Machine Learning Prediction and Experimental Validation of Antimicrobial Peptide Activity Differences against Gram-Positive and Gram-Negative Bacteria.

Antimicrobial peptides (AMPs) are primary candidates for addressing bacterial resistance. Although their target spectrum specificity varies significantly between Gram-positive and Gram-negative bacteria, current predictive models generally lack experimental validation. In this study, we constructed various machine learning models based on known sequences to systematically evaluate the performance of k-mer frequencies, physicochemical properties, and hybrid features in distinguishing the AMP target specificity. Results indicated that the random forest model based on eight key physicochemical properties performed best, achieving a test set accuracy of 82.09% with balanced classification and robust generalization. Feature importance analysis revealed that hydrophilicity and isoelectric point (pI) are the core physicochemical factors determining the target spectrum differences. The model was rigorously validated through a dual-track approach: first, via the synthesis and in vitro testing of 18 novel protozoan-derived AMPs (overall accuracy 66.67%) and, second, through a blind test on 55 independent external sequences, achieving a robust accuracy of 81.82%. Furthermore, the framework successfully identified candidates with potent activity against multidrug-resistant pathogens including Pseudomonas aeruginosa and Klebsiella pneumoniae. This experimentally validated predictive framework provides a reliable computational tool for the high-throughput screening and rational design of targeted antimicrobial peptides.