收稿日期: 2020-10-10
网络出版日期: 2022-02-21
基金资助
国家自然科学基金(51972003);国家重点研发计划(2018YFE0192500);首都特色临床研究(Z181100001718186);北大医学交叉研究种子基金-中央高校基本科研业务费(BMU2020MX013);北京大学医学部智慧医疗专项(BMU2019ZHYL003)
Computer simulation of molecular docking between methylene blue and some proteins of Porphyromonas gingivalis
Received date: 2020-10-10
Online published: 2022-02-21
Supported by
National Natural Science Foundation of China(51972003);National Key Research and Development Plan(2018YFE0192500);Capital Characteristic Clinical Research(Z181100001718186);Fundamental Research Funds for the Central Universities: Peking University Medicine Seed Fund for Interdisciplinary Research(BMU2020MX013);Smart Medical Project of Peking University Health Science Center(BMU2019ZHYL003)
目的: 使用计算机模拟的靶点预测与分子对接的方法,研究抗菌光动力疗法中光敏剂与细菌结合的靶点,并计算结合能。方法: 在Uniprot数据库和RCSB PDB数据库中获取并汇总牙龈卟啉单胞菌(Porphyromonas gingivalis,Pg)的蛋白名称;在SciFinder数据库、PubChem数据库、ChemSpider数据库和Chemical Book中筛选并对比亚甲基蓝的结构图,并用ChemBioDraw软件绘制确认;在PharmMapper数据库对亚甲基蓝三维结构进行靶点预测,并用Cytoscape 软件构建可视化网络图;在 String 数据库中构建亚甲基蓝靶点与Pg蛋白交集的相互作用网络;选择FimA、Mfa4、RgpB、Kgp K1蛋白,使用AutoDock软件计算亚甲基蓝与上述蛋白的对接能量,并进行分子对接。结果: 靶点预测结果显示,268个亚甲基蓝潜在靶点和1 865个Pg的蛋白之间有19个共同的靶点,这19个靶点为:groS、radA、rplA、dps、fabH、pyrG、thyA、panC、RHO、frdA、ileS、bioA、def、ddl、TPR、murA、lepB、cobT、gyrB。分子对接结果显示,亚甲基蓝能与FimA蛋白的9个位点结合,结合能-6.26 kcal/mol;与Mfa4蛋白的4个位点和氢键形成位点GLU47结合,结合能-5.91 kcal/mol;与RgpB蛋白的氢键形成位点LYS80结合,结合能-5.14 kcal/mol;与Kgp K1蛋白的6个位点和氢键形成位点GLY1114结合,结合能-5.07 kcal/mol。结论: 计算机模拟的靶点预测与分子对接技术可以初步揭示亚甲基蓝与Pg部分蛋白发生结合、结合程度及结合位点,为将来研究光敏剂与细胞、细菌结合位点的筛选提供参考。
袁临天 , 马利沙 , 刘润园 , 齐伟 , 张栌丹 , 王贵燕 , 王宇光 . 计算机模拟亚甲基蓝与牙龈卟啉单胞菌部分蛋白的分子对接[J]. 北京大学学报(医学版), 2022 , 54(1) : 23 -30 . DOI: 10.19723/j.issn.1671-167X.2022.01.005
Objective: To study the binding target of photosensitizer and bacteria in antimicrobial photodynamic therapy with computer-simulated target prediction and molecular docking research methods and to calculate the binding energy. Methods: The protein names of Porphyromonas gingivalis (Pg) were obtained and summarized in Uniprot database and RCSB PDB database; the structure diagrams of methy-lene blue were screened in SciFinder database, PubChem database, ChemSpider database, and Chemical Book, and ChemBioDraw software was used to draw and confirm the three-dimensional structure for target prediction and Cytoscape software was used to build a visual network diagram; a protein interaction network was searched and built between the methylene blue target and the common target of Pg in the String database; then we selected FimA, Mfa4, RgpB, and Kgp K1 proteins, used AutoDock software to calculate the docking energy of methylene blue and the above-mentioned proteins and performed molecular docking. Results: The target prediction results showed that there were 19 common targets between the 268 potential targets of methylene blue and 1 865 Pg proteins. The 19 targets were: groS, radA, rplA, dps, fabH, pyrG, thyA, panC, RHO, frdA, ileS, bioA, def, ddl, TPR, murA, lepB, cobT, and gyrB. The results of the molecular docking showed that methylene blue could bind to 9 sites of FimA protein, with a binding energy of -6.26 kcal/mol; with 4 sites of Mfa4 protein and hydrogen bond formation site GLU47, and the binding energy of -5.91 kcal/mol, the binding energy of LYS80, the hydrogen bond forming site of RgpB protein, was -5.14 kcal/mol, and the binding energy of 6 sites of Kgp K1 protein and the hydrogen bond forming site GLY1114 of -5.07 kcal/mol. Conclusion: Computer simulation of target prediction and molecular docking technology can initially reveal the binding, degree of binding and binding sites of methylene blue and Pg proteins. This method provides a reference for future research on the screening of binding sites of photosensitizers to cells and bacteria.
| [1] | Aoyama N, Suzuki JI, Kobayashi N, et al. Associations among tooth loss, systemic inflammation and antibody titers to periodontal pathogens in Japanese patients with cardiovascular disease[J]. J Periodontal Res, 2018, 53(1):117-122. |
| [2] | Papapanou PN, Sanz M, Budunneli N, et al. Periodontitis: consensus report of workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions[J]. J Periodontol, 2018, 89(Suppl 1):173-182. |
| [3] | Yan X, Lu H, Zhang L, et al. A three-year study on periodontal microorganisms of short locking-taper implants and adjacent teeth in patients with history of periodontitis[J]. J Dent, 2020, 95:103299. |
| [4] | 梁雨晴, 董秤均, 伍文彬. 牙龈卟啉单胞菌与阿尔茨海默病的相关性研究进展[J]. 中华神经医学杂志, 2020, 19(5):525-527. |
| [5] | 王春萌, 洪丽华, 张志民, 等. 牙龈卟啉单胞菌在消化系统恶性肿瘤中的作用及机制[J]. 华西口腔医学杂志, 2019, 37(5):521-526. |
| [6] | Kou Y, Inaba H, Kato T, et al. Inflammatory responses of gingival epithelial cells stimulated with Porphyromonas gingivalis vesicles are inhibited by hop-associated polyphenols[J]. J Periodontol, 2008, 79(1):174-180. |
| [7] | Ikal R, Hasegawa Y, Izumigawa M, et al. Mfa4, an accessory protein of Mfa1 fimbriae, modulates fimbrial biogenesis, cell auto-aggregation, and biofilm formation in Porphyromonas gingivalis[J]. PLoS One, 2015, 10(10):e0129454 |
| [8] | Kato T, Kawai S, Nakano K, et al. Virulence of Porphyromonas gingivalis is altered by substitution of fimbria gene with different genotype[J]. Cell Microbiol, 2007, 9(3):753-765. |
| [9] | Cieplik F, Deng D, Crielaard W, et al. Antimicrobial photodynamic therapy: what we know and what we don’t[J]. Crit Rev Microbiol, 2018, 44(5):571-589. |
| [10] | Kwiatkowski S, Knap B, Prielaard D, et al. Photodynamic therapy mechanisms, photosensitizers and combinations[J]. Biomed Pharmacother, 2018, 106:1098-1107. |
| [11] | Malik Z, Ladan H, Nitzan Y. Photodynamic inactivation of Gram-negative bacteria: problems and possible solutions[J]. J Photochem Photobiol B, 1992, 14(3):262-266. |
| [12] | Nitzan Y, Gutterman M, Malik Z, et al. Inactivation of gram-negative bacteria by photosensitized porphyrins[J]. Photochem Photobiol, 1992, 55(1):89-96. |
| [13] | Malik Z, Hanania J, Nitzan Y. Bactericidal effects of photoactivated porphyrins: an alternative approach to antimicrobial drugs[J]. J Photochem Photobiol B, 1990, 5(3/4):281-293. |
| [14] | Wang KK, Finlay JC, Busch TM, et al. Explicit dosimetry for photodynamic therapy: macroscopic singlet oxygen modeling[J]. J Biophotonics, 2010, 3(5/6):304-318. |
| [15] | Kuimove MK, Yahioglu G, Ogilby PR. Singlet oxygen in a cell: spatially dependent lifetimes and quenching rate constants[J]. J Am Chem Soc, 2009, 131(1):332-340. |
| [16] | Redmond RW, Kochevar IE. Spatially resolved cellular responses to singlet oxygen[J]. Photochem Photobiol, 2006, 82(5):1178-1186. |
| [17] | Pourhajibagher M, Bahador A. Gene expression profiling of fimA gene encoding fimbriae among clinical isolates of Porphyromonas gingivalis in response to photo-activated disinfection therapy[J]. Photodiagnosis Photodyn Ther, 2017, 20:1-5. |
| [18] | Pourhajibagher M, Bahador A. Evaluation of the crystal structure of a fimbrillin (FimA) from Porphyromonas gingivalis as a therapeutic target for photo-activated disinfection with toluidine blue O[J]. Photodiagnosis Photodyn Ther, 2017, 17:98-102. |
| [19] | Pourhajibagher M, Bahador A. In silico identification of a therapeutic target for photo-activated disinfection with indocyanine green: modeling and virtual screening analysis of Arg-gingipain from Porphyromonas gingivalis[J]. Photodiagnosis Photodyn Ther, 2017, 18:149-154. |
| [20] | Forli S, Huey R, Pique ME, et al. Computational protein-ligand docking and virtual drug screening with the AutoDock suite[J]. Nat Protoc, 2016, 11(5):905-919. |
| [21] | Ongarora BG, Fontenot KR, HU X, et al. Phthalocyanine-peptide conjugates for epidermal growth factor receptor targeting[J]. J Med Chem, 2012, 55(8):3725-3738. |
| [22] | Shanmugaraj K, Anandakumar S, Ilanchelian M. Unraveling the binding interaction of Toluidine blue O with bovine hemoglobin: a multi spectroscopic and molecular modeling approach[J]. Rsc Advances, 2015, 5(6):3930-3940. |
| [23] | Tsvetkov VB, Soloveva AB, Melik-Nubarov NS. Computer modeling of the complexes of Chlorin e6 with amphiphilic polymers[J]. Phys Chem Chem Phy, 2014, 16(22):10903-10913. |
| [24] | Zhang Y, Wan Y, Chen Y, et al. Ultrasound-enhanced chemo-photodynamic combination therapy by using albumin “nanoglue”-based nanotheranostics[J]. Acs Nano, 2020, 14(5):5560-5569. |
| [25] | Kandoussi I, Lakhlili W, Taoufik J, et al. Docking analysis of verteporfin with YAP WW domain[J]. Bioinformation, 2017, 13(7):237-240. |
| [26] | Alves E, Faustino MA, Neves MG, et al. An insight on bacterial cellular targets of photodynamic inactivation[J]. Future Med Chem, 2014, 6(2):141-164. |
| [27] | Nagano K. FimA fimbriae of the periodontal disease-associated bacterium Porphyromonas gingivalis[J]. Yakugaku Zasshi, 2013, 133(9):963-974. |
| [28] | Xu Q, Shoji M, Shibata S, et al. A distinct type of pilus from the human microbiome[J]. Cell, 2016, 165(3):690-703. |
| [29] | Zhou XY, Gao JL, Hunter N, et al. Sequence-independent processing site of the C-terminal domain (CTD) influences maturation of the RgpB protease from Porphyromonas gingivalis[J]. Mol Microbiol, 2013, 89(5):903-917. |
| [30] | 尧晨光, 奚彩丽, 朱祥, 等. EV71 3C蛋白酶表达纯化、活性分析及与抑制剂模拟对接研究[J]. 中国病原生物学杂志, 2017, 12(8):722-726. |
| [31] | 刘福和, 陈少军, 倪文娟. 川芎中抗血栓活性成分的计算机虚拟筛选研究[J]. 中国药房, 2017, 28(16):2182-2186. |
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