{"id":130976,"date":"2026-04-03T09:39:37","date_gmt":"2026-04-03T14:39:37","guid":{"rendered":"https:\/\/www.unmc.edu\/newsroom\/?p=130976"},"modified":"2026-04-03T09:39:39","modified_gmt":"2026-04-03T14:39:39","slug":"antimicrobial-peptide-database-is-expanded-and-improved","status":"publish","type":"post","link":"https:\/\/www.unmc.edu\/newsroom\/2026\/04\/03\/antimicrobial-peptide-database-is-expanded-and-improved\/","title":{"rendered":"Antimicrobial peptide database is expanded and improved"},"content":{"rendered":"
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This story appeared in the department’s 2025 annual report<\/em><\/p>\n\n\n\n

Research on antimicrobial peptides has come a long way in the past 45 years. And the Antimicrobial Peptide Database created by the laboratory of Guangshun Wang, PhD, a professor in UNMC\u2019s Department of Pathology, Microbiology and Immunology, has been embraced around the globe by scientists, who have cited it thousands of times in their research.<\/p>\n\n\n\n

The professor\u2019s research focuses on innate immune antimicrobial peptides and their potential application as novel antimicrobials to combat drug-resistant pathogens. To promote research and education, his lab has been updating and expanding the Antimicrobial Peptide Database for two decades. This unique and comprehensive tool laid the foundation for his lab to decipher design principles of antimicrobial peptides and develop the database filtering technology for designing potent, selective and stable peptides with demonstrated efficacy in animal models.<\/p>\n\n\n\n

Dr. Wang said that early in the database development, he wanted to understand natural peptides. \u201cI thought if we understand that, even partially, we might be able to design better peptides. That\u2019s why we limited our scope and initially restricted inclusion of synthetic peptides into our database.\u201d The number of peptides discovered has increased through the succeeding versions of the APD, but it has been gradual, he said, because discovery of natural peptides is not easy.<\/p>\n\n\n\n

After the international symposium in 2023 marking the 20th<\/sup> anniversary of the database, Dr. Wang and Dr. Joseph Khoury, department chairman, turned their eye to the future. \u201cWe started to think maybe we should expand the scope of data we can collect in this database\u201d and make it even more useful. Therefore, this year brought a new version of the database, APD6. \u201cAfter database reconfiguration, we can now add a representative set of synthetic peptides without losing the search power for natural antimicrobial peptides. The AI-predicted peptides will be included if they have actually been made in the lab and confirmed to have antimicrobial activity.\u201d<\/p>\n\n\n\n

The success of AlphaFold in protein structure prediction has stimulated the interest in predicting novel antibiotics in the same manner.  While much progress has been made, Dr. Wang said there is a long way to go before AI-predicted peptides reach the market, calling the work in its early stages. \u201cOne of the major contributions of the APD6 is that we outlined a blueprint for advanced AI predictions. The database collects information for the entire information pipeline, from peptide discovery through all the procedures\u2014at the end you will do in vivo<\/em> assay, in vivo <\/em>toxicity, then you will do efficacy and production, then you have clinical trials. Step by step, that\u2019s the pipeline. And information for all these steps can be searched in the database.\u201d<\/p>\n\n\n\n

Further, Dr. Wang identified: \u201cThe data types can determine AI prediction quality. We don\u2019t collect everything into the database. There\u2019s a saying, garbage in, garbage out, or GIGO in computing science. We are doing it relatively conservatively by establishing a set of criteria for data registration.\u201d The APD6 also proposes data filtering to remove noises, thereby improving AI prediction quality. Dr. Wang plans to report such filtering procedures in future database versions with the establishment of the AI prediction pipeline in collaboration with colleagues.  <\/p>\n\n\n\n

What\u2019s new:<\/strong><\/p>\n\n\n\n