Antimicrobial Peptides (AMPs) evolutionarily conserved effectors of innate immunity, have emerged as multifunctional agents with broad-spectrum antimicrobial activity, immunomodulatory capabilities, and potential applicability in cancer therapy. Despite their functional diversity and biological potency, the clinical translation of AMPs remains constrained by significant challenges, including proteolytic instability, hemolytic toxicity, high production costs, and protracted development pipelines. This review examines how the diverse molecular mechanisms through which AMPs exert antimicrobial effects-including membrane disruption, intracellular targeting, and immunomodulation-are intrinsically linked to their structural diversity and ecological breadth. We critically evaluate engineering strategies that improve developability, including rational sequence modification, nano/targeted delivery, optimized formulations, and combination regimens with antibiotics or bacteriophages. As a complementary perspective, we briefly summarize recent progress in computational prediction and AI-driven screening/design for AMP discovery and multi-objective optimization, while highlighting major limitations such as dataset bias, scarcity of reliable negative data, and experimental validation bottlenecks. Beyond infectious disease, we discuss the impact of AMPs on reshaping the tumor microbiota-immune axis, revealing a dual function in both microbial control and immune regulation within oncogenic contexts. Overall, this review provides a balanced appraisal of evidence and translational pathways for advancing AMP-based therapeutics.
Current bioinformatics approaches for bacterial diagnostic target discovery remain constrained by their reliance on gene annotations and fixed-boundary genome segmentation, which overlook unannotated intergenic regions and introduce sequence-truncation artifacts. Here, we developed an open-source, annotation-independent pan-genomic pipeline featuring an overlapping sliding-window algorithm (500-bp window, 100-bp step) and a three-tier subtractive screening funnel. Using Staphylococcus aureus as a model, the pipeline screened 1,629 genomes against 852 non-S. aureus Staphylococcus genomes and >20,000 background bacterial genomes. Seven highly conserved, unannotated targets (SA-1 to SA-7) were identified, with all seven translated into qPCR primer sets (SAP-1 to SAP-7), among which three (SAP-1 to SAP-3) were further characterized by in vitro experiments. Multi-layer in silico evaluation demonstrated 100% intraspecific sensitivity and zero cross-reactivity against background genomes, including the S. aureus complex. In vitro testing using crude cell lysates confirmed specific amplification of S. aureus DNA without non-target cross-reactivity, establishing a qualitative limit of detection (LOD) of 10
5
CFU/mL. Additional computational validation on draft genomes, raw sequencing reads, near-neighbor species, and a clinical truth set corroborated marker robustness under realistic conditions. This framework successfully circumvents conventional gene-centric limitations, providing a generalizable computational strategy for target discovery across other high-priority bacterial pathogens.
Yuyang Zhou, Jiayi Wang, Junhua Xiao et al.· Journal of Bioinformatics an...· 0 citations