Antifungipept
Antifungipept is a powerful online platform for predicting antifungal activity of short peptides with less than 150 amino acid residues. Leveraging the state-of-the-art antifungal index (AFI) as the metric, this tool offers antifungal peptide identification, antifungal activity prediction against four key fungal pathogens, as well as rational peptide designs.
Features
- Quantitative Antifungal Activity Metric: AFI deliver a comprehensive numerical evaluation of peptide's antifungal activity by integrating the predictions of identification and activity regression models. The metric has been proved efficient in assessing both antifungal and antimicrobial activity of peptides.
- Antifungal Peitide Identification: Identify antifungal peptide using a classifiction model, resulting to outcomes of true or false along with the prediction likelihood.
- Antifungal Activity Prediction: Predict the minimum inhibitory concentration (MIC) values in μM unit against four representative fungal pathogens, including Candida albicans, Candida krusei, Candida parapsilosis, and Cryptococcus neoformans.
- Rational Design of Antifungal Peptides: Facilitate the rational design of antifungal peptides through truncation, single-point mutation, and global optimization, all guided by the AFI.
- Antifungal Peptide Dataset: Provide the datasets used for training the predictive models and for further development. ``` Refs:
- Zhang J., et. al. ACS Med. Chem. Lett., 2022, 13(1): 99-104. DOI: 10.1021/acsmedchemlett.1c00556.
- Zhang, J., et. al. J. Chem. Inf. Model. 2024, 64 (10), 4277–4285. DOI: 10.1021/acs.jcim.4c00142. ```
NIR online
NIR Online is an advanced platform for Near-Infrared (NIR) spectroscopy calibration. It is designed to aid researchers and professionals in developing and validating calibration models for various applications such as quality control in food and pharmaceuticals. The platform offers a suite of tools for precise data analysis and model optimization, ensuring reliable and accurate results.
Features
- Calibration Model Development: Tools for creating regression and classification NIR models.
- Model optimization: Advanced algorithms for model optimization, including spectral preprocessing, feature selection, outlier detection, and calibration transfer.
- User-Friendly Interface: Simplifies complex calibration and evaluation processes.