PhytoAFP: In Silico Approaches for Designing Plant-Derived Antifungal Peptides

TYAGI, Atul, Sudeep ROY, Sanjay SINGH, Manoj SEMWAL, Ajit K. SHASANY, Ashok SHARMA and Ivo PROVAZNÍK. PhytoAFP: In Silico Approaches for Designing Plant-Derived Antifungal Peptides. Antibiotics-Basel. BASEL: MDPI, 2021, vol. 10, No 7, p. 1-12. ISSN 2079-6382. Available from: https://dx.doi.org/10.3390/antibiotics10070815.

Basic information

• Original name: PhytoAFP: In Silico Approaches for Designing Plant-Derived Antifungal Peptides
• Authors: TYAGI, Atul, Sudeep ROY, Sanjay SINGH, Manoj SEMWAL, Ajit K. SHASANY, Ashok SHARMA and Ivo PROVAZNÍK (203 Czech Republic, guarantor, belonging to the institution).
• Edition: Antibiotics-Basel, BASEL, MDPI, 2021, 2079-6382.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 30104 Pharmacology and pharmacy
• Country of publisher: Switzerland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 5.222
• RIV identification code: RIV/00216224:14110/21:00124178
• Organization unit: Faculty of Medicine
• Doi: http://dx.doi.org/10.3390/antibiotics10070815
• UT WoS: 000686144900001
• Keywords in English: plant defensins; innate immunity; host defense peptides; antimicrobial peptides
• Tags: 14110515, rivok
• Tags: International impact, Reviewed

Abstract

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.