Abstract
Background The rise of antibiotic-resistant bacteria presents a pressing need for exploring new natural compounds with innovative mechanisms to replace existing antibiotics. Bacteriocins offer promising alternatives for developing therapeutic and preventive strategies in livestock, aquaculture, and human health. Specifically, those produced by LAB are recognized as GRAS and QPS. Methods In this study was used a deep learning neural network for binary classification of bacteriocin amino acid sequences, distinguishing those produced by LAB. The features were extracted using the k-mer method and vector embedding. Ten different groups were tested, combining embedding vectors and k-mers: EV, ‘EV+3-mers’, ‘EV+5-mers’, ‘EV+7-mers’, ‘EV+15-mers’, ‘EV+20-mers’, ‘EV+3-mers+5-mers’, ‘EV+3-mers+7-mers’, ‘EV+5-mers+7-mers’, and ‘EV+15-mers+20-mers’. Results Five sets of 100 characteristic k-mers unique to bacteriocins produced by LAB were obtained for values of k = 3, 5, 7, 15, and 20. Significant difference was observed between using only and concatenation. Specially, ‘5-mers+7-mers+EV ’ group showed superior accuracy and loss results. Employing k-fold cross-validation with k=30, the average results for loss, accuracy, precision, recall, and F1 score were 9.90%, 90.14%, 90.30%, 90.10%, and 90.10% respectively. Folder 22 stood out with 8.50% loss, 91.47% accuracy, and 91.00% precision, recall, and F1 score. Conclusions The model developed in this study achieved consistent results with those seen in the reviewed literature. It outperformed some studies by 3-10%. The lists of characteristic k-mers pave the way to identify new bacteriocins that could be valuable for therapeutic and preventive strategies within the livestock, aquaculture industries, and potentially in human health.