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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

A Novel In silico Filtration Method for Discovery of Encrypted Antimicrobial Peptides

Author(s): Farnoosh Barneh, Ahmad Nazarian, Rezvan Mousavi Nadoshan and Kamran Pooshang Bagheri*

Volume 19, Issue 5, 2024

Published on: 04 December, 2023

Page: [502 - 512] Pages: 11

DOI: 10.2174/0115748936274103231114105340

Price: $65

Abstract

Background: Antibacterial resistance has been one of the most important causes of death in the last few decades, necessitating the need to discover new antibiotics. Antimicrobial peptides (AMPs) are among the best candidates due to their broad-spectrum and potent activity against bacteria and low probability of developing resistance against them.

Objective: In this study, we proposed a novel filtration method using knowledge-based approaches to discover encrypted AMPs within a protein sequence.

Methods: The encrypted AMPs were selected from a protein sequence, in this case, lactoferrin, based on hydrophobicity, cationicity, alpha-helix structure, helical wheel projection, and binding affinities to gram-negative and positive bacterial membranes.

Results: Six out of 20 potential encrypted AMPs were ultimately selected for further assays. Molecular docking of the selected AMPs with outer and inner membranes of gram-negative bacteria and also gram-positive bacterial membranes showed reasonable binding affinity ranging from ‘-6.7 to -7.5’ and ‘- 4.5 to -5.7’ and ‘-4.6 to -5.7’ kcal/mol, respectively. No toxicity was shown in the candidate AMPs.

Conclusion: According to in silico results, our method succeeded to discover six new encrypted AMPs from human lactoferrin, designated as lactoferrin-derived peptides (LDPs). Further in silico and experimental assays should also be performed to prove the efficiency of our knowledge-based filtration method.

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Graphical Abstract

[1]
O’Neill J. Review on antimicrobial resistance. Antimicrob resist tackling a Cris Heal wealth Nations 2014; 2014(4)
[2]
Fair RJ, Tor Y. Antibiotics and bacterial resistance in the 21st century. Perspect Medicin Chem 2014; 6: PMC.S14459.
[http://dx.doi.org/10.4137/PMC.S14459] [PMID: 25232278]
[3]
Capita R, Alonso-Calleja C. Antibiotic-resistant bacteria: A challenge for the food industry. Crit Rev Food Sci Nutr 2013; 53(1): 11-48.
[http://dx.doi.org/10.1080/10408398.2010.519837] [PMID: 23035919]
[4]
Levy SB. The challenge of antibiotic resistance. Sci Am 1998; 278(3): 46-53.
[http://dx.doi.org/10.1038/scientificamerican0398-46] [PMID: 9487702]
[5]
Jenssen H, Hamill P, Hancock REW. Peptide antimicrobial agents. Clin Microbiol Rev 2006; 19(3): 491-511.
[http://dx.doi.org/10.1128/CMR.00056-05] [PMID: 16847082]
[6]
Aghazadeh H, Memariani H, Ranjbar R, Pooshang Bagheri K. The activity and action mechanism of novel short selective LL‐37‐derived anticancer peptides against clinical isolates of Escherichia coli. Chem Biol Drug Des 2019; 93(1): 75-83.
[http://dx.doi.org/10.1111/cbdd.13381] [PMID: 30120878]
[7]
Shams Khozani R, Shahbazzadeh D, Harzandi N, Feizabadi MM, Pooshang Bagheri K. Kinetics study of antimicrobial peptide, melittin, in simultaneous biofilm degradation and eradication of potent biofilm producing mdr pseudomonas aeruginosa isolates. Int J Pept Res Ther 2019; 25(1): 329-38.
[http://dx.doi.org/10.1007/s10989-018-9675-z]
[8]
Zarghami V, Ghorbani M, Pooshang Bagheri K, Shokrgozar MA. Melittin antimicrobial peptide thin layer on bone implant chitosan-antibiotic coatings and their bactericidal properties. Mater Chem Phys 2021; 263: 124432.
[http://dx.doi.org/10.1016/j.matchemphys.2021.124432]
[9]
Ashby M, Petkova A, Gani J, Mikut R, Hilpert K. Use of peptide libraries for identification and optimization of novel antimicrobial peptides. Curr Top Med Chem 2016; 17(5): 537-53.
[http://dx.doi.org/10.2174/1568026616666160713125555] [PMID: 27411326]
[10]
Sabatier JM. Antibacterial peptides. Antibiotics 2020; 9(4): 142.
[http://dx.doi.org/10.3390/antibiotics9040142] [PMID: 32224942]
[11]
Memariani H, Shahbazzadeh D, Sabatier JM, Pooshang Bagheri K. Membrane‐active peptide PV 3 efficiently eradicates multidrug‐resistant Pseudomonas aeruginosa in a mouse model of burn infection. Acta Pathol Microbiol Scand Suppl 2018; 126(2): 114-22.
[http://dx.doi.org/10.1111/apm.12791] [PMID: 29327480]
[12]
Bevalian P, Pashaei F, Akbari R, Pooshang Bagheri K. Eradication of vancomycin-resistant Staphylococcus aureus on a mouse model of third-degree burn infection by melittin: An antimicrobial peptide from bee venom. Toxicon 2021; 199: 49-59.
[http://dx.doi.org/10.1016/j.toxicon.2021.05.015] [PMID: 34087287]
[13]
Koo HB, Seo J. Antimicrobial peptides under clinical investigation. Pept Sci 2019; 111(5): e24122.
[http://dx.doi.org/10.1002/pep2.24122]
[14]
GALE EF. Mechanisms of antibiotic action. Pharmacol Rev 1963; 15: 481-530.
[15]
Akbari R, Hakemi Vala M, Hashemi A, Aghazadeh H, Sabatier JM, Pooshang Bagheri K. Action mechanism of melittin-derived antimicrobial peptides, MDP1 and MDP2, de novo designed against multidrug resistant bacteria. Amino Acids 2018; 50(9): 1231-43.
[http://dx.doi.org/10.1007/s00726-018-2596-5] [PMID: 29905903]
[16]
Kaur-Boparai J, Sharma PK. Mini review on antimicrobial peptides, sources, mechanism and recent applications. Protein Pept Lett 2020; 27(1): 4-16.
[http://dx.doi.org/10.2174/18755305MTAwENDE80] [PMID: 31438824]
[17]
Bulet P, Stöcklin R, Menin L. Anti-microbial peptides: From invertebrates to vertebrates. Immunol Rev 2004; 198(1): 169-84.
[http://dx.doi.org/10.1111/j.0105-2896.2004.0124.x] [PMID: 15199962]
[18]
Huan Y, Kong Q, Mou H, Yi H. Antimicrobial peptides: Classification, design, application and research progress in multiple fields. Front Microbiol 2020; 11: 582779.
[http://dx.doi.org/10.3389/fmicb.2020.582779] [PMID: 33178164]
[19]
Cesaro A, Torres MDT, Gaglione R, et al. Synthetic antibiotic derived from sequences encrypted in a protein from human plasma. ACS Nano 2022; 16(2): 1880-95.
[http://dx.doi.org/10.1021/acsnano.1c04496] [PMID: 35112568]
[20]
Auestad N, Layman DK. Dairy bioactive proteins and peptides: A narrative review. Nutr Rev 2021; 79(S2): 36-47.
[http://dx.doi.org/10.1093/nutrit/nuab097]
[21]
Torres MDT, Melo MCR, Crescenzi O, Notomista E, de la Fuente-Nunez C, de la Fuente-Nunez C. Mining for encrypted peptide antibiotics in the human proteome. Nat Biomed Eng 2021; 6(1): 67-75.
[http://dx.doi.org/10.1038/s41551-021-00801-1] [PMID: 34737399]
[22]
Santos MFS, Freitas CS, Verissimo da Costa GC, Pereira PR, Paschoalin VMF. Identification of antibacterial peptide candidates encrypted in stress-related and metabolic saccharomyces cerevisiae proteins. Pharmaceuticals 2022; 15(2): 163.
[http://dx.doi.org/10.3390/ph15020163]
[23]
Gawde U, Chakraborty S, Waghu FH, et al. CAMPR4: A database of natural and synthetic antimicrobial peptides. Nucleic Acids Res 2023; 51(D1): D377-83.
[http://dx.doi.org/10.1093/nar/gkac933] [PMID: 36370097]
[24]
Kopeć K, Pędziwiatr M, Gront D, et al. Comparison of α-helix and β-sheet structure adaptation to a quantum dot geometry: Toward the identification of an optimal motif for a protein nanoparticle cover. ACS Omega 2019; 4(8): 13086-99.
[http://dx.doi.org/10.1021/acsomega.9b00505] [PMID: 31460436]
[25]
Abrusán G, Marsh JA. Alpha helices are more robust to mutations than beta strands. PLOS Comput Biol 2016; 12(12): e1005242.
[http://dx.doi.org/10.1371/journal.pcbi.1005242] [PMID: 27935949]
[26]
Zelezetsky I, Tossi A. Alpha-helical antimicrobial peptides—Using a sequence template to guide structure–activity relationship studies. Biochim Biophys Acta Biomembr 2006; 1758(9): 1436-49.
[http://dx.doi.org/10.1016/j.bbamem.2006.03.021] [PMID: 16678118]
[27]
Eisapoor SS, Jamili S, Shahbazzadeh D, Ghavam Mostafavi P, Pooshang Bagheri K. A new, high yield, rapid, and cost‐effective protocol to deprotection of cysteine‐rich conopeptide, omega‐conotoxin MVIIA. Chem Biol Drug Des 2016; 87(5): 687-93.
[http://dx.doi.org/10.1111/cbdd.12702] [PMID: 26662374]
[28]
Stadtman ER, Moskovitz J, Levine RL. Oxidation of methionine residues of proteins: biological consequences. Antioxid Redox Signal 2003; 5(5): 577-82.
[http://dx.doi.org/10.1089/152308603770310239] [PMID: 14580313]
[29]
Bateman A, Martin M-J, Orchard S, et al. UniProt: The universal protein knowledgebase in 2021. Nucleic Acids Res 2021; 49(D1): D480-9.
[http://dx.doi.org/10.1093/nar/gkaa1100] [PMID: 33237286]
[30]
Zhou X, Zheng W, Li Y, et al. I-TASSER-MTD: A deep-learning-based platform for multi-domain protein structure and function prediction. Nat Protoc 2022; 17(10): 2326-53.
[http://dx.doi.org/10.1038/s41596-022-00728-0] [PMID: 35931779]
[31]
DeLano WL. The PyMol molecular graphics system. Version 20 Schrodinger. Inc. New York 2017.
[32]
Schiffer M, Edmundson AB. Use of helical wheels to represent the structures of proteins and to identify segments with helical potential. Biophys J 1967; 7(2): 121-35.
[http://dx.doi.org/10.1016/S0006-3495(67)86579-2] [PMID: 6048867]
[33]
Gautier R, Douguet D, Antonny B, Drin G. HELIQUEST: a web server to screen sequences with specific α-helical properties. Bioinformatics 2008; 24(18): 2101-2.
[http://dx.doi.org/10.1093/bioinformatics/btn392] [PMID: 18662927]
[34]
Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2009; 31(2): NA.
[http://dx.doi.org/10.1002/jcc.21334] [PMID: 19499576]
[35]
Seeliger D, de Groot BL. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J Comput Aided Mol Des 2010; 24(5): 417-22.
[http://dx.doi.org/10.1007/s10822-010-9352-6] [PMID: 20401516]
[36]
Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings. J Chem Inf Model 2021; 61(8): 3891-8.
[http://dx.doi.org/10.1021/acs.jcim.1c00203] [PMID: 34278794]
[37]
Lee J, Hitzenberger M, Rieger M, Kern NR, Zacharias M, Im W. CHARMM-GUI supports the Amber force fields. J Chem Phys 2020; 153(3): 035103.
[http://dx.doi.org/10.1063/5.0012280] [PMID: 32716185]
[38]
Lindberg B, Lindh F, Lönngren J, Lindberg AA, Svenson SB. Structural studies of the O-specific side-chain of the lipopolysaccharide from Escherichia coli O 55. Carbohydr Res 1981; 97(1): 105-12.
[http://dx.doi.org/10.1016/S0008-6215(00)80528-5] [PMID: 7030487]
[39]
Micciulla S, Gerelli Y, Schneck E. Structure and conformation of wild-type bacterial lipopolysaccharide layers at air-water interfaces. Biophys J 2019; 116(7): 1259-69.
[http://dx.doi.org/10.1016/j.bpj.2019.02.020] [PMID: 30878200]
[40]
Wei L, Ye X, Sakurai T, Mu Z, Wei L. ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning. Bioinformatics 2022; 38(6): 1514-24.
[http://dx.doi.org/10.1093/bioinformatics/btac006] [PMID: 34999757]
[41]
Wang G, Li X, Wang Z. APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res 2016; 44(D1): D1087-93.
[http://dx.doi.org/10.1093/nar/gkv1278] [PMID: 26602694]
[42]
Okella H, Georrge JJ, Ochwo S, et al. New putative antimicrobial candidates: In silico design of fish-derived antibacterial peptide-motifs. Front Bioeng Biotechnol 2020; 8: 604041.
[http://dx.doi.org/10.3389/fbioe.2020.604041] [PMID: 33344436]
[43]
Saint Jean KD, Henderson KD, Chrom CL, Abiuso LE, Renn LM, Caputo GA. Effects of hydrophobic amino acid substitutions on antimicrobial peptide behavior. Probiotics Antimicrob Proteins 2018; 10(3): 408-19.
[http://dx.doi.org/10.1007/s12602-017-9345-z] [PMID: 29103131]
[44]
Tossi A, Sandri L, Giangaspero A. Amphipathic, α-helical antimicrobial peptides. Biopolymers 2000; 55(1): 4-30.
[http://dx.doi.org/10.1002/1097-0282(2000)55:1<4::AID-BIP30>3.0.CO;2-M] [PMID: 10931439]
[45]
Zhang QY, Yan ZB, Meng YM, et al. Antimicrobial peptides: Mechanism of action, activity and clinical potential. Mil Med Res 2021; 8(1): 48.
[http://dx.doi.org/10.1186/s40779-021-00343-2] [PMID: 34496967]
[46]
Waghu FH, Idicula-Thomas S. Collection of antimicrobial peptides database and its derivatives: Applications and beyond. Protein Sci 2020; 29(1): 36-42.
[http://dx.doi.org/10.1002/pro.3714] [PMID: 31441165]

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