Welcome to Antifungipept
Antifungipept is a comprehensive tool designed for the prediction and design of antifungal peptides. It integrates advanced quantitative structure-activity relationship (QSAR) models for accurate antifungal activity prediction and provides a robust platform for rational peptide design.
Key Features:
- Identification model for antifungal peptides.
- Activity prediction models against multiple Candida species and Cryptococcus neoformans.
- Antifungal index (AFI) for quantitatively assessing peptide effectiveness.
- AFP20200317 database with over 400,000 putative antifungal peptide sequences.
- Tools for sequence segmentation, mutation, and optimization guided by AFI.
Model Performance Metrics
Table 1. Results of the Antifungal Peptide Classification Model
Sample size | Calibration | Validation | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Acc. | Sen. | Spec. | F1 | MCC | Acc. | Sen. | Spec. | F1 | MCC | |
n=9240, 2310 a | 0.95 | 0.95 | 0.95 | 0.95 | 0.90 | 0.89 | 0.90 | 0.89 | 0.89 | 0.79 |
a Sample size of calibration and validation set, respectively.
Table 2. Results of Antifungal Activity Prediction ModelsTargets | Calibration | Validation | ||
---|---|---|---|---|
RMSE | R2 | RMSE | R2 | |
C. albicans (n=1266, 317) | 0.69 | 0.90 | 1.23 | 0.66 |
C. krusei (n=76, 19) | 0.48 | 0.94 | 1.10 | 0.69 |
C. neoformans (n=220, 55) | 0.82 | 0.90 | 0.89 | 0.89 |
C. parapsilosis (n=118, 30) | 0.73 | 0.90 | 1.17 | 0.69 |
Dataset Availability
Access our comprehensive datasets for training and validation:
- Classification training data: Download
- pMIC regression data for:
Backend and Source Code
Antifungipept is powered by the antifungal backend. Explore and contribute to our open-source project on GitHub.
References and Citation
For detailed methodologies used in the development of Antifungipept, including the calculation of AFI and antifungal activity, please refer to the following publication:
- Zhang, J. et al. (2022) "Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure-Activity Relationship", ACS Med. Chem. Lett., 13(1), pp. 99-104. Available at: https://pubs.acs.org/doi/10.1021/acsmedchemlett.1c00556.
Please cite this publication when using the data and methods from this database for academic or commercial purposes.