Submitted:
30 September 2025
Posted:
02 October 2025
You are already at the latest version
Abstract
Keywords:
1. Introduction
2. Broad-Spectrum Activity of Peptides
2.1. Antibacterial Activity
2.2. Antiviral Activity
2.3. Antifungal Activity
2.4. Antiparasitic Activity
3. Approaches for Obtaining AMPs
3.1. Peptide Production via Chemical Synthesis
3.2. Peptide Production via Enzymatic Pathways
3.3. Peptide Extraction from Natural Sources
3.4. Peptide Production via Recombinant Technology
4. Biochemical Characteristics of AMPs
4.1. Structure
4.2. Charge, pH, saline concentration
4.3. Hidrofobicity
4.4. Size
5. Antimicrobial Peptide Action Mechanism
5.1. Extracellular Target AMPs
5.2. Intracelular target AMPs
5.3. Imunomodulatory AMPs
6. Clinical trial of AMPs
7. Computational Approaches for AMPs Discovery and Design
8. AMPs and their Use in the Food Industry
9. AMPs and Their Use in Veterinary Medicine
10. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
| BAP | Bioactive Peptide |
| AMP | Antimicrobial Peptide |
| APD3 | Antimicrobial Peptide Database 3 |
| AMR | Antibiotic Resistance |
| WHO | World Health Organization |
| MERS | Middle East respiratory syndrome |
| SARS | Severe acute respiratory syndrome |
| AVP | Antiviral peptides |
| AFP | Antifungal peptides |
| SPPS | Solid-phase Peptide Synthesis |
| HBTU | O-benzotriazole-N,N,N′,N′-tetramethyl-uronium-hexafluoro-phosphate |
| HATU | O-(7-azabenzotriazol-1-yl)-N,N,N′,N′-tetramethyluronium hexafluorophosphate |
| DIC | N,N′-diisopropylcarbodiimide |
| CCK | Cyclic Cystine Knot |
| DMF | Dimethylformamide |
| UAE | Ultrasound-assisted extraction |
| PEF | Pulsed electric fields |
| HHP | High hydrostatic pressure |
| AI | Artificial Intelligence |
| ML | Machine Learning |
| GST | Gglutathione S-transferase |
| MBP | Maltose-binding protein |
| GRAS | Generally recognized as safe |
| PG | Phosphatidylglycerol |
| CL | Cardiolipin |
| PS | Phosphatidylserine |
| LPS | Lipopolysaccharides |
| HSPG | Heparan sulfate proteoglycan |
| HCMV | Human cytomegaloviruses |
| MCMV | Murine cytomegaloviruses |
| DHFR | Dihydrofolate reductase |
| TCA | Tricarboxylic acid |
| SQR | Succinate-coenzyme Q reductase |
| ROS | Reactive oxygen species |
| HSV-1 | Simplex virus type 1 |
| LBP | Binding protein |
| FDA | Food and Drug Administration |
| IV | Intravenous injections |
| IM | Intramuscular injections |
| SC | Subcutaneous injections |
| CS/SA | Chitosan/sodium alginate |
| QSAR | Quantitative Structure-Activity Relationship |
| CNNs | Convolutional Neural Networks |
| DNNs | Deep Neural Networks |
| RNNs | Recurrent Neural Networks |
| VAEs | Variational Autoencoders |
| GANs | Generative Adversarial Networks |
| MRSA | Methicillin-resistant Staphylococcus aureus |
| DL | Deep learning |
| SVM | Support vector machines |
| AIR | Ambiguous interaction restraints (AIRs). |
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| Criterion | Chemical Synthesis | Enzymatic | Natural | Recombinant |
|---|---|---|---|---|
| Cost | High | Moderate | Variable | Low |
| Yield | Medium | Low | Very Low | High |
| Scalability | High | Moderate | Low | Very High |
| Technical Complexity | High | Low | Medium | High |
| Application Versatility | Very High | Moderate | Low | High |
| Peptide Name | Sequence | Structure | Source | Biological Activity | Medical use | Target site | Delivery path | Company | Reference |
|---|---|---|---|---|---|---|---|---|---|
| Bacitracin | Leu-D-Glu Ile-Lys-D-Orn-Ile-D-Phe-His-D-Asp-Asn | ![]() |
Bacillus licheniformis | Antibacterial | Prevent wound infections, pneumonia and empyema in infants, skin and eye infections. | Cell wall | Topical | Various companies | [95] |
| Dalbavancin Xydalba | Not available | ![]() |
Semi synthetic |
Antibacterial | Acute bacterial skin infections, Osteomyelitis and septic arthritis | Cell wall | IV | DALVANCE® | [96] |
| Daptomycin Cubicin |
decanoyl-Trp-Asn-Asp-Thr-Gly- Orn-Asp-D-Ala-Asp-Gly-D-Ser-Glu(3R-Me)-Asp (Ph(2-NH2)) | ![]() |
Streptomyces roseosporus | Antibacterial | Complicated skin infections (cSSSI) and bloodstream infections (bacteremia) | Cell membrane | IV | CUBICIN® | [97] |
| Enfuvirtide Fuzeon T20 |
YTSLIHSLIEESQNQQEKNEQELLELDKWASLWNWF | ![]() |
Synthetic | Anti-HIV | Human Immunodeficiency Virus (HIV) Infections; AIDS | Fusion protein gp41 | SC | FUZEON® |
[98] |
| Gramicidin D | VGALAVVVWLW LWLW-ethanolamine | ![]() |
Bacillus brevis | Antibacterial | Skin lesions, surface wounds and eye infections | Cell membrane | Topical | Various companies |
[99] |
| Gramicidin S | cyclo[Leu-D-Phe-Pro-Val-Orn-Leu-D-Phe-Pro-Val-Orn] | ![]() |
Bacillus brevis | Antibacterial | Against bacteria and fungi restricted use as spermicide and to treat genital ulcers | Cell membrane | Topical | Various companies | [100] |
| Obiltoxaximab | Not available | Not available | monoclonal antibody | Antibacterial | Treatment and prevention of inhalational anthrax | Antitoxin | IV | ANTHIM® | [101] |
| Oritavancin | Not available | Not available | Semi synthetic |
Antibacterial | Acute bacterial skin | Cell wall | IV | KIMYRSA™ ORBACTIV® | [102] |
| Palivizumab | Not available | ![]() |
monoclonal antibody |
Antiviral | Prevent serious lung infections (such as pneumonia) that are caused by respiratory syncytial virus-RSV | Blocking viral replication | IM | SYNAGIS® | [103] |
| Polymyxin B | Not available | ![]() |
Bacillus polymyxa | Antibacterial | Infections of the urinary tract, meninges, and blood stream | Cell membrane | IV | Various companies | [104] |
| Polymyxin E Colistin |
6-mh-DabTDab [γDablLDabDabT] |
![]() |
Bacillus polymyxa | Antibacterial | Acute or chronic infections due to gram-negative bacilli | Cell membrane | Oral | Various companies | [105] |
| Raxibacumab | Not available | Not available | monoclonal antibody | Antibacterial | Treatment and prevention of inhalational anthrax | Antitoxin | IV | RAXIBACUMAB ® | [106] |
| Telavancin TD-6424 |
Not available | ![]() |
Semisynthetic | Antibacterial | Osteomyelitis and bacterial infections | Cell membrane | IV | VIBATIV® | [107] |
| Tyrothricin | Not available | ![]() |
Brevibacillus parabrevis | Antibacterial Antifungal | Infected skin and infected oropharyngeal mucous membranes | Cell membrane | Topical/ Oral |
Various companies | [108,109] |
| Thymalfasin | Ac-Ser-Asp-Ala-Ala-Val-Asp-Thr-Ser-Ser-Glu-Ile-Thr-Thr-Lys-Asp-Leu-Lys-Glu-Lys-Lys-Glu-Val-Val-Glu-Glu-Ala-Glu-Asn-OH | Not available
|
Synthetic | Antiviral | Hepatitis B and C. Boost the immune response in the treatment of other diseases. |
Immuno modulator |
IV | ZADAXIN® | [110] |
| Vancomycin | Not available | ![]() |
Amycolatopsis orientalis | Antibacterial | Septicemia, infective endocarditis, skin, bone and lower respiratory tract infections. | Cell wall | IV/Oral | Various companies | [111] |
| Database Name | Functionality | Additional Information | URL | Reference |
|---|---|---|---|---|
| ACEP | Identification of AMPs. | The classification method is based on deep learning (DL). | https://github.com/Fuhaoyi/ACEP | [145] |
| ADAPTABLE | Designing novel peptides, predicting their activities, and identifying functional motifs. | Webserver and data-miner of antimicrobial peptides. | http://gec.upicardie.fr/adaptable/ | [146] |
| AFP-MFL | A novel deep learning model that can predict antifungal peptides. | It only needs the peptide sequence to run. | https://inner.weigroup.net/AFPMFL/#/ | [147] |
| AlphaFold | To predict a protein/peptide’s 3D structure based on amino acid sequence. | AI system developed by Google DeepMind. | https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb | [148,149] |
| AntiBP 3.0 | A web-tool for predicting, scanning and designing AMPs. | Based on machine learning techniques. | https://webs.iiitd.edu.in/raghava/antibp3/ | [150] |
| AMP- Scanner | A web server tool for predicting if it is an AMP based on amino acid sequence. | Only includes bacteria targets. | https://www.dveltri.com/ascan/ | [151] |
| AMPA | A web server tool for identifying active regions in antimicrobial proteins. | The algorithm uses an antimicrobial propensity scale to generate an antimicrobial profile. | https://tcoffee.crg.eu/apps/ampa/do | [152,153] |
| AMPDeep | Deep learning approach to predict hemolytic activity of AMPs. | It was built on Python. | https://github.com/milad73s/AMPDeep | [154] |
| amPEPpy | A python application for predicting antimicrobial peptide sequences. | The classification method is based on random forest. | https://github.com/tlawrence3/amPEPpy | [155] |
| AmpGram | A web server tool for identification of AMPs. | The classification method is based on random forest and n-gram analysis. | http://biongram.biotech.uni.wroc.pl /AmpGram/ | [156] |
| AutoDock | A tool to predict how small molecules bind to a receptor of know 3D structure. | It has two options: AutoDock-GPU and AutoDock Vina. | https://autodock.scripps.edu/ | [139] |
| AxPEP | It is a collection of sequence-based machine learning methods for AMPs prediction. | The classification method is based on random forest. | https://app.cbbio.online/ampep/home | [157] |
| CABS-dock | A web server tool for flexible protein-peptide docking. | It requires the sequence of a protein receptor and a peptide sequence. | https://biocomp.chem.uw.edu.pl/CABSdock | [141] |
| CHARMM36 | Broad-scope molecular simulation software for complex environments. | There is no cost to academic students. | https://academiccharmm.org/ | [143] |
| CS-AMPPred | An SVM-based (support vector machines) tool to predict antimicrobial activity in cysteine-knotted proteins. | Were based on 310 AMPs and 310 non-antimicrobial peptide sequences. | https://sourceforge.net/projects/csamppred/ | [158] |
| Cysmotif Searcher | A Perl package for revealing peptide sequences possessing cysteine motifs. | Cysteine motifs are common to various families of AMPs and others cysteine-rich peptides. | https://github.com/fallandar/cysmotifsearcher | [159] |
| deepAMP | A tool for predicting protein AMPylation sites from binary profile representation. | The classification method is based on a convolutional neural network. | https://github.com/MehediAzim/DeepAmp | [160] |
| GROMACS | Powerful open-source suite for molecular dynamics simulation and analysis. | Broad spectrum of calculation types, preparation and analysis tools. | https://manual.gromacs.org/archive/4.6.7/online/speptide.html | [142] |
| HADDOCK | A web platform for biomolecular docking simulations. | A data-driven docking approach guided by ambiguous interaction restraints (AIRs). | https://rascar.science.uu.nl/haddock2.4/ | [140] |
| iAMPCN | DL approach to identifying antimicrobial peptides and their functional activities. | It was built on Python. | https://github.com/joy50706/iAMPCN | [161] |
| iTASSER | DL approach to identifying antimicrobial peptides and their functional activities. | It was built on Python. | https://zhanggroup.org/I-TASSER/ | [162] |
| Joker | A web tool to predict structure of a protein. | It also predicts the function. | https://github.com/williamfp7/Joker | [163] |
| MARTINI | Coarse-grained force field for biomolecular simulations, parameterized using experimental data and atomistic simulations. | Based on a four-to-one mapping scheme. | https://cgmartini.nl/ | [144] |
| MLBP | Multi-label DL approach to identifying multi-functional bioactive peptide functions. | It was built on Python. | https://github.com/tangwending/MLBP | [164] |
| ModlAMP | A Python package for working with any sequence of natural amino acids. | It includes the following modules: sequence generation, sequence library analysis and description calculation. | https://modlamp.org/ | [165] |
| NAMD | A highly scalable software for parallel molecular dynamics simulations of large biomolecules. | It uses the program VHD for simulation setup and trajectory analysis. | https://www.ks.uiuc.edu/Research/namd/ | [166] |
| PEP2D | A web tool for predicting secondary structure of peptides. | The model was trained and tested based on 3100 peptide structures. | https://webs.iiitd.edu.in/raghava/pep2d/ | [167] |
| PEP-FOLD 4 | A de novo approach to predict peptide structure from amino acid sequences. | The peptides should have 5 to 50 amino acids. | https://bioserv.rpbs.univparisdiderot.fr/services/PEP-FOLD4/ #overview | [39] |
| RoseTTAFold | A Python-based AI tool for protein/peptide structure prediction. | The structure prediction method is based on DL. | https://github.com/RosettaCommons/RoseTTAFold?tab=readme-ov-file | [137] |
| sAMPpred-GAT | A web tool for identification of AMPs. | The program uses graphs constructed based on predicted peptide structures. | http://bliulab.net/sAMPpred-GAT/server | [168] |
| SWISS-MODEL | A web tool for protein structure prediction and modeling. | It supports interactive modeling for both simple and complex needs. | wissmodel.expasy.org | [169] |
| VMD | A program to display, animate and analyze large biomolecules systems. | It has 3-D graphics | https://www.ks.uiuc.edu/Research/vmd/ | [169] |
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