  Protein representation learning plays a crucial role in understanding the
structure and function of proteins, which are essential biomolecules involved
in various biological processes. In recent years, deep learning has emerged as
a powerful tool for protein modeling due to its ability to learn complex
patterns and representations from large-scale protein data. This comprehensive
survey aims to provide an overview of the recent advances in deep learning
techniques applied to protein science. The survey begins by introducing the
developments of deep learning based protein models and emphasizes the
importance of protein representation learning in drug discovery, protein
engineering, and function annotation. It then delves into the fundamentals of
deep learning, including convolutional neural networks, recurrent neural
networks, attention models, and graph neural networks in modeling protein
sequences, structures, and functions, and explores how these techniques can be
used to extract meaningful features and capture intricate relationships within
protein data. Next, the survey presents various applications of deep learning
in the field of proteins, including protein structure prediction,
protein-protein interaction prediction, protein function prediction, etc.
Furthermore, it highlights the challenges and limitations of these deep
learning techniques and also discusses potential solutions and future
directions for overcoming these challenges. This comprehensive survey provides
a valuable resource for researchers and practitioners in the field of proteins
who are interested in harnessing the power of deep learning techniques. By
consolidating the latest advancements and discussing potential avenues for
improvement, this review contributes to the ongoing progress in protein
research and paves the way for future breakthroughs in the field.
