DaDL-SChlo: protein subchloroplast localization prediction based on generative adversarial networks and pre-trained protein language model

Abstract

Chloroplast is a crucial site for photosynthesis in plants. Determining the location and distribution of proteins in subchloroplasts is significant for studying the energy conversion of chloroplasts and regulating the utilization of light energy in crop production. However, the prediction accuracy of the currently developed protein subcellular site predictors is still limited due to the complex protein sequence features and the scarcity of labeled samples. We propose DaDL-SChlo, a multi-location protein subchloroplast localization predictor, which addresses the above problems by fusing pre-trained protein language model deep learning features with traditional handcrafted features and using generative adversarial networks for data augmentation. The experimental results of cross-validation and independent testing show that DaDL-SChlo has greatly improved the prediction performance of protein subchloroplast compared with the state-of-the-art predictors. Specifically, the overall actual accuracy outperforms the state-of-the-art predictors by 10.7% on 10-fold cross-validation and 12.6% on independent testing. DaDL-SChlo is a promising and efficient predictor for protein subchloroplast localization. The datasets and codes of DaDL-SChlo are available at https://github.com/xwanggroup/DaDL-SChlo.

TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides

“…One possible extension is to collect additional TIPs to develop a more comprehensive prediction model. Another extension could be the employment of well-known feature extractors, such as a bidirectional recurrent neural network (RNN) [ 55 ] and ProtBERT [ 56 ], to effectively capture the key information of TIPs. For the last extension, we can try to incorporate TIPred with recent innovative computational frameworks, such as an iterative feature representation algorithm [ 57 ] and deep learning (DL)-based framework [ 39 , 58 ].…”

A Review for Artificial Intelligence Based Protein Subcellular Localization

“…In addition, a long short-term memory network (LSTM) which combines the previous states and current inputs is also commonly used [56,57], with Generative Adversarial Network (GAN) [58] and Synthetic Minority Over-sampling Technique (SMOTE) [59] used for synthesizing minority samples to deal with data imbalance. Developing data augmentation methods by deep learning algorithms has also made protein language model construction possible [60,61]. Through transfer learning [62], pretrained models can be fine-tuned on different downstream tasks, reducing the need for large amounts of labeled data for training.…”