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

J 2021

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

TYAGI, Atul, Sudeep ROY, Sanjay SINGH, Manoj SEMWAL, Ajit K. SHASANY et. al.

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

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30104 Pharmacology and pharmacy

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

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

Abstract

V originále

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.

Citovat

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.

@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.

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