Login Register Cart 0
Generic placeholder image

Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Mini-Review Article

Tools in the Era of Multidrug Resistance in Bacteria: Applications for New Antimicrobial Peptides Discovery

Author(s): Antonio Moretta, Carmen Scieuzo, Rosanna Salvia*, Željko D. Popović, Alessandro Sgambato and Patrizia Falabella*

Volume 28, Issue 35, 2022

Published on: 25 August, 2022

Page: [2856 - 2866] Pages: 11

DOI: 10.2174/1381612828666220817163339

Price: $65

Abstract

Antimicrobial peptides (AMPs) are small molecules belonging to innate immunity that act against bacteria, fungi, and viruses. With the spread of bacterial strains resistant to current antibiotics, the scientific community is deeply committed to the identification and study of new molecules with putative antimicrobial activity. In this context, AMPs represent a promising alternative to overcome this problem. To date, several databases have been built up to provide information on the AMPs identified so far and their physico-chemical properties. Moreover, several tools have been developed and are available online that allow to highlight sequences with putative antimicrobial activity and predict their biological activity. These tools can also predict the secondary and tertiary structures of putative AMPs, thus allowing molecular docking studies to evaluate potential interactions with proteins/ligands. In this paper, we focused our attention on online available AMPs databases and computational tools for biological activity and tertiary structure prediction, highlighting some papers in which the computational approach was successfully used. As the identification of peptides starts from the analysis of a large amount of data, we show that bioinformatics predictions are the best starting point for the identification of new sequences of interest that can be subsequently produced and tested.

Keywords: AMP, drug discovery, computational methods, activity prediction, drug design, anticancer, antifungal, antiviral.

« Previous Next »
[1]
Manniello MD, Moretta A, Salvia R, et al. Insect antimicrobial peptides: Potential weapons to counteract the antibiotic resistance. Cell Mol Life Sci 2021; 78(9): 4259-82.
[http://dx.doi.org/10.1007/s00018-021-03784-z] [PMID: 33595669]
[2]
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]
[3]
van’t Hof W, Veerman EC, Helmerhorst EJ, Amerongen AV. Antimicrobial peptides: Properties and applicability. Biol Chem 2001; 382(4): 597-619.
[PMID: 11405223]
[4]
Nakatsuji T, Gallo RL. Antimicrobial peptides: Old molecules with new ideas. J Invest Dermatol 2012; 132(3 Pt 2): 887-95.
[http://dx.doi.org/10.1038/jid.2011.387] [PMID: 22158560]
[5]
Moretta A, Scieuzo C, Petrone AM, et al. A new hope in biomedical and pharmaceutical field. Front Cell Infect Microbiol 2021; 11: 453.
[http://dx.doi.org/10.3389/fcimb.2021.668632]
[6]
Demir Y, Kotan MŞ, Dikbas N, Beydemir S. Phytase from Weissella halotolerans: Purification, partial characterisation and the effect of some metals. Int J Food Prop 2017; 20: 1-11.
[http://dx.doi.org/10.1080/10942912.2017.1368547]
[7]
Barashkova AS, Rogozhin EA. Isolation of antimicrobial peptides from different plant sources: Does a general extraction method exist? Plant Methods 2020; 16(1): 143.
[http://dx.doi.org/10.1186/s13007-020-00687-1] [PMID: 33110440]
[8]
Malik E, Dennison SR, Harris F, Phoenix DA. pH dependent antimicrobial peptides and proteins, their mechanisms of action and potential as therapeutic agents. Pharmaceuticals 2016; 9(4): 67.
[http://dx.doi.org/10.3390/ph9040067] [PMID: 27809281]
[9]
Moretta A, Salvia R, Scieuzo C, et al. A bioinformatic study of antimicrobial peptides identified in the Black Soldier Fly (BSF) Hermetia illucens (Diptera: Stratiomyidae). Sci Rep 2020; 10(1): 16875.
[http://dx.doi.org/10.1038/s41598-020-74017-9] [PMID: 33037295]
[10]
Mahlapuu M, Håkansson J, Ringstad L, Björn C. Antimicrobial peptides: An emerging category of therapeutic agents. Frontiers in cellular and infection microbiology. Front Cell Infect Microbiol 2016; 6: 194.
[http://dx.doi.org/10.3389/fcimb.2016.00194] [PMID: 28083516]
[11]
Travkova OG, Moehwald H, Brezesinski G. The interaction of antimicrobial peptides with membranes. Adv Colloid Interface Sci 2017; 247: 521-32.
[http://dx.doi.org/10.1016/j.cis.2017.06.001] [PMID: 28606715]
[12]
Sato H, Feix JB. Peptide-membrane interactions and mechanisms of membrane destruction by amphipathic α-helical antimicrobial peptides. Biochim Biophys Acta 2006; 1758(9): 1245-56.
[http://dx.doi.org/10.1016/j.bbamem.2006.02.021] [PMID: 16697975]
[13]
Buda De Cesare G, Cristy SA, Garsin DA, Lorenz MC. Antimicrobial peptides: A new frontier in antifungal therapy. MBio 2020; 11(6): e02123-20.
[http://dx.doi.org/10.1128/mBio.02123-20] [PMID: 33144376]
[14]
Lei J, Sun L, Huang S, et al. The antimicrobial peptides and their potential clinical applications. Am J Transl Res 2019; 11(7): 3919-31.
[PMID: 31396309]
[15]
Soltani S, Hammami S, Cotter PD, et al. Bacteriocins as a new generation of antimicrobials: Toxicity aspects and regulations. FEMS Microbiol Rev 2021; 45.
[16]
Jenssen H, Hamill P, Hancock RE. Peptide antimicrobial agents. Clin Microbiol Rev 2006; 19(3): 491-511.
[http://dx.doi.org/10.1128/CMR.00056-05] [PMID: 16847082]
[17]
López-Meza JE, Ochoa-Zarzosa A, Aguilar JA. Loeza-lara PD. Antimicrobial peptides: Diversity and perspectives for their biomedical application. Biomedical Engineering. Trends, Research and Technologies Inc. 2011; pp. 275-304.
[18]
Li Y, Xiang Q, Zhang Q, Huang Y, Su Z. Overview on the recent study of antimicrobial peptides: Origins, functions, relative mechanisms and application. Peptides 2012; 37(2): 207-15.
[http://dx.doi.org/10.1016/j.peptides.2012.07.001] [PMID: 22800692]
[19]
Chernysh SI, Gordja NA, Simonenko NP. Diapause and immune response: Induction of antimicrobial peptides synthesis in the blowfly, Calliphora vicina R-D (Diptera: Calliphoridae). Entomol Sci 2000; 3: 139-44.
[20]
Denlinger D. Why study diapause? Entomol Res 2008; 38(1): 1-9.
[http://dx.doi.org/10.1111/j.1748-5967.2008.00139.x]
[21]
Gordya N, Yakovlev A, Kruglikova A, et al. Natural antimicrobial peptide complexes in the fighting of antibiotic resistant biofilms: Calliphora vicina medicinal maggots. PLoS One 2017; 12(3): e0173559.
[http://dx.doi.org/10.1371/journal.pone.0173559] [PMID: 28278280]
[22]
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]
[23]
Patocka J, Nepovimova E, Klimova B, Wu Q, Kuca K. Antimicrobial peptides: Amphibian host defense peptides. Curr Med Chem 2019; 26(32): 5924-46.
[http://dx.doi.org/10.2174/0929867325666180713125314] [PMID: 30009702]
[24]
Magana M, Pushpanathan M, Santos AL, et al. The value of antimicrobial peptides in the age of resistance. Lancet Infect Dis 2020; 20(9): e216-30.
[http://dx.doi.org/10.1016/S1473-3099(20)30327-3] [PMID: 32653070]
[25]
Hegedüs N, Marx F. Antifungal proteins: More than antimicrobials? Fungal Biol Rev 2013; 26(4): 132-45.
[http://dx.doi.org/10.1016/j.fbr.2012.07.002] [PMID: 23412850]
[26]
Kang X, Dong F, Shi C, et al. DRAMP 2.0, an updated data repository of antimicrobial peptides. Sci Data 2019; 6(1): 148.
[http://dx.doi.org/10.1038/s41597-019-0154-y] [PMID: 31409791]
[27]
Liu S, Bao J, Lao X, Zheng H. Novel 3D structure based model for activity prediction and design of antimicrobial peptides. Sci Rep 2048(8): 1-12.
[28]
Liu S, Fan L, Sun J, Lao X, Zheng H. Computational resources and tools for antimicrobial peptides. J Pept Sci 2017; 23(1): 4-12.
[http://dx.doi.org/10.1002/psc.2947] [PMID: 27966278]
[29]
Fan L, Sun J, Zhou M, et al. DRAMP: A comprehensive data repository of antimicrobial peptides. Sci Rep 2016; 6(1): 24482.
[http://dx.doi.org/10.1038/srep24482] [PMID: 27075512]
[30]
Waghu FH, Barai RS, Gurung P, Idicula-Thomas S. CAMPR3: A database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Res 2016; 44(D1): D1094-7.
[http://dx.doi.org/10.1093/nar/gkv1051] [PMID: 26467475]
[31]
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]
[32]
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]
[33]
Baker MA, Maloy WL, Zasloff M, Jacob LS. Anticancer efficacy of Magainin2 and analogue peptides. Cancer Res 1993; 53(13): 3052-7.
[PMID: 8319212]
[34]
Hoskin DW, Ramamoorthy A. Studies on anticancer activities of antimicrobial peptides. Biochim Biophys Acta 2008; 1778(2): 357-75.
[http://dx.doi.org/10.1016/j.bbamem.2007.11.008] [PMID: 18078805]
[35]
Bevers EM, Comfurius P, Zwaal RFA. Regulatory mechanisms in maintenance and modulation of transmembrane lipid asymmetry: Pathophysiological implications. Lupus 1996; 5(5): 480-7.
[http://dx.doi.org/10.1177/096120339600500531] [PMID: 8902787]
[36]
van Zoggel H, Hamma-Kourbali Y, Galanth C, et al. Antitumor and angiostatic peptides from frog skin secretions. Amino Acids 2012; 42(1): 385-95.
[http://dx.doi.org/10.1007/s00726-010-0815-9] [PMID: 21132338]
[37]
Neelabh Singh K, Rani J. Sequential and structural aspects of antifungal peptides from animals, bacteria and fungi based on bioinformatics tools. Probiotics Antimicrob Proteins 2016; 8(2): 85-101.
[http://dx.doi.org/10.1007/s12602-016-9212-3] [PMID: 27060002]
[38]
Aoki W, Ueda M. Characterization of antimicrobial peptides toward the development of novel antibiotics. Pharmaceuticals 2013; 6(8): 1055-81.
[http://dx.doi.org/10.3390/ph6081055] [PMID: 24276381]
[39]
Goodsell DS. Illustrations of the HIV life cycle. Curr Top Microbiol Immunol 2015; 389: 243-52.
[http://dx.doi.org/10.1007/82_2015_437] [PMID: 25716304]
[40]
Kołodziej M, Joniec J, Bartoszcze M, Mirski T, Gryko R. Peptides--a new strategy for combating viral infections. Przegl Epidemiol 2011; 65(3): 477-82.
[PMID: 22184952]
[41]
Mulder KC, Lima LA, Miranda VJ, Dias SC, Franco OL. Current scenario of peptide-based drugs: The key roles of cationic antitumor and antiviral peptides. Front Microbiol 2013; 4: 321.
[http://dx.doi.org/10.3389/fmicb.2013.00321] [PMID: 24198814]
[42]
Agrawal P, Bhalla S, Chaudhary K, Kumar R, Sharma M, Raghava GPS. In silico approach for prediction of antifungal peptides. Front Microbiol 2018; 9: 323.
[http://dx.doi.org/10.3389/fmicb.2018.00323] [PMID: 29535692]
[43]
Agrawal P, Bhagat D, Mahalwal M, Sharma N, Raghava GP. AntiCP 2.0: An updated model for predicting anticancer peptides. Brief Bioinform 2021; 22: bbaa153.
[44]
MacCallum JL, Tieleman DP. Hydrophobicity scales: A thermodynamic looking glass into lipid-protein interactions. Trends Biochem Sci 2011; 36(12): 653-62.
[http://dx.doi.org/10.1016/j.tibs.2011.08.003] [PMID: 21930386]
[45]
Thakur N, Qureshi A, Kumar M. AVPpred: Collection and prediction of highly effective antiviral peptides. Nucleic Acids Res 2012; 40: W199-204.
[http://dx.doi.org/10.1093/nar/gks450] [PMID: 22638580]
[46]
Roy A, Kucukural A, Zhang Y. I-TASSER: A unified platform for automated protein structure and function prediction. Nat Protoc 2010; 5(4): 725-38.
[http://dx.doi.org/10.1038/nprot.2010.5] [PMID: 20360767]
[47]
Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: Protein structure and function prediction. Nat Methods 2015; 12(1): 7-8.
[http://dx.doi.org/10.1038/nmeth.3213] [PMID: 25549265]
[48]
Wu S, Zhang Y. LOMETS: A local meta-threading-server for protein structure prediction. Nucleic Acids Res 2007; 35(10): 3375-82.
[http://dx.doi.org/10.1093/nar/gkm251] [PMID: 17478507]
[49]
Zheng W, Zhang C, Wuyun Q, Pearce R, Li Y, Zhang Y. LOMETS2: Improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins. Nucleic Acids Res 2019; 47(W1): W429-36.
[http://dx.doi.org/10.1093/nar/gkz384] [PMID: 31081035]
[50]
Zhang Y, Skolnick J. SPICKER: A clustering approach to identify near-native protein folds. J Comput Chem 2004; 25(6): 865-71.
[http://dx.doi.org/10.1002/jcc.20011] [PMID: 15011258]
[51]
Yang J, Zhang Y. I-TASSER server: New development for protein structure and function predictions. Nucleic Acids Res 2015; 43(W1): W174-81.
[http://dx.doi.org/10.1093/nar/gkv342] [PMID: 25883148]
[52]
Di Somma A, Moretta A, Cané C, et al. Structural and functional characterization of a novel recombinant antimicrobial peptide from Hermetia illucens. Curr Issues Mol Biol 2021; 44(1): 1-13.
[http://dx.doi.org/10.3390/cimb44010001] [PMID: 35723380]
[53]
Pettersen EF, Goddard TD, Huang CC, et al. UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem 2004; 25(13): 1605-12.
[http://dx.doi.org/10.1002/jcc.20084] [PMID: 15264254]
[54]
Khara JS, Obuobi S, Wang Y, et al. Disruption of drug-resistant biofilms using de novo designed short α-helical antimicrobial peptides with idealized facial amphiphilicity. Acta Biomater 2017; 57: 103-14.
[http://dx.doi.org/10.1016/j.actbio.2017.04.032] [PMID: 28457962]
[55]
Zelezetsky I, Tossi A. Alpha-helical antimicrobial peptides--using a sequence template to guide structure-activity relationship studies. Biochim Biophys Acta 2006; 1758(9): 1436-49.
[http://dx.doi.org/10.1016/j.bbamem.2006.03.021] [PMID: 16678118]
[56]
Fjell CD, Hiss JA, Hancock RE, Schneider G. Designing antimicrobial peptides: Form follows function. Nat Rev Drug Discov 2011; 11(1): 37-51.
[http://dx.doi.org/10.1038/nrd3591] [PMID: 22173434]
[57]
Tripathi JK, Pal S, Awasthi B, et al. Variants of self-assembling peptide, KLD-12 that show both rapid fracture healing and antimicrobial properties. Biomaterials 2015; 56: 92-103.
[http://dx.doi.org/10.1016/j.biomaterials.2015.03.046] [PMID: 25934283]
[58]
Häffner SM, Malmsten M. Influence of self-assembly on the performance of antimicrobial peptides. Curr Opin Colloid Interface Sci 2018; 38: 56-79.
[http://dx.doi.org/10.1016/j.cocis.2018.09.002]
[59]
Abdel Monaim SAH, Jad YE, El-Faham A, de la Torre BG, Albericio F. Teixobactin as a scaffold for unlimited new antimicrobial peptides: SAR study. Bioorg Med Chem 2018; 26(10): 2788-96.
[http://dx.doi.org/10.1016/j.bmc.2017.09.040] [PMID: 29029900]
[60]
Müller AT, Hiss JA, Schneider G. Recurrent neural network model for constructive peptide design. J Chem Inf Model 2018; 58(2): 472-9.
[http://dx.doi.org/10.1021/acs.jcim.7b00414] [PMID: 29355319]
[61]
Veltri D, Kamath U, Shehu A. Deep learning improves antimicrobial peptide recognition. Bioinformatics 2018; 34(16): 2740-7.
[http://dx.doi.org/10.1093/bioinformatics/bty179] [PMID: 29590297]
[62]
Di Somma A, Canè C, Moretta A, Duilio A. Interaction of Temporin-L Analogues with the E. coli FtsZ Protein. Antibiotics 2021; 10(6): 704.
[http://dx.doi.org/10.3390/antibiotics10060704] [PMID: 34208230]
[63]
Di Somma A, Avitabile C, Cirillo A, et al. The antimicrobial peptide Temporin L impairs E. coli cell division by interacting with FtsZ and the divisome complex. Biochim Biophys Acta, Gen Subj 2020; 1864(7): 129606.
[http://dx.doi.org/10.1016/j.bbagen.2020.129606] [PMID: 32229224]
[64]
Juretić D, Vukičević D, Petrov D, et al. Knowledge-based computational methods for identifying or designing novel, non-homologous antimicrobial peptides. Eur Biophys J 2011; 40(4): 371-85.
[http://dx.doi.org/10.1007/s00249-011-0674-7] [PMID: 21274708]
[65]
Chung CR, Jhong JH, Wang Z, et al. Characterization and identification of natural antimicrobial peptides on different organisms. Int J Mol Sci 2020; 21(3): 986.
[http://dx.doi.org/10.3390/ijms21030986] [PMID: 32024233]
[66]
Grafskaia EN, Polina NF, Babenko VV, et al. Discovery of novel antimicrobial peptides: A transcriptomic study of the sea anemone Cnidopus japonicus. J Bioinform Comput Biol 2018; 16(2): 1840006.
[http://dx.doi.org/10.1142/S0219720018400061] [PMID: 29361893]
[67]
Lata S, Sharma BK, Raghava GPS. Analysis and prediction of antibacterial peptides. BMC Bioinformatics 2007; 8(1): 263.
[http://dx.doi.org/10.1186/1471-2105-8-263] [PMID: 17645800]
[68]
Pearson CS, Kloos Z, Murray B, et al. Combined bioinformatic and rational design approach to develop antimicrobial peptides against Mycobacterium tuberculosis. Antimicrob Agents Chemother 2016; 60(5): 2757-64.
[http://dx.doi.org/10.1128/AAC.00940-15] [PMID: 26902758]
[69]
Dziuba B, Dziuba M. New milk protein-derived peptides with potential antimicrobial activity: An approach based on bioinformatic studies. Int J Mol Sci 2014; 15(8): 14531-45.
[http://dx.doi.org/10.3390/ijms150814531] [PMID: 25141106]
[70]
Li Y, Cai J, Du C, et al. Bioinformatic analysis and antiviral effect of Periplaneta americana defensins. Virus Res 2022; 308: 198627.
[http://dx.doi.org/10.1016/j.virusres.2021.198627] [PMID: 34785275]
[71]
Aminov RI. A brief history of the antibiotic era: Lessons learned and challenges for the future. Front Microbiol 2010; 1: 134.
[http://dx.doi.org/10.3389/fmicb.2010.00134] [PMID: 21687759]
[72]
Parisien A, Allain B, Zhang J, Mandeville R, Lan CQ. Novel alternatives to antibiotics: Bacteriophages, bacterial cell wall hydrolases, and antimicrobial peptides. J Appl Microbiol 2008; 104(1): 1-13.
[PMID: 18171378]
[73]
Schweizer F. Cationic amphiphilic peptides with cancer-selective toxicity. Eur J Pharmacol 2009; 625(1-3): 190-4.
[http://dx.doi.org/10.1016/j.ejphar.2009.08.043] [PMID: 19835863]
[74]
Suryawanshi SK, Chouhan U. Application of bioinformatics in the prediction and identification of potential antimicrobial peptides from Curcuma longa. Biosci Biotechnol Res Commun 2016; 9(2): 220-8.
[http://dx.doi.org/10.21786/bbrc/9.1/8]

Rights & Permissions Print Cite
Article Metrics
6
© 2024 Bentham Science Publishers | Privacy Policy