Emerging infectious diseases (EID) are serious problems caused by fungi in humans and plant species. They are a severe threat to food security worldwide. In our current work, we have developed a support vector machine (SVM)-based model that attempts to design and predict therapeutic plant-derived antifungal peptides (PhytoAFP). The residue composition analysis shows the preference of C, G, K, R, and S amino acids. Position preference analysis shows that residues G, K, R, and A dominate the N-terminal. Similarly, residues N, S, C, and G prefer the C-terminal. Motif analysis reveals the presence of motifs like NYVF, NYVFP, YVFP, NYVFPA, and VFPA. We have developed two models using various input functions such as mono-, di-, and tripeptide composition, as well as binary, hybrid, and physiochemical properties, based on methods that are applied to the main data set. The TPC-based monopeptide composition model achieved more accuracy, 94.4%, with a Matthews correlation coefficient (MCC) of 0.89. Correspondingly, the second-best model based on dipeptides achieved an accuracy of 94.28% under the MCC 0.89 of the training dataset.
- Publikační typ
- časopisecké články MeSH
Emerging infectious diseases (EIDs) are a severe problem caused by fungi in human and plant species across the world. They pose a worldwide threat to food security as well as human health. Fungal infections are increasing now day by day worldwide, and the current antimycotic drugs are not effective due to the emergence of resistant strains. Therefore, it is an urgent need for the finding of new plant-origin antifungal peptides (PhytoAFPs). Huge numbers of peptides were extracted from different plant species which play a protective role against fungal infection. Hundreds of plant-origin peptides with antifungal activity have already been reported. So there is a requirement of a dedicated platform which systematically catalogs plant-origin peptides along with their antifungal properties. PlantAFP database is a resource of experimentally verified plant-origin antifungal peptides, collected from research articles, patents, and public databases. The current release of PlantAFP database contains 2585 peptide entries among which 510 are unique peptides. Each entry provides comprehensive information of a peptide that includes its peptide sequence, peptide name, peptide class, length of the peptide, molecular mass, antifungal activity, and origin of peptides. Besides this primary information, PlantAFP stores peptide sequences in SMILES format. In order to facilitate the user, many tools have been integrated into this database that includes BLAST search, peptide search, SMILES search, and peptide-mapping is also included in the database. PlantAFP database is accessible at http://bioinformatics.cimap.res.in/sharma/PlantAFP/.
