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An Overview of Databases and Bioinformatics Tools for Plant Antimicrobial Peptides

期刊

CURRENT PROTEIN & PEPTIDE SCIENCE
卷 23, 期 1, 页码 6-19

出版社

BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1389203723666211222170342

关键词

AMPs; antimicrobial cationic peptides; data mining; drug design; physicochemical characterization; sequence analysis

资金

  1. Brazilian agency Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) [99999.013148/2013-03]
  2. Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) [143807/2020-0]

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Antimicrobial peptides (AMPs) are important candidates for drug development due to their broad-spectrum antimicrobial activity and rapid killing action. Computational strategies help minimize experimental time and cost. Despite increased availability, finding plant AMPs is still challenging, highlighting the need for further research and development.
Antimicrobial Peptides (AMPs) are small, ribosomally synthesized proteins found in nearly all forms of life. In plants, AMPs play a central role in plant defense due to their distinct physicochemical properties. Due to their broad-spectrum antimicrobial activity and rapid killing action, plant AMPs have become important candidates for the development of new drugs to control plant and animal pathogens that are resistant to multiple drugs. Further research is required to explore the potential uses of these natural compounds. Computational strategies have been increasingly used to understand key aspects of antimicrobial peptides. These strategies will help to minimize the time and cost of wet-lab experimentation. Researchers have developed various tools and databases to provide updated information on AMPs. However, despite the increased availability of antimicrobial peptide resources in biological databases, finding AMPs from plants can still be a difficult task. The number of plant AMP sequences in current databases is still small and yet often redundant. To facilitate further characterization of plant AMPs, we have summarized information on the location, distribution, and annotations of plant AMPs available in the most relevant databases for AMPs research. We also mapped and categorized the bioinformatics tools available in these databases. We expect that this will allow researchers to advance in the discovery and development of new plant AMPs with potent biological properties. We hope to provide insights to further expand the application of AMPs in the fields of biotechnology, pharmacy, and agriculture.

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