Deep learning methods for protein representation and function prediction: A comprehensive overview

Deep learning has revolutionized protein function prediction by capturing intricate protein relationships, yet a comprehensive survey of its methodologies remains elusive. In this review, we systematically dissect recent advances by addressing three pivotal questions: (a) which modalities are most critical for various function prediction tasks, (b) which deep learning strategies optimally model these modalities, (c) what common and task-specific challenges persist. We categorize protein data into eight distinct types – from fundamental representations to specialized expert knowledge – and provide an exhaustive analysis of state-of-the-art deep learning models alongside emerging self-supervised learning strategies. Moreover, we compare the evolution of architectures across different modeling paradigms, highlighting their respective strengths and limitations. Our investigation spans over fifteen downstream tasks across five key research areas, including protein function annotation, protein–protein interactions, protein–ligand interactions, mutation effect prediction, and remote homology detection. Finally, we discuss current challenges and propose potential solutions, offering strategic guidance for data selection, methodological innovation, and future research directions in the application of deep learning to protein function prediction.