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The global antibiotic resistance issue constitutes a driving force for developing host defense antimicrobial peptides (AMPs) into a new generation of antibiotics. To facilitate this development, we report the antimicrobial peptide database version 6 (APD6) with (i) the consolidated database platform, (ii) the most comprehensive AMP information pipeline (AMPIP), and (iii) the expanded wheel of function. As of 18 March 2025, the APD6 platform housed records for 5188 peptides, including 3306 natural, 1380 synthetic, and 239 predicted AMPs with systematic classification schemes for each group. Based on the refined dataset, we present an updated view and findings on natural AMPs. Natural AMPs provide a fundamental dataset for peptide design and predicting potential AMPs. While current artificial intelligence prediction of AMPs is limited to activity and hemolysis, the APD6 provides new positive and negative datasets (e.g. pH, salt, serum effects, and resistance) for building advanced AI prediction models to identify more robust antibiotics. The AMPIP covers information ranging from peptide discovery, in vitro/in vivo activity and toxicity data, to clinical trials. In addition, the APD6 (available at https://aps.unmc.edu) contains an expanded wheel of peptide functions (e.g. anticancer and antidiabetic), allowing for developing peptide therapeutics outside the antibiotic arena.

The global antibiotic resistance issue constitutes a driving force for developing host defense antimicrobial peptides (AMPs) into a new generation of antibiotics. To facilitate this development, we report the antimicrobial peptide database version 6 (APD6) with (i) the consolidated database platform, (ii) the most comprehensive AMP information pipeline (AMPIP), and (iii) the expanded wheel of function. As of 18 March 2025, the APD6 platform housed records for 5188 peptides, including 3306 natural, 1380 synthetic, and 239 predicted AMPs with systematic classification schemes for each group. Based on the refined dataset, we present an updated view and findings on natural AMPs. Natural AMPs provide a fundamental dataset for peptide design and predicting potential AMPs. While current artificial intelligence prediction of AMPs is limited to activity and hemolysis, the APD6 provides new positive and negative datasets (e.g. pH, salt, serum effects, and resistance) for building advanced AI prediction models to identify more robust antibiotics. The AMPIP covers information ranging from peptide discovery, in vitro/in vivo activity and toxicity data, to clinical trials. In addition, the APD6 (available at https://aps.unmc.edu) contains an expanded wheel of peptide functions (e.g. anticancer and antidiabetic), allowing for developing peptide therapeutics outside the antibiotic arena.

In 2023, the Antimicrobial Peptide Database (currently available at https://aps.unmc.edu) is 20-years-old. The timeline for the APD expansion in peptide entries, classification methods, search functions, post-translational modifications, binding targets, and mechanisms of action of antimicrobial peptides (AMPs) has been summarized in our previous Protein Science paper. This article highlights new database additions and findings. To facilitate antimicrobial development to combat drug-resistant pathogens, the APD has been re-annotating the data for antibacterial activity (active, inactive, and uncertain), toxicity (hemolytic and nonhemolytic AMPs), and salt tolerance (salt sensitive and insensitive). Comparison of the respective desired and undesired AMP groups produces new knowledge for peptide design. Our unification of AMPs from the six life kingdoms into "natural AMPs" enabled the first comparison with globular or transmembrane proteins. Due to the dominance of amphipathic helical and disulfide-linked peptides, cysteine, glycine, and lysine in natural AMPs are much more abundant than those in globular proteins. To include peptides predicted by machine learning, a new "predicted" group has been created. Remarkably, the averaged amino acid composition of predicted peptides is located between the lower bound of natural AMPs and the upper bound of synthetic peptides. Synthetic peptides in the current APD, with the highest cationic and hydrophobic amino acid percentages, are mostly designed with varying degrees of optimization. Hence, natural AMPs accumulated in the APD over 20 years have laid the foundation for machine learning prediction. We discuss future directions for peptide discovery. It is anticipated that the APD will continue to play a role in research and education.

Natural products offer an important avenue to novel therapeutics against drug-resistant bacteria, viruses, fungi, parasites, and cancer. However, there are numerous hurdles and challenges in discovering such molecules, including antimicrobial peptides (AMPs). While a thorough characterization of AMPs is limited by the amount of material, existing technology, and researcher's expertise, peptide classification is complicated by incomplete information as well as different methods proposed for AMPs from bacteria, plants, and animals. This article describes unified classification schemes for natural AMPs on a common platform: the Antimicrobial Peptide Database (APD; https://aps.unmc.edu). The various criteria for these unified classifications include peptide biological source, biosynthesis machinery, biological activity, amino acid sequence, mechanism of action, and three-dimensional structure. To overcome the problem with a limited number of known 3D structures, a universal peptide classification has also been refined and executed in the APD database. This universal method, based on the spatial connection patterns of polypeptide chains, is independent of peptide source, size, activity, 3D structure, or mechanism of action. It facilitates information registration, naming, exchange, decoding, prediction, and design of novel antimicrobial peptides.

The antimicrobial peptide database (APD) has served the antimicrobial peptide field for 18 years. Because it is widely used in research and education, this article documents database milestones and key events that have transformed it into the current form. A comparison is made for the APD peptide statistics between 2010 and 2020, validating the major database findings to date. We also describe new additions ranging from peptide entries to search functions. Of note, the APD also contains antimicrobial peptides from host microbiota, which are important in shaping immune systems and could be linked to a variety of human diseases. Finally, the database has been re-programmed to the web branding and latest security compliance of the University of Nebraska Medical Center. The reprogrammed APD can be accessed at https://aps.unmc.edu.

This article is written for the 2020 tool issue of Protein Science. It briefly introduces the widely used antimicrobial peptide database, initially online in 2003. After a description of the main features of each database version and some recent additions, the focus is on the peptide design parameters for each of the four unified classes of natural antimicrobial peptides (AMPs). The amino acid signature in AMPs varies substantially, leading to a variety of structures for functional and mechanistic diversity. Also, Nature is a master of combinatorial chemistry by deploying different amino acids onto the same structural scaffold to tune peptide functions. In addition, the single-domain AMPs may be posttranslationally modified, self-assembled, or combined with other AMPs for function. Elucidation of the design principles of natural AMPs will facilitate future development of novel molecules for various applications.

The antimicrobial peptide database (APD, http://aps.unmc.edu/AP/) is an original database initially online in 2003. The APD2 (2009 version) has been regularly updated and further expanded into the APD3. This database currently focuses on natural antimicrobial peptides (AMPs) with defined sequence and activity. It includes a total of 2619 AMPs with 261 bacteriocins from bacteria, 4 AMPs from archaea, 7 from protists, 13 from fungi, 321 from plants and 1972 animal host defense peptides. The APD3 contains 2169 antibacterial, 172 antiviral, 105 anti-HIV, 959 antifungal, 80 antiparasitic and 185 anticancer peptides. Newly annotated are AMPs with antibiofilm, antimalarial, anti-protist, insecticidal, spermicidal, chemotactic, wound healing, antioxidant and protease inhibiting properties. We also describe other searchable annotations, including target pathogens, molecule-binding partners, post-translational modifications and animal models. Amino acid profiles or signatures of natural AMPs are important for peptide classification, prediction and design. Finally, we summarize various database applications in research and education.

The antimicrobial peptide database (APD, http://aps.unmc.edu/AP/main.php) has been updated and expanded. It now hosts 1228 entries with 65 anticancer, 76 antiviral (53 anti-HIV), 327 antifungal and 944 antibacterial peptides. The second version of our database (APD2) allows users to search peptide families (e.g. bacteriocins, cyclotides, or defensins), peptide sources (e.g. fish, frogs or chicken), post-translationally modified peptides (e.g. amidation, oxidation, lipidation, glycosylation or d-amino acids), and peptide binding targets (e.g. membranes, proteins, DNA/RNA, LPS or sugars). Statistical analyses reveal that the frequently used amino acid residues (>10%) are Ala and Gly in bacterial peptides, Cys and Gly in plant peptides, Ala, Gly and Lys in insect peptides, and Leu, Ala, Gly and Lys in amphibian peptides. Using frequently occurring residues, we demonstrate database-aided peptide design in different ways. Among the three peptides designed, GLK-19 showed a higher activity against Escherichia coli than human LL-37.

An antimicrobial peptide database (APD) has been established based on an extensive literature search. It contains detailed information for 525 peptides (498 antibacterial, 155 antifungal, 28 antiviral and 18 antitumor). APD provides interactive interfaces for peptide query, prediction and design. It also provides statistical data for a select group of or all the peptides in the database. Peptide information can be searched using keywords such as peptide name, ID, length, net charge, hydrophobic percentage, key residue, unique sequence motif, structure and activity. APD is a useful tool for studying the structure-function relationship of antimicrobial peptides. The database can be accessed via a web-based browser at the URL: http://aps.unmc.edu/AP/main.html.