With more and more consumers choosing clean label foods with natural and healthy ingredients for better lifestyle, and avoiding foods made with chemical additives to minimize the risk of health and disease, natural food antimicrobials play an increasing critical role in the clean label food market. However, commonly used food antimicrobials present various limitations. Therefore, novel natural antimicrobials are urgently needed by the food industry. The interest in natural antimicrobial peptides and proteins (AMPs) is increasing recently because of their broad range of activity, stability to commonly used food processes, which enable them to represent promising alternatives to traditional antimicrobials. Nevertheless, studies on the discovery and application of AMPs to foods are still quite limited. At present, only few bacterial AMPs, such as nisin, have been approved as a food antimicrobial in multiple countries, leaving thousands of other AMPs in the antimicrobial peptide databased (APD) untapped. It is logical to assume that there are more candidates like nisin in the database, which are suitable for food applications. The search, however, has been hindered as a consequence of limited information, inadequate tools, and ineffective research strategies. This presentation will introduce a novel approach for rapidly discovering potent antimicrobial peptides as natural food antimicrobials to improve safety, quality, and shelf-life of clean label food products. The study integrated a well-recognized Nebraska APD database, deep understanding of antimicrobial peptides, state-of-the-art machine learning algorithms, and solid scientific knowledge of natural food preservatives. How to develop effective applications of natural AMPs in food products also will be discussed. Promisingly, this approach may lead to a breakthrough solution for rapidly discovering novel and natural food antimicrobials of great benefit to the food industry, consumers, and environment.

Rapid discovery of novel antimicrobial peptides with machine learning algorithms to improve safety, quality, and shelf-life of clean label food products

ACS Spring 2022

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