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Proteins are fundamental biological entities that play a key role in lifeactivities. The amino acid sequences of proteins can be folded into stable 3Dstructures in the real physicochemical world, forming a special kind ofsequence-structure data. With the development of Artificial Intelligence (AI)techniques, Protein Representation Learning (PRL) has recently emerged as apromising research topic for extracting informative knowledge from massiveprotein sequences or structures. To pave the way for AI researchers with littlebioinformatics background, we present a timely and comprehensive review of PRLformulations and existing PRL methods from the perspective of modelarchitectures, pretext tasks, and downstream applications. We first brieflyintroduce the motivations for protein representation learning and formulate itin a general and unified framework. Next, we divide existing PRL methods intothree main categories: sequence-based, structure-based, and sequence-structureco-modeling. Finally, we discuss some technical challenges and potentialdirections for improving protein representation learning. The latest advancesin PRL methods are summarized in a GitHub repositoryhttps://github.com/LirongWu/awesome-protein-representation-learning.

A Survey on Protein Representation Learning: Retrospect and Prospect