding and protein-protein interactions, playing a central role in AR-mediated transcriptional and translational regulation. The presence of NR LBD and C4-type zinc finger structures suggests that AR may play a central role in nuclear receptor signaling pathways. Prediction of the Secondary Structure of the AR Gene-Encoded Protein The secondary structure of the AR protein was analyzed using the NPS @ SOPMA website ( https://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html ). The higher the content of α-helix and β-sheet, the more ordered the protein structure. As shown in Fig. 5 , the AR protein contains three types of secondary structures, with random coils accounting for the highest proportion (74.13%), followed by α-helices (21.30%) and β-sheets (4.57%). Figure 6 displays the relationship curve of the various secondary structures. Note Blue: α-helix, Purple: β-sheet, Green: β-turn, Yellow: Random coil Figure 6. Structural profiles of α-helix, β-folding, irregular curl and β-turns Prediction of the Tertiary Structure of the AR Gene-encoded Protein In this study, the tertiary structure of the AR protein was predicted using the RCSB PDB database (Fig. 7 ). This method employs homology modeling, searching for known proteins with similar structures in the PDB database to serve as templates for constructing and optimizing the structure model of the target protein. As shown in Fig. 7 , the tertiary structure of AR consists of α-helix, β-sheet, and random coil, which is consistent with the results from the secondary structure prediction. Prediction of Subcellular Localization of the AR Gene-encoded Protein Protein localization is closely related to its functional mechanisms 29 . The subcellular localization of the AR protein was predicted using the DeepLoc-2.0 website, with results shown in Fig. 8 . The most likely subcellular localization of AR is the nucleus (probability = 0.8104), followed by the cytoplasm (probability = 0.4178). Experimental validation of its precise cellular localization can further provide a theoretical foundation for drug design. Molecular Docking Molecular docking was used to analyze the interaction between the effective compound of Radix Sanguisorbae (Mei Dou Chun) and its potential target, the androgen receptor (AR). The structure of the protein has a resolution of 1.8 Å and has been validated by X-ray diffraction according to the database. Figure 9 shows the molecular docking visualization results. The optimal binding energy of Mei Dou Chun with the androgen receptor was found to be -5.570 kcal/mol, which is lower than − 5 kcal/mol, indicating a stable binding between them 30 . Conclusion This study systematically analyzed the structural and functional properties of the androgen receptor (AR) protein, a potential target of the active compound Mei Dou Chun from Radix Sanguisorbae, using bioinformatics methods. Additionally, molecular docking was employed to validate the bioinformatics predictions. The study provides a comprehensive analysis of AR's structure and function and elucidates the molecular mechanism of action between Mei Dou Chun and AR. This work offers new insights into the target research of active ingredients from food-medicine homologous resources.By integrating phylogenetic analysis, protein structure modeling, and molecular docking, this study establishes a complete analytical process that encompasses "target identification—structural-functional analysis—mechanism validation." In contrast to traditional approaches relying on single methods (e.g., molecular docking or experimental validation), this strategy enables more efficient identification of key molecular players and the elucidation of their potential interaction pathways. This, in turn, enhances research efficiency and the accuracy of target screening. However, this study primarily relies on databases and computational models, making it a theoretical exploration. The predicted results still need to be validated through experimental evidence, such as cell-based experiments, animal models, or receptor activation studies. Further experimental validation of these predictions would not only improve the reliability of the research but also lay the foundation for developing AR-targeted immunomodulatory drugs. Declarations Data availability Data generated during the current study are available from the corresponding author upon reasonable request Funding This work was supported by the Guizhou Provincial Major Science and Technology Support Program (Qiankehe Zhicheng [2025] Major 001) Author contributions Y-F L: Conceptualization, Methodology, Writing. X-X Y, J-W L: Writing-review & editing. M-Z W,T W: Conceptualization, Writing-original draft, Writing-review & editing, Funding acquisition, Resources, Supervision Competing interests The authors declare no competing interests. Additional informationCorrespondence and requests for materials should be addressed to Y-F L References Wu, C., Liu, J., Cheng, S., et al. Research progress on the medicinal and edible plants in Guangxi and their utilization. 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2","display":"","copyAsset":false,"role":"figure","size":1566094,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7481346/v1/77fc5c692b3c8d66c18a7834.png"},{"id":93950580,"identity":"1abc1cb9-ce97-4b59-be48-aa0443c9f12c","added_by":"auto","created_at":"2025-10-20 14:56:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102716,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted hydrophobicity of AR gene-encoded proteins\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7481346/v1/97f4de474dbedc849f632073.png"},{"id":93950578,"identity":"39227aee-c7c9-43cb-b42c-48e053d6c170","added_by":"auto","created_at":"2025-10-20 14:56:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33152,"visible":true,"origin":"","legend":"\u003cp\u003eConserved domains of protein encoded by AR gene\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7481346/v1/184e82bdd306a472b8ebfd29.png"},{"id":93950543,"identity":"0d68cd79-724b-48f4-b50b-666c32ee2e1a","added_by":"auto","created_at":"2025-10-20 14:56:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":416570,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted secondary structure of the protein encoded by the AR gene\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7481346/v1/56db7468ca6d9d8517a232db.png"},{"id":93950562,"identity":"d52be32c-33b8-494d-af16-53b00434baab","added_by":"auto","created_at":"2025-10-20 14:56:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":172704,"visible":true,"origin":"","legend":"\u003cp\u003eStructural profiles of α-helix, β-folding, irregular curl and β-turns\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Blue: α-helix, Purple: β-sheet, Green: β-turn, Yellow: Random coil\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7481346/v1/c41c9e51048fbaf9b43af434.png"},{"id":93950560,"identity":"2811c70b-137c-4914-b9c9-4d00a6659010","added_by":"auto","created_at":"2025-10-20 14:56:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":97281,"visible":true,"origin":"","legend":"\u003cp\u003eTertiary structure of protein encoded by AR gene\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7481346/v1/4236957685bcafc58a8bd81f.png"},{"id":93951026,"identity":"ea7c5e70-a339-4bfe-8094-736c936e22bd","added_by":"auto","created_at":"2025-10-20 15:04:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":459794,"visible":true,"origin":"","legend":"\u003cp\u003eSubcellular Localization of AR Protein\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7481346/v1/0ab9857663e818ac1087a525.png"},{"id":93950540,"identity":"1289acd9-6db5-4ea5-b1ce-46f61fe51aa5","added_by":"auto","created_at":"2025-10-20 14:56:11","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":216427,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking map of Medicagol with AR\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7481346/v1/c665b6ecd377ee6d631ad3b7.png"},{"id":97249306,"identity":"4d2d182b-3fc8-4acf-b0f1-25ebe6c9f3b3","added_by":"auto","created_at":"2025-12-02 13:12:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4024872,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7481346/v1/4f5f1342-b12f-4e5c-93ca-2ef592b919ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structural Analysis and Functional Prediction of the Target Protein AR of Medicagol, an Active Component of Millettia reticulata","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe concept of homology of medicine and food refers to natural resources that possess both medicinal and dietary values, and through dietary means, regulate bodily functions and prevent diseases. This embodies the traditional wisdom of Chinese medicine, which advocates that medicine and food share the same origin\u003csup\u003e1\u003c/sup\u003e. Millettia reticulata (chicken blood vine), a perennial woody vine from the Fabaceae family, is commonly used in traditional Chinese medicine and medicinal diets, possessing high medicinal value and health benefits. In China, M. reticulata is predominantly distributed in the southern and southwestern regions, particularly in Guangxi, Yunnan, Guizhou, Sichuan, and Guangdong provinces\u003csup\u003e2\u003c/sup\u003e.In traditional Chinese medicinal diets, M. reticulata is often used in preparations such as stewed chicken with chicken blood vine or chicken blood vine soup, which are prescribed to invigorate blood and qi.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese formulations are primarily utilized for regulating menstrual disorders, postpartum weakness, and rheumatic pains in women3. The main active components of M. reticulata include isoflavones, triterpenes, and sterols, which exhibit pharmacological properties such as blood toning, blood circulation enhancement, antioxidant effects, anti-inflammatory activities, and immunomodulation\u003csup\u003e4\u003c/sup\u003e. Modern pharmacological studies have demonstrated that M. reticulata extracts promote hematopoiesis, vasodilation, and improved blood circulation, showing beneficial therapeutic effects for conditions such as anemia, menstrual irregularities, and rheumatoid arthritis\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAndrogen receptor (AR), a ligand-dependent transcription factor belonging to the nuclear receptor superfamily, primarily mediates the biological effects of androgens such as testosterone and dihydrotestosterone (DHT)\u003csup\u003e6\u003c/sup\u003e. AR is widely expressed in tissues such as the prostate, muscles, bones, testes, and central nervous system, playing a pivotal role in male sexual differentiation, maintenance of sexual function, skeletal homeostasis, and muscle synthesis\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe mechanism of androgen receptor (AR) action includes both classical and non-classical pathways. In the classical pathway, androgens bind to cytoplasmic AR, inducing a conformational change, dimerization, and nuclear translocation. Once in the nucleus, AR binds to androgen response elements (AREs), thereby regulating the expression of target genes\u003csup\u003e8,9\u003c/sup\u003e. In contrast, the non-classical pathway involves cross-talk with signaling pathways such as MAPK and PI3K/Akt, leading to rapid modulation of cellular functions. This pathway is particularly relevant in the context of cancer initiation and progression\u003csup\u003e10\u003c/sup\u003e. Dysregulation of AR has been closely linked to diseases such as prostate cancer, androgen insensitivity syndrome, and polycystic ovary syndrome, making AR a critical target for various therapeutic strategies\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA comprehensive application of bioinformatics analysis\u003csup\u003e12\u003c/sup\u003e, evolutionary analysis\u003csup\u003e13\u003c/sup\u003e, and molecular docking\u003csup\u003e14\u003c/sup\u003e techniques was employed to systematically investigate the interaction between Medicagol, a sterol component of Millettia reticulata, and the androgen receptor (AR). Compared to traditional experimental methods, bioinformatics approaches can efficiently and accurately predict the potential targets of natural products and analyze their binding mechanisms, thus providing a theoretical basis for experimental validation and overcoming the limitations of high costs and long timelines. Building upon this, molecular docking was further utilized to validate the mode of receptor-ligand binding. In comparison with similar studies, this study reveals the mechanisms of natural product-nuclear receptor interactions through multi-dimensional data analysis, offering a new perspective for research on sterol components from Millettia reticulata and providing a theoretical foundation for drug development based on natural products. It also lays the groundwork for the development of novel immunoregulatory drugs targeting AR proteins.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eData Source\u003c/h2\u003e\u003cp\u003eThe data sources are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData sources\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDatabase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWebsite\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTCMSP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://old.tcmsp-e.com/tcmsp.php\u003c/span\u003e\u003cspan address=\"https://old.tcmsp-e.com/tcmsp.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeepLoc-2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://services.healthtech.dtu.dk/services/DeepLoc-2.0/\u003c/span\u003e\u003cspan address=\"https://services.healthtech.dtu.dk/services/DeepLoc-2.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eprotparam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.expasy.org/protparam\u003c/span\u003e\u003cspan address=\"https://web.expasy.org/protparam\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eprotscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.expasy.org/protscale\u003c/span\u003e\u003cspan address=\"https://www.expasy.org/protscale\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProsite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prosite.expasy.org\u003c/span\u003e\u003cspan address=\"https://prosite.expasy.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPS@SOPMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html\u003c/span\u003e\u003cspan address=\"https://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRCSB PDB:Homepage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003ePrediction of Active Ingredients and Targets of Millettia reticulata\u003c/h2\u003e\u003cp\u003eTCMSP is a systems pharmacology platform specifically designed for the research and development of traditional Chinese medicine. It helps researchers explore the mechanisms of drug action within specific signaling pathways, different cell types, and under personalized conditions, thereby facilitating the screening of new drugs and the development of combination therapy strategies. Additionally, the database can be used to construct networks of drug-target-disease associations, revealing potential therapeutic mechanisms\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The screening criteria were set as oral bioavailability (OB)\u0026thinsp;\u0026ge;\u0026thinsp;30% \u003csup\u003e16\u003c/sup\u003e and drug-likeness (DL)\u0026thinsp;\u0026ge;\u0026thinsp;0.18\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eProtein Sequence Alignment and Evolutionary Analysis\u003c/h3\u003e\n\u003cp\u003eProtein sequences of the androgen receptor (AR) from Homo sapiens, Pan troglodytes, Theropithecus gelada, Pan paniscus, Gorilla gorilla gorilla, Pongo abelii, Piliocolobus tephrosceles, Papio anubis, Rhinopithecus roxellana, and Symphalangus syndactylus were subjected to multiple sequence alignment and phylogenetic tree construction using MEGA X (version 10.0.5). The analysis aimed to examine the conservation of AR family proteins. The sequences, including HsaAR, PtrAR-1, PpaAR, PabAR-3, GgoAR-X1, PteAR-X1, TgeAR-X1, PanAR-X1, and RroAR, were retrieved from the UniProt database. A phylogenetic tree was constructed using the Neighbor-Joining (NJ) method\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, with branch values representing the percentage of overlapping trees for related taxa (1000 bootstrap replicates). Larger branch values indicate closer evolutionary relationships, whereas smaller values suggest more distant relationships.\u003c/p\u003e\n\u003ch3\u003ePhysicochemical Property Analysis and Structure Prediction of Proteins\u003c/h3\u003e\n\u003cp\u003eTo predict the physicochemical properties of the target protein, the ProtParam tool was used for analysis, providing information such as molecular weight, theoretical isoelectric point, half-life, extinction coefficient, charge characteristics, and amino acid composition\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Additionally, the Protscale platform was employed to assess the hydrophobicity of the protein, outputting a graphical representation of the hydrophobic index for each amino acid residue as well as the overall hydrophobic trend of the protein. For functional structure prediction, the Prosite database was used to scan the protein's amino acid sequence for conserved domains, allowing for the identification of functionally relevant structural domains. The NPS@SOPMA website was utilized for secondary structure analysis, with its core algorithm, SOPMA, achieving a prediction accuracy rate of over 80%. This tool outputs the proportions of various secondary structures such as α-helices and β-sheets. The tertiary structure model was constructed based on the RCSB PDB database, providing a visual reference of the protein\u0026rsquo;s three-dimensional spatial conformation.\u003c/p\u003e\n\u003ch3\u003eProtein Subcellular Localization\u003c/h3\u003e\n\u003cp\u003eThe biological function of a protein within a cell depends on its subcellular environment, making its localization information crucial for understanding the protein's function and structural characteristics\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In this study, DeepLoc-2.0\u003csup\u003e21\u003c/sup\u003e was employed, a next-generation protein subcellular localization prediction platform that significantly enhances prediction accuracy compared to traditional tools like PSORT II.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMolecular Docking\u003c/h2\u003e\u003cp\u003eMolecular docking is a key technique in computational drug design, which involves constructing a three-dimensional model of the interaction between a small molecule and its target (such as proteins or DNA) to predict their binding mode and affinity\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This method is widely used in drug discovery, target validation, and protein structure research\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In this study, SwissDock was utilized to perform docking calculations, and PyMOL software was used for preprocessing structural data and visualizing the results. The selected protein structures required relatively low resolution and were determined through X-ray crystallography.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003ePotential Active Compounds in Millettia reticulata\u003c/h2\u003e\u003cp\u003eA total of 68 compounds were retrieved from the TCMSP database for Millettia reticulata. Among them, 24 compounds met the criteria of oral bioavailability (OB)\u0026thinsp;\u0026ge;\u0026thinsp;30% and drug-likeness (DL)\u0026thinsp;\u0026ge;\u0026thinsp;0.18, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Since the present study focused on the structural and functional characteristics of proteins, DL was used as the primary screening index. Based on this criterion, Medicagol (DL\u0026thinsp;=\u0026thinsp;0.6, OB\u0026thinsp;=\u0026thinsp;57.49%) was selected as the compound of interest. Notably, Medicagol has so far been identified exclusively in Millettia reticulata. The TCMSP database was further queried for potential targets of Medicagol, which included the androgen receptor (AR), heat shock protein HSP90, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma isoform, serine/threonine-protein kinase Chk1, and the mRNA of cAMP-dependent protein kinase catalytic subunit alpha (PKA C-α). Among these, the androgen receptor was chosen for subsequent analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffective components of Poria cocos\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMolecule Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMolecular Weight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOB (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3-Hydroxystigmast-5-en-7-one\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e428.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3S,8S,9S,10R,13R,14S,17R)-10,13-dimethyl-17-[(2R,5S)-5-propan-2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e428.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStigmasterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e412.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehederagenin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e414.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebeta-sitosterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e414.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecampesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e400.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAugelicin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e426.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8-C-α-L-arabinosylluteolin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e418.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedicagol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e296.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePsi-Baptigenin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e282.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epetunidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e317.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elicochalcone a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e338.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8-o-Methylreyusi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e298.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCajinin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e300.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsume close grain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e302.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Z)-3-(4-hydroxy-3-methoxy-phenyl)-N-[2-(4-hydroxyphenyl)ethyl]acrylamide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e313.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e118.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,7-dihydroxy-6-methoxy-dihydroflavonol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e302.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eluteolin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e286.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ealoe-emodin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e270.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(+)-catechin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e290.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCalycosin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e284.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVestitol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e272.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eformononetin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e268.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLupinidine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e234.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eProtein Sequence Alignment and Evolutionary Analysis\u003c/h2\u003e\u003cp\u003eTo investigate the evolutionary characteristics of the androgen receptor (AR) protein, multiple sequence alignments of AR proteins from Homo sapiens (human), Pan troglodytes (chimpanzee), Theropithecus gelada (gelada baboon), Pan paniscus (bonobo), Gorilla gorilla gorilla (Western lowland gorilla), Pongo abelii (Sumatran orangutan), Piliocolobus tephrosceles (gray-cheeked mangabey), Papio anubis (Anubis baboon), Rhinopithecus roxellana (Golden snub-nosed monkey), and Symphalangus syndactylus (siamangs) were performed using MEGA X (version 10.0.5) to analyze the conservation of the AR protein family (Fig.\u0026nbsp;2). The phylogenetic tree was constructed using the Neighbor-Joining (NJ) method, where the branch values represent the similarity between taxonomic groups. Higher branch values indicate closer evolutionary relationships, whereas lower values suggest more distant connections. The alignment results show that AR proteins across different species are highly conserved, with only a few amino acid differences, indicating that AR is highly conserved among primates. The phylogenetic tree analysis revealed that Homo sapiens (HsaAR) shares a close evolutionary relationship with Pongo abelii (PabAR-3) and Gorilla gorilla gorilla (GgoAR-X1), supporting the hypothesis of a common origin for humans and great apes\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eHsa - Homo sapiens, Ptr - Pan troglodytes, Ppa - Pan paniscus, Ggo - Gorilla gorilla gorilla, Pab - Pongo abelii, Pte - Piliocolobus tephrosceles, Tge - Theropithecus gelada, Pan - Papio anubis, Rro - Rhinopithecus roxellana, Ssy - Symphalangus syndactylus.\u003cb\u003ea\u003c/b\u003e: Multiple sequence comparison of AR gene-encoded proteins across different species.\u003c/b\u003e: Phylogenetic tree construction of AR gene-encoded proteins across different species.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 2.\u003c/b\u003e Multiple comparisons and buildup of AR gene encoded proteins with proteins from different species\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAR Gene-Encoded Protein Primary Structure Prediction\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003ePrediction of the Physicochemical Properties of AR Gene-Encoded Protein\u003c/h2\u003e\u003cp\u003eThe primary structure of the AR protein was analyzed using ProtParam. The results indicated that the molecular weight of the protein is 99.19 kDa, and the theoretical isoelectric point (pI) is 6.00. The protein is composed of 920 amino acids, representing 20 different amino acid types, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among these, glycine (10.40%) is the most abundant, while tryptophan is the least abundant at 0.9%. The total number of negatively charged residues (Asp\u0026thinsp;+\u0026thinsp;Glu) in the AR protein is 92, accounting for 10%, while positively charged residues (Arg\u0026thinsp;+\u0026thinsp;Lys) total 81, representing 8.8%. These characteristics directly influence the hydrophilicity of the protein. In mammals, the AR protein has a half-life of 30 hours and an instability index (II) of 56.78, classifying it as an unstable protein. Moreover, the aliphatic index\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e is 68.04, indicating a high thermal stability of the protein. The AR protein's GRAVY value is -0.439, suggesting it is hydrophilic.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber and percentage of amino acids in each category\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.40%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAla\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGln\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLys\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.60%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCys\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.90%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.40%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.10%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eHydrophobicity Prediction of the AR Gene-Encoded Protein\u003c/h2\u003e\u003cp\u003eThe hydrophobicity of the AR protein was predicted using the Protscale tool, employing the Kyte \u0026amp; Doolittle algorithm\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A score greater than 0 indicates hydrophobicity, while a score less than 0 indicates hydrophilicity. The most hydrophobic amino acid, located at position 814, has a score of 2.778, whereas the lowest score of -3.500 occurs between positions 61 and 76. The average hydrophobicity of the AR protein was calculated to be -0.441, which is less than zero, indicating that it is a hydrophilic protein. This result is consistent with the previous ProtParam analysis. Based on the combined analysis of its physicochemical properties and hydrophobicity prediction, AR is identified as a hydrophilic and unstable protein.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of Conserved Domains in the AR Gene-Encoded Protein\u003c/h2\u003e\u003cp\u003eThe amino acid sequence of the AR protein was analyzed using Prosite, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The analysis revealed that the C-terminal region of the AR protein (positions 669\u0026ndash;900) contains the Nuclear Receptor Ligand Binding Domain (NR LBD). The NR LBD belongs to the nuclear receptor superfamily, which can activate transcription factors and is widely involved in regulating various physiological functions, including individual development, reproductive processes, metabolic balance, and homeostasis\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Therefore, the activity of AR is highly dependent on the mechanism of action of the nuclear receptor-related structural domains. Additionally, AR contains two typical C4-type zinc finger domains within the 557\u0026ndash;632 region. These domains typically consist of three or more cysteine residues and stabilize their conformation by binding to zinc ions. However, there is no high conservation between their sequences\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Zinc finger structures play a key regulatory role in DNA or RNA binding and protein-protein interactions, playing a central role in AR-mediated transcriptional and translational regulation. The presence of NR LBD and C4-type zinc finger structures suggests that AR may play a central role in nuclear receptor signaling pathways.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of the Secondary Structure of the AR Gene-Encoded Protein\u003c/h2\u003e\u003cp\u003eThe secondary structure of the AR protein was analyzed using the NPS @ SOPMA website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html\u003c/span\u003e\u003cspan address=\"https://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The higher the content of α-helix and β-sheet, the more ordered the protein structure. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the AR protein contains three types of secondary structures, with random coils accounting for the highest proportion (74.13%), followed by α-helices (21.30%) and β-sheets (4.57%). Figure\u0026nbsp;6 displays the relationship curve of the various secondary structures.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eBlue: α-helix, Purple: β-sheet, Green: β-turn, Yellow: Random coil\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 6.\u003c/b\u003e Structural profiles of α-helix, β-folding, irregular curl and β-turns\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of the Tertiary Structure of the AR Gene-encoded Protein\u003c/h2\u003e\u003cp\u003eIn this study, the tertiary structure of the AR protein was predicted using the RCSB PDB database (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This method employs homology modeling, searching for known proteins with similar structures in the PDB database to serve as templates for constructing and optimizing the structure model of the target protein. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the tertiary structure of AR consists of α-helix, β-sheet, and random coil, which is consistent with the results from the secondary structure prediction.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of Subcellular Localization of the AR Gene-encoded Protein\u003c/h2\u003e\u003cp\u003eProtein localization is closely related to its functional mechanisms\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The subcellular localization of the AR protein was predicted using the DeepLoc-2.0 website, with results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The most likely subcellular localization of AR is the nucleus (probability\u0026thinsp;=\u0026thinsp;0.8104), followed by the cytoplasm (probability\u0026thinsp;=\u0026thinsp;0.4178). Experimental validation of its precise cellular localization can further provide a theoretical foundation for drug design.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eMolecular Docking\u003c/h2\u003e\u003cp\u003eMolecular docking was used to analyze the interaction between the effective compound of \u003cem\u003eRadix Sanguisorbae\u003c/em\u003e (Mei Dou Chun) and its potential target, the androgen receptor (AR). The structure of the protein has a resolution of 1.8 \u0026Aring; and has been validated by X-ray diffraction according to the database. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the molecular docking visualization results. The optimal binding energy of Mei Dou Chun with the androgen receptor was found to be -5.570 kcal/mol, which is lower than \u0026minus;\u0026thinsp;5 kcal/mol, indicating a stable binding between them\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study systematically analyzed the structural and functional properties of the androgen receptor (AR) protein, a potential target of the active compound Mei Dou Chun from Radix Sanguisorbae, using bioinformatics methods. Additionally, molecular docking was employed to validate the bioinformatics predictions. The study provides a comprehensive analysis of AR's structure and function and elucidates the molecular mechanism of action between Mei Dou Chun and AR. This work offers new insights into the target research of active ingredients from food-medicine homologous resources.By integrating phylogenetic analysis, protein structure modeling, and molecular docking, this study establishes a complete analytical process that encompasses \"target identification\u0026mdash;structural-functional analysis\u0026mdash;mechanism validation.\" In contrast to traditional approaches relying on single methods (e.g., molecular docking or experimental validation), this strategy enables more efficient identification of key molecular players and the elucidation of their potential interaction pathways. This, in turn, enhances research efficiency and the accuracy of target screening. However, this study primarily relies on databases and computational models, making it a theoretical exploration. The predicted results still need to be validated through experimental evidence, such as cell-based experiments, animal models, or receptor activation studies. Further experimental validation of these predictions would not only improve the reliability of the research but also lay the foundation for developing AR-targeted immunomodulatory drugs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData generated during the current study are available from the corresponding author upon reasonable request\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Guizhou Provincial Major Science and Technology Support Program (Qiankehe Zhicheng [2025] Major 001)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY-F L: Conceptualization, Methodology, Writing. X-X Y, J-W L: Writing-review \u0026amp; editing. M-Z W,T W: Conceptualization, Writing-original draft, Writing-review \u0026amp; editing, Funding acquisition, Resources, Supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003einterests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAdditional informationCorrespondence and requests for materials should be addressed to Y-F L\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWu, C., Liu, J., Cheng, S., et al. Research progress on the medicinal and edible plants in Guangxi and their utilization. Guangxi Plants, 45(03), 450-465 (2025).\u003c/li\u003e\n\u003cli\u003eXiang, Y., Liu, Y., Feng, J., et al. A study on the traditional textual research of Radix Sanguisorbae in classical prescriptions. 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European Journal of Medicinal Chemistry, 261(11), 58-69 https://doi.org/10.1016/j.ejmech.2023.115869 (2023).\u003c/li\u003e\n\u003cli\u003eBrown, R. S. Zinc finger proteins: Getting a grip on RNA. Current Opinion in Structural Biology, 15(1), 94-98 https://doi.org/10.1016/j.sbi.2005.01.006 (2005).\u003c/li\u003e\n\u003cli\u003eLees, M. B., \u0026amp; Bizzozero, O. A. Structure and acylation of proteolipid protein. Myelin. Routledge, 237-255 (2023).\u003c/li\u003e\n\u003cli\u003eMaahury, M. F., \u0026amp; Allo, V. L. The computational calculation and molecular docking of aeroplysinin-1 as antibacterial. Indonesian Journal of Chemical Research, 9(2), 124-128 https://doi.org/10.30598//IJCR.2020.9-MIR (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Homology of medicine and food, Millettia reticulata, Medicagol, Androgen receptor, Structural analysis, Functional prediction, Bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-7481346/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7481346/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo investigate the interaction mechanism between the steroidal active component Medicagol from Millettia reticulata and the androgen receptor (AR) using bioinformatics, and to explore its potential therapeutic effects. Active components of Millettia reticulata were screened using the TCMSP database. Multiple sequence alignment and phylogenetic tree construction of AR were performed with MEGA X (10.0.5). The primary structure was analyzed using ProtParam, hydrophobicity predicted by Protscale, and conserved domains identified via Prosite. Secondary structure was analyzed using NPS@SOPMA, while tertiary structure was predicted by homology modeling from the RCSB PDB database. Subcellular localization was predicted using DeepLoc-2.0, and molecular docking was performed with SwissDock to verify the interaction between Medicagol and AR. The AR protein consists of 920 amino acids with a molecular weight of 99.19 kDa and a theoretical isoelectric point of 6.00, classifying it as a hydrophilic and unstable protein. Its secondary structure is predominantly random coil (74.13%), with the presence of a nuclear receptor ligand-binding domain (NR LBD) and C4-type zinc finger motifs. Medicagol anchors into the AR ligand-binding pocket through hydrophobic interactions and hydrogen bonds, with key residues including Pro, Glu, Lys, and Ala. AR is highly conserved among primates, clustering closely with Pongo abelii and Gorilla gorilla gorilla. Molecular docking confirmed that Medicagol forms a stable complex with AR. This study elucidates the molecular binding mechanism of Medicagol with AR, providing theoretical support for the development of AR-targeted immunoregulatory drugs and the precise utilization of medicinal-food homologous resources.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Structural Analysis and Functional Prediction of the Target Protein AR of Medicagol, an Active Component of Millettia reticulata","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 14:56:07","doi":"10.21203/rs.3.rs-7481346/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"023f3efd-6e08-461c-80c7-d18cb577057d","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56505943,"name":"Biological sciences/Biochemistry"},{"id":56505944,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56505945,"name":"Biological sciences/Drug discovery"},{"id":56505946,"name":"Biological sciences/Structural biology"}],"tags":[],"updatedAt":"2025-12-01T18:23:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 14:56:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7481346","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7481346","identity":"rs-7481346","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}