اخبار و اعلانات

 آمار

تعداد نشریات282
تعداد نشریات سازمان80
تعداد شماره‌ها3,272
تعداد مقالات36,149
تعداد مشاهده مقاله55,713,270
تعداد دریافت فایل اصل مقاله94,492,148
Nasre Taheri, Mahnaz, Goudarzi, Mehdi, Ebrahimipour, Gholamhossein, Hashemi, Ali. (1405). Bioinformatic Characterization and Molecular Dynamics Simulations of Cliotide U1 Interactions with Model Bacterial Phospholipid Bilayers. سامانه مدیریت نشریات علمی, 16(2), 190-196. doi: 10.22034/jmpb.2027.372251.2148
Mahnaz Nasre Taheri; Mehdi Goudarzi; Gholamhossein Ebrahimipour; Ali Hashemi. "Bioinformatic Characterization and Molecular Dynamics Simulations of Cliotide U1 Interactions with Model Bacterial Phospholipid Bilayers". سامانه مدیریت نشریات علمی, 16, 2, 1405, 190-196. doi: 10.22034/jmpb.2027.372251.2148
Nasre Taheri, Mahnaz, Goudarzi, Mehdi, Ebrahimipour, Gholamhossein, Hashemi, Ali. (1405). 'Bioinformatic Characterization and Molecular Dynamics Simulations of Cliotide U1 Interactions with Model Bacterial Phospholipid Bilayers', سامانه مدیریت نشریات علمی, 16(2), pp. 190-196. doi: 10.22034/jmpb.2027.372251.2148
Nasre Taheri, Mahnaz, Goudarzi, Mehdi, Ebrahimipour, Gholamhossein, Hashemi, Ali. Bioinformatic Characterization and Molecular Dynamics Simulations of Cliotide U1 Interactions with Model Bacterial Phospholipid Bilayers. سامانه مدیریت نشریات علمی, 1405; 16(2): 190-196. doi: 10.22034/jmpb.2027.372251.2148

Bioinformatic Characterization and Molecular Dynamics Simulations of Cliotide U1 Interactions with Model Bacterial Phospholipid Bilayers

Journal of Medicinal Plants and By-products
دوره 16، شماره 2، خرداد و تیر 2027، صفحه 190-196    اصل مقاله (3.93 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22034/jmpb.2027.372251.2148
نویسندگان
Mahnaz Nasre Taheri1؛ Mehdi Goudarzi* 2؛ Gholamhossein Ebrahimipour* 1؛ Ali Hashemi3
1Department of Microbiology, Faculty of Biological Sciences and Technology, Shahid Beheshti University, Tehran, Iran
2Department of Microbiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Ira
3Department of Microbiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
The worldwide rise in multidrug-resistant (MDR) microorganisms has intensified efforts to identify novel antimicrobial strategies beyond traditional antibiotics. This study presents a comprehensive in silico bioinformatic and molecular dynamics (MD) analysis of the antimicrobial peptide Cliotide U1, derived from Urtica dioica, to elucidate its structural properties, predicted toxicity profile, and membrane interaction mechanisms against Gram-positive and Gram-negative bacteria. In silico toxicity and hemolytic predictions using ToxinPred and HemoPI classified Cliotide U1 as a non-toxic and non-hemolytic peptide, while dPABBs analysis revealed potential anti-biofilm activity. Structural modeling and helical wheel analysis indicated that the peptide adopts a stable amphipathic α-helical structure, with cationic and hydrophobic residues optimally distributed for membrane binding. A 1000-ns all-atom MD simulation of Cliotide U1 in an aqueous environment was conducted to evaluate its structural stability, whereas separate 100-ns MD simulations were performed to investigate peptide interactions with model bacterial phospholipid bilayers using the CHARMM36 force field. In the peptide–membrane simulations, Cliotide U1 showed favorable interactions with the Gram-negative membrane. In contrast, limited surface interaction with Gram-positive membranes was observed, suggesting a carpet-like mechanism of action. RMSD and RMSF analyses indicated higher structural stability and lower fluctuations in the Gram-negative system. Overall, these computational findings identify Cliotide U1 as a predicted stable, selective, and low-toxicity antimicrobial peptide capable of potentially disrupting bacterial membranes through electrostatic and hydrophobic interactions. This peptide holds promise as a natural candidate for the development of next-generation antimicrobial agents, although experimental validation is required to confirm these predictions.
کلیدواژه‌ها
Cliotide U1؛ Urtica dioica؛ Antimicrobial Peptides؛ Molecular Dynamics Simulation؛ Anti-Biofilm Activity
مراجع
  1. Zhang R., Xu L., Dong C. Antimicrobial peptides: an overview of their structure, function and mechanism of action. Protein and Peptide Letters. 2022;29(8):641-650.
  2. Tanhaeian A., Sekhavati M.H., Moghaddam M. Antimicrobial activity of some plant essential oils and an antimicrobial-peptide against some clinically isolated pathogens. Chemical and Biological Technologies in Agriculture. 2020;7(1):13.
  3. Ibrahim T.K., Khalaf W.A., Ali A.A. The Inhibitory effect of alcoholic extraction of Pimpinella anisum against Candida albicans isolated from gingivitis. Journal of Medicinal plants and By-products. 2025;14(1):93-97.
  4. Najafi-Olya Z., Shakib P., Sepahdar A., Hasanvand M.H., Mousavi Z., Sarvi P., Mirzania F. Antimicrobial effect of Pinus longifolia Roxb. ex. Lamb. and Pinus eldarica Medw. fruits hydroalcoholic extract against Candida albicans and Enterococcus faecalis. Journal of Medicinal Plants and By-Products. 2026;15(3):347.
  5. Nsaif R. Comparative study between Aloe vera extract mouth rinse and chlorhexidine mouth rinse in preventing biofilm growth on titanium dental implants. Journal of Medicinal plants and By-products. 2026;6:684-689.
  6. Afkar S., Shokoohizadeh L. Antimicrobial properties and essential oil profile of Ziziphora clinopodioides Lam. from three regions of Lorestan Province. Journal of Medicinal plants and By-products. 2026;15(3):394-401.
  7. Mahlapuu M., Håkansson J., Ringstad L., Björn C. Antimicrobial peptides: an emerging category of therapeutic agents. Frontiers in Cellular and Infection Microbiology. 2016;6:194.
  8. Hancock R.E., Sahl H-G. Antimicrobial and host-defense peptides as new anti-infective therapeutic strategies. Nature Biotechnology. 2006; 24(12):1551-1557.
  9. Aronica P.G., Reid L.M., Desai N., Li J., Fox S.J., Yadahalli S., Essex J.W., Verma C.S. Computational methods and tools in antimicrobial peptide research. Journal of Chemical Information and Modeling. 2021; 61(7):3172-3196.
  10. Catalina-Hernandez E., Aguilella-Arzo M., Peralvarez-Marin A., Lopez-Martin M. Computational insights into membrane disruption by cell-penetrating peptides. Journal of Chemical Information and Modeling. 2025; 65(3):1549-1559.
  11. Pokhrel R., Shakya R., Baral P., Chapagain P. Molecular modeling and simulation of the peptidoglycan layer of Gram-positive bacteria Staphylococcus aureus. Journal of Chemical Information and Modeling. 2022; 62(20):4955-4962.
  12. Maleš M., Zoranić L. Simulation study of the effect of antimicrobial peptide associations on the mechanism of action with bacterial and eukaryotic membranes. Membranes. 2022;12(9):891.
  13. Sani M-A., Separovic F. How membrane-active peptides get into lipid membranes. Accounts of Chemical Research. 2016; 49(6):1130-1138.
  14. Rossetti P., Trollmann M.F., Wichmann C., Gutsmann T., Eggeling C., Böckmann R.A. From membrane composition to antimicrobial strategies: experimental and computational approaches to AMP design and selectivity. Small. 2025;22(16):e2411476.
  15. Taheri M.N., Seyedjavadi S.S., Goudarzi M., Ebrahimipour G., Hashemi A. Cliotide U1, a Novel Antimicrobial Peptide Isolated From Urtica dioica Leaves. Bioinformatics and Biology Insights. 2025; 19:11779322251315291.
  16. Chaudhary K., Kumar R., Singh S., Tuknait A., Gautam A., Mathur D., Anand P., Varshney G.C., Raghava G.P. A web server and mobile app for computing hemolytic potency of peptides. Scientific Reports. 2016;6(1):22843.
  17. Gupta S., Kapoor P., Chaudhary K., Gautam A., Kumar R., Raghava G.P. Peptide toxicity prediction. Methods in Molecular Biology. 2014;143-157.
  18. Hasannejad-Asl B., Heydari S., Azod F., Pooresmaeil F., Esmaeili A., Bolhassani A. Peptide-membrane docking and molecular dynamic simulation of in silico detected antimicrobial peptides from Portulaca oleracea's transcriptome. Probiotics and Antimicrobial Proteins. 2024;16(5):1501-1515.
  19. Sharma A., Gupta P., Kumar R., Bhardwaj A. dPABBs: a novel in silico approach for predicting and designing anti-biofilm peptides. Scientific Reports. 2016;6(1):21839.
  20. Gautier R., Douguet D., Antonny B., Drin G. HELIQUEST: a web server to screen sequences with specific α-helical properties. Bioinformatics. 2008; 24(18):2101-2102.
  21. Abraham M., Alekseenko A., Bergh C., Blau C., Briand E., Doijade M., Fleischmann S., Gapsys V., Garg G., Gorelov S., Gouaillardet G. GROMACS 2023.2 Manual; Zenodo. CERN European Organization for Nuclear Research: Geneve, Switzerland. 2023.
  22. Huang J., Rauscher S., Nawrocki G., Ran T., Feig M., de Groot BL, Grubmüller H, MacKerell AD. CHARMM36: An improved force field for folded and intrinsically disordered proteins. Biophysical Journal. 2017;112(3):175a-176a.
  23. Hess B., Bekker H., Berendsen H.J., Fraaije J.G. LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry. 1997;18(12):1463-1472.
  24. Wu E.L., Cheng X., Jo S., Rui H., Song K.C., Dávila‐Contreras E.M., Qi Y., Lee J., Monje‐Galvan V., Venable R.M., Klauda J.B. CHARMM‐GUI membrane builder toward realistic biological membrane simulations. Wiley Online Library 2014.
  25. Epand R.M., Rotem S., Mor A., Berno B., Epand R.F. Bacterial membranes as predictors of antimicrobial potency. Journal of the American Chemical Society. 2008;130(43):14346-14352.
  26. Zaeifi D., Najafi A., Mirnejad R. Molecular dynamics simulation of antimicrobial peptide CM15 in Staphylococcus aureus and Escherichia coli model bilayer lipid. Iranian Journal of Biotechnology. 2023;21(2):e3344.
  27. Allen W.J., Lemkul J.A., Bevan D.R. GridMAT‐MD: a grid‐based membrane analysis tool for use with molecular dynamics. Journal of Computational Chemistry. 2009;30(12):1952-1958.
  28. Fantner G.E., Barbero R.J., Gray DS, Belcher AM. Kinetics of antimicrobial peptide activity measured on individual bacterial cells using high-speed atomic force microscopy. Nature Nanotechnology. 2010;5(4):280-285.
  29. Abraham MJ., Murtola T., Schulz R., Páll S., Smith J.C., Hess B., Lindahl E. GROMACS: High-performance molecular simulations through multi-level parallelism from laptops to supercomputers. 2015; SoftwareX 1:19-25.
  30. Humphrey W., Dalke A., Schulten K. VMD: visual molecular dynamics. Journal of Molecular Graphics and Modelling. 1996;14(1):33-38.
  31. Mookherjee N., Anderson MA, Haagsman HP, Davidson DJ. Antimicrobial host defence peptides: functions and clinical potential. Nature Reviews Drug Discovery. 2020;19(5):311-332.
  32. Saiba R., Debnath A., Vemparala S. The role of negatively charged groups in antimicrobial cationic peptide mimics: Insights into membrane interactions. Journal of Physical Chemistry B. 2025;129(28):7134-7145.
  33. Niazi S.K. Protein catalysis through structural dynamics: a comprehensive analysis of energy conversion in enzymatic systems and its computational limitations. Pharmaceutical. 2025;18(7):951.
  34. Kurnaz LB., Luo Y., Yang X., Alabresm A., Leighton R., Kumar R., Hwang J., Decho AW., Nagarkatti P., Nagarkatti M., Tang C. Facial amphiphilicity index correlating chemical structures with antimicrobial efficacy. Bioactive Materials. 2023;20:519-527.
  35. Miquel S., Lagrafeuille R., Souweine B., Forestier C. Anti-biofilm activity as a health issue. Frontiers in Microbiology. 2016;7:592.
  36. Shai Y. Mode of action of membrane active antimicrobial peptides. Biopolymers. 2002; 66(4):236-48.
  37. Morandat S., Azouzi S., Beauvais E., Mastouri A., El Kirat K. Atomic force microscopy of model lipid membranes. Analytical and Bioanalytical Chemistry. 2013:405(5):1445-1461.
آمار
تعداد مشاهده مقاله: 16
تعداد دریافت فایل اصل مقاله: 9


counter
سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب