<span class="fontstyle۰">Plant Antimicrobial Peptides: Bioinformatics Approaches for Discovery, Characterization, and Functional Insights

Plant antimicrobial peptides (AMPs) are small, naturally occurring biomolecules that play crucial roles in the plant innate immune system. Their structural diversity, cationic nature, and stability under adverse conditions enable them to act effectively against a broad spectrum of pathogens, including fungi, bacteria, and viruses. Beyond biotic defense, AMPs also contribute to tolerance against abiotic stresses such as drought, salinity, and ultraviolet radiation. This review integrates findings from peer-reviewed studies and biological databases focusing on plant AMPs, with an emphasis on bioinformatics tools and computational approaches. Both conventional sequence-based analyses (e.g., BLAST, HMMER) and recent advances in machine learning and structural modeling (e.g., Deep-AmPEP, AlphaFold) were assessed to illustrate their role in AMP discovery and functional characterization. Comparative analyses reveal the evolution of bioinformatics methodologies and their successful application in crop species for large-scale AMP identification and in silico characterization. Case studies demonstrate how integrative computational pipelines can accelerate AMP discovery and provide practical insights for agricultural improvement. Additionally, the review highlights the potential for developing a comprehensive Iranian plant AMP database and species-specific predictive models to facilitate regional research. Plant AMPs represent promising molecules for biotechnology, agriculture, and pharmaceuticals. The integration of computational prediction, genetic engineering, and applied research will accelerate their use in crop improvement and therapeutic development. Future studies focusing on database expansion and machine learning applications are expected to further enhance our understanding and utilization of plant-derived AMPs.

کلیدواژه ها:

Antimicrobial Peptides ، Bioinformatics ، Machine Learning ، In Silico Prediction ، Bioinformatics Pipelines ، Database Development