Abstract
Leguminous crops are vital to sustainable agriculture due to their ability to fix atmospheric nitrogen, improving soil fertility and reducing the need for synthetic fertilizers. Additionally, they are an excellent source of protein for both human consumption and animal feed. Antimicrobial peptides (AMPs), found in various leguminous seeds, exhibit broad-spectrum antimicrobial activity through diverse mechanisms, including interaction with microbial cell membranes and interference with cellular processes, making them valuable for enhancing crop resilience and food safety. In the field of plant sciences, computational biology methods have been instrumental in the discovery and optimization of AMPs. These methods enable rapid exploration of sequence space and the prediction of AMPs using deep learning technologies. Optimizing AMP annotations through computational design offers a strategic approach to enhance efficacy and minimize potential side effects, providing a viable alternative to conventional antimicrobial agents. However, the presence of overlapping sequences across multiple databases poses a challenge for creating a reliable dataset for AMP prediction. To address this, we conducted a comprehensive analysis of sequence redundancy across various AMP databases. These databases encompass a wide range of AMPs from different sources and with specific functions, including both naturally occurring and artificially synthesized AMPs. Our analysis revealed significant overlap, underscoring the need for a non-redundant AMP sequence database. We present the development of a new database that consolidates unique AMP sequences derived from leguminous seeds, aiming to create a more refined dataset for the binary classification and prediction of plant-derived AMPs. This database will support the advancement of sustainable agricultural practices by enhancing the use of plant-based AMPs in agroecology, contributing to improved crop protection and food security.