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

TYAGI, Atul, Sudeep ROY, Sanjay SINGH, Manoj SEMWAL, Ajit K. SHASANY, Ashok SHARMA a Ivo PROVAZNÍK. PhytoAFP: In Silico Approaches for Designing Plant-Derived Antifungal Peptides. Antibiotics-Basel. BASEL: MDPI, 2021, roč. 10, č. 7, s. 1-12. ISSN 2079-6382. Dostupné z: https://dx.doi.org/10.3390/antibiotics10070815.

Základní údaje

• Originální název: PhytoAFP: In Silico Approaches for Designing Plant-Derived Antifungal Peptides
• Autoři: TYAGI, Atul, Sudeep ROY, Sanjay SINGH, Manoj SEMWAL, Ajit K. SHASANY, Ashok SHARMA a Ivo PROVAZNÍK (203 Česká republika, garant, domácí).
• Vydání: Antibiotics-Basel, BASEL, MDPI, 2021, 2079-6382.

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Článek v odborném periodiku
• Obor: 30104 Pharmacology and pharmacy
• Stát vydavatele: Švýcarsko
• Utajení: není předmětem státního či obchodního tajemství
• WWW: URL
• Impakt faktor: Impact factor: 5.222
• Kód RIV: RIV/00216224:14110/21:00124178
• Organizační jednotka: Lékařská fakulta
• Doi: http://dx.doi.org/10.3390/antibiotics10070815
• UT WoS: 000686144900001
• Klíčová slova anglicky: plant defensins; innate immunity; host defense peptides; antimicrobial peptides
• Štítky: 14110515, rivok
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.