Other formats:
BibTeX
LaTeX
RIS
@article{1834642, author = {Tyagi, Atul and Roy, Sudeep and Singh, Sanjay and Semwal, Manoj and Shasany, Ajit K. and Sharma, Ashok and Provazník, Ivo}, article_location = {BASEL}, article_number = {7}, doi = {http://dx.doi.org/10.3390/antibiotics10070815}, keywords = {plant defensins; innate immunity; host defense peptides; antimicrobial peptides}, language = {eng}, issn = {2079-6382}, journal = {Antibiotics-Basel}, title = {PhytoAFP: In Silico Approaches for Designing Plant-Derived Antifungal Peptides}, url = {https://www.mdpi.com/2079-6382/10/7/815}, volume = {10}, year = {2021} }
TY - JOUR ID - 1834642 AU - Tyagi, Atul - Roy, Sudeep - Singh, Sanjay - Semwal, Manoj - Shasany, Ajit K. - Sharma, Ashok - Provazník, Ivo PY - 2021 TI - PhytoAFP: In Silico Approaches for Designing Plant-Derived Antifungal Peptides JF - Antibiotics-Basel VL - 10 IS - 7 SP - 1-12 EP - 1-12 PB - MDPI SN - 20796382 KW - plant defensins KW - innate immunity KW - host defense peptides KW - antimicrobial peptides UR - https://www.mdpi.com/2079-6382/10/7/815 N2 - 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. ER -
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. \textit{Antibiotics-Basel}. BASEL: MDPI, 2021, vol.~10, No~7, p.~1-12. ISSN~2079-6382. Available from: https://dx.doi.org/10.3390/antibiotics10070815.
|