SLP-T5: A New Method for Predicting Protein Sub-chloroplast Localization Based on Text-to-Text Transfer Transformer

Chloroplasts convert light energy into chemical energy and are essential for photosynthesis. Proteins in different chloroplast positions have different functions. Thus, precise prediction of sub-chloroplast localization of an unknown protein is crucial for understanding related biochemical processes. The state-of-the-art methods for this task are mainly based on machine learning and deep learning, but they continue to struggle with data imbalance and the lack of training data, both of which result in low accuracy. Here, we proposed a new method for predicting Sub-chloroplast Localization Proteins based on the Text-to-Text Transfer Transformer (T5) model, called SLP-T5, which included a feature extraction module with ProtT5, utilized the Multi-Labeled Data Augmentation module to generate additional training data and address the lack of training data, and employed a multi-label predictor with a Threshold Selection Module to combat data imbalance and improve prediction accuracy. Experimental results showed that SLP-T5 outperformed current methods, increasing the overall actual accuracy by 9% to 92.5%. The SLP-T5 provided further evidence for the localization of predicted chloroplast proteins in the UniProtKB/Swiss-Prot database, with 95% accuracy, and formed a comprehensive documentation. SLP-T5 significantly improved the performance of protein sub-chloroplast localization, providing a robust, accurate, and efficient predictor, showcasing a successful application of large language models in protein function prediction. The codes for SLP-T5 and datasets are available at https://anonymous.4open.science/r/SLP-T5-0809.