A Transformer based fusion model for accurately predicting Gene Ontology (GO) terms from full-scale Protein Sequences
Recent developments in next-generation sequencing technology have led to the creation of extensive, open-source protein databases consisting of hundreds of millions of sequences. To render these sequences applicable in biomedical applications, they must be meticulously annotated by wet lab testing or extracting them from existing literature. Over the last few years, researchers have developed numerous automatic annotation systems, particularly deep learning models based on machine learning and artificial intelligence, to address this issue. In this work, we propose a transformer-based fusion model capable of predicting Gene Ontology (GO) terms from full-scale protein sequences, achieving state-of-the-art accuracy compared to other contemporary machine learning annotation systems. The approach performs particularly well on clustered split datasets, which comprise training and testing samples originating from distinct distributions that are structurally diverse. This demonstrates that the model is able to understand both short and long-term dependencies within the enzyme’s structure and can precisely identify the motifs associated with the various GO terms. Furthermore, the technique is lightweight and less computationally expensive compared to the benchmark methods, while at the same time not unaffected by sequence length, rendering it appropriate for diverse applications with varying sequence lengths.
Dataset link: https://drive.google.com/file/d/19SXO7Asy2vsAab6cl36-IkUqq9DYMgDb/view?usp=sharing
step1: Clone the repository on the local machine
step2: Download the models from the following directory and paste them into the project root: https://drive.google.com/file/d/19SXO7Asy2vsAab6cl36-IkUqq9DYMgDb/view?usp=sharing step3: Install pytorch by running the following in the terminal:
pip install torch==1.10.2+cpu torchvision==0.11.3+cpu --extra-index-url https://download.pytorch.org/whl/cpu
step4: Install other necessary libraries by running the following in the terminal:
pip install -r requirements.txt
step5: Run the training scripts to train the model and run evaluations.