ProtGO: A Transformer based Fusion Model for accurately predicting Gene Ontology (GO) Terms from full scale Protein Sequences

TL;DR

ProtGO is a transformer-based model that accurately predicts Gene Ontology terms from full protein sequences, outperforming existing methods in accuracy, efficiency, and handling diverse sequence lengths.

Contribution

This work introduces a novel lightweight transformer fusion model that achieves state-of-the-art GO term prediction accuracy from full protein sequences, with improved efficiency and robustness.

Findings

Achieves state-of-the-art accuracy in GO term prediction.

Performs well on structurally diverse datasets.

Is computationally less expensive than benchmark methods.

Abstract

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.