AMPfinder is a simple, yet accurate, computational pipeline that processes either genomes and proteome sequences. The search for AMPs is based on alignment searching the existing antimicrobial peptide database and predicting on the feature model in amino acid sequence obtained from the translation of the original transcriptome sequence data.
AMPfinder is a free software tool that combines ORF prediction and accurately classification of the AMPs from protein or nucleotide data, which performs translation of the input transcriptome data by using Prodigal, and selects short sequences containing ORF and signal peptide cleavage sites. Then, DIAMOND was used for homology detection and machine learning prediction model were used to search for potential AMPs, in which case all known or potential motifs will be revealed and classified. Due to the combination of various search methods, AMPfinder searcher allows to obtain the most complete repertoire of AMPs for one or more transcriptomes in a short amount of time. Therefore, AMPfinder seems to be the most suitable tool for rapid screening of potential AMPs.
Anti-microbial peptides (AMPs), naturally encoded by genes and generally containing 12–100 amino acids, are crucial components of the innate immune system and can protect the host from various pathogenic bacteria and viruses. In recent years, the widespread use of antibiotics has resulted in the rapid growth of antibiotic-resistant microorganisms that often induce critical infection and pathogenesis. Since antibiotic resistance is a growing phenomenon in contemporary medicine, newer antibiotic treatments of the antimicrobial peptides can provide a possible solution. In the past, there have been several databases or tools dedicated to integrating the information. With the development and maturation of high-throughput sequencing technologies in recent years, more protein sequences need to be specific and accurate to reduce the cost of experiments, so we need a tool that can be efficiently calculated and accurately predicted.
