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MLAFP-XN: Leveraging neural network model for development of antifungal peptide identification tool
Md Fahim Sultan1, Md Shazzad Hossain Shaon1, Tasmin Karim1
1Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh.
Abstract:
Infectious fungi have been an increasing global concern in the present era. A promising approach to tackle this pressing concern involves utilizing Antifungal peptides (AFP) to develop an antifungal drug that can selectively eliminate fungal pathogens from a host with minimal toxicity to the host. Accordingly, identifying precise therapeutic antifungal peptides is crucial for developing effective drugs and treatments. This study proposed MLAFP-XN, a neural network-based strategy for accurately detecting active AFP in sequencing data to achieve this objective. In this work, eight feature extraction techniques and the XGB feature selection strategy are utilized together to present an enhanced methodology. A total of 24 classification models were evaluated, and the most effective four have been selected. Each of these models demonstrated superior accuracy on independent test sets, with respective scores of 97.93 %, 99.47 %, and 99.48 %. Our model outperforms current state of the art methods. In addition, we created a companion website to demonstrate our AFP recognition process and use SHAP to identify the most influential properties.
Insights
This study introduces MLAFP-XN, a neural network tool for identifying active antifungal peptides (AFP). This method accurately detects antifungal peptides, aiding in the development of new antifungal drugs with minimal host toxicity.
Area of Science:
- Biotechnology
- Computational Biology
- Drug Discovery
Background:
- Infectious fungi pose a growing global health threat.
- Antifungal peptides (AFP) offer a promising strategy for targeted fungal pathogen elimination with low host toxicity.
- Accurate identification of therapeutic AFP is critical for effective antifungal drug development.
Purpose of the Study:
- To propose MLAFP-XN, a novel neural network-based strategy for the accurate detection of active antifungal peptides (AFP) in sequencing data.
- To enhance AFP identification methodology by integrating multiple feature extraction and selection techniques.
- To develop a computational tool for accelerating the discovery of novel antifungal agents.
Main Methods:
- Utilized eight feature extraction techniques combined with the XGB feature selection strategy.
- Evaluated a total of 24 classification models to identify the most effective ones for AFP detection.
- Developed a neural network-based strategy (MLAFP-XN) for analyzing sequencing data.
Main Results:
- The MLAFP-XN strategy demonstrated superior accuracy on independent test sets, achieving scores of 97.93%, 99.47%, and 99.48%.
- The developed models significantly outperform existing state-of-the-art methods in antifungal peptide recognition.
- SHAP analysis was employed to identify key features influencing AFP recognition.
Conclusions:
- MLAFP-XN provides a highly accurate and efficient method for identifying active antifungal peptides.
- This approach facilitates the development of novel, targeted antifungal therapies.
- A companion website was developed to showcase the AFP recognition process and its influential properties.
