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Effector-GAN: prediction of fungal effector proteins based on pretrained deep representation learning methods and
Yansu Wang1,2, Ximei Luo1,2, Quan Zou1
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
Motivation:
Phytopathogenic fungi secrete effector proteins to subvert host defenses and facilitate infection. Systematic analysis and prediction of candidate fungal effector proteins are crucial for experimental validation and biological control of plant disease. However, two problems are still considered intractable to be solved in fungal effector prediction: one is the high-level diversity in effector sequences that increases the difficulty of protein feature learning, and the other is the class imbalance between effector and non-effector samples in the training dataset.
Results:
In our study, pretrained deep representation learning methods are presented to represent multiple characteristics of sequences for predicting fungal effectors and generative adversarial networks are adapted to create synthetic feature samples to address the data imbalance problem. Compared with the state-of-the-art fungal effector prediction methods, Effector-GAN shows an overall improvement in accuracy in the independent test set.
Availability And Implementation:
Effector-GAN offers a user-friendly interface to inspect potential fungal effector proteins (http://lab.malab.cn/~wys/webserver/Effector-GAN). The Python script can be downloaded from http://lab.malab.cn/~wys/gitlab/effector-gan.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
Effector-GAN accurately predicts fungal effector proteins by using deep learning and generative adversarial networks to overcome sequence diversity and data imbalance challenges. This tool aids in plant disease control.
Area of Science:
- Plant Pathology
- Bioinformatics
- Computational Biology
Background:
- Phytopathogenic fungi secrete effector proteins to manipulate host defenses, complicating plant disease management.
- Accurate prediction of fungal effector proteins is essential for developing effective biological control strategies.
- Existing prediction methods struggle with effector sequence diversity and imbalanced datasets.
Purpose of the Study:
- To develop an advanced computational tool for predicting fungal effector proteins.
- To address the challenges of sequence diversity and class imbalance in effector prediction.
- To improve the accuracy and efficiency of identifying potential fungal effectors.
Main Methods:
- Utilized pretrained deep representation learning to capture diverse sequence characteristics.
- Adapted generative adversarial networks (GANs) to synthesize feature samples, mitigating data imbalance.
- Developed Effector-GAN, a novel prediction method integrating these approaches.
Main Results:
- Effector-GAN demonstrated superior accuracy compared to state-of-the-art methods on an independent test set.
- The model effectively handles high sequence diversity and class imbalance issues.
- Achieved significant improvements in predicting fungal effector proteins.
Conclusions:
- Effector-GAN provides a robust and accurate solution for fungal effector prediction.
- The developed tool facilitates experimental validation and aids in plant disease biological control.
- Offers a user-friendly web interface and downloadable script for broader accessibility.
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