Deep learning has revolutionized protein structure prediction and design, enabling accurate prediction of protein structures from sequence information. AlphaFold2, a deep learning model, has achieved high accuracy in predicting protein structures, surpassing traditional methods. This review discusses recent advancements in deep learning-based methods for protein structure prediction and design, including the prediction of protein complexes, different conformations, and protein evolution. These methods have expanded the scope of research in structural biology, offering new opportunities for biomedical research. AlphaFold2 uses multiple sequence alignments (MSAs) and distance measurements to predict protein structures. It has been adapted to predict protein complexes and different conformations, with models like RoseTTAFold and OpenFold improving upon AlphaFold2's performance. Protein language models, such as ESMfold, have also been developed to predict structures without relying on MSAs, offering faster and more scalable solutions. The integration of co-evolutionary information and structural data has enabled the prediction of protein structures with high accuracy. These models have been applied to study protein interactions, structural diversity, and evolutionary relationships. The availability of large predicted structures has facilitated the analysis of protein families and the identification of novel structures. Protein structure prediction has also been extended to protein complexes and integrative structural modeling, where multiple data sources are combined to improve accuracy. The prediction of different conformations has been successfully applied to various proteins, including transporters and GPCRs, demonstrating the potential of deep learning in capturing dynamic protein behavior. In protein design, deep learning models have been used to generate proteins with specific structures or functions, showing promising results in enzyme design and drug development. These models have improved the controllability of protein design, allowing for the creation of proteins with desired properties. Despite these advancements, challenges remain in predicting complex protein structures and dynamics. Future research aims to enhance deep learning models for predicting alternative conformations and improve the accuracy of protein-small molecule interactions. The combination of experimental and computational approaches is expected to further advance structural biology, enabling a comprehensive understanding of protein structures and functions.Deep learning has revolutionized protein structure prediction and design, enabling accurate prediction of protein structures from sequence information. AlphaFold2, a deep learning model, has achieved high accuracy in predicting protein structures, surpassing traditional methods. This review discusses recent advancements in deep learning-based methods for protein structure prediction and design, including the prediction of protein complexes, different conformations, and protein evolution. These methods have expanded the scope of research in structural biology, offering new opportunities for biomedical research. AlphaFold2 uses multiple sequence alignments (MSAs) and distance measurements to predict protein structures. It has been adapted to predict protein complexes and different conformations, with models like RoseTTAFold and OpenFold improving upon AlphaFold2's performance. Protein language models, such as ESMfold, have also been developed to predict structures without relying on MSAs, offering faster and more scalable solutions. The integration of co-evolutionary information and structural data has enabled the prediction of protein structures with high accuracy. These models have been applied to study protein interactions, structural diversity, and evolutionary relationships. The availability of large predicted structures has facilitated the analysis of protein families and the identification of novel structures. Protein structure prediction has also been extended to protein complexes and integrative structural modeling, where multiple data sources are combined to improve accuracy. The prediction of different conformations has been successfully applied to various proteins, including transporters and GPCRs, demonstrating the potential of deep learning in capturing dynamic protein behavior. In protein design, deep learning models have been used to generate proteins with specific structures or functions, showing promising results in enzyme design and drug development. These models have improved the controllability of protein design, allowing for the creation of proteins with desired properties. Despite these advancements, challenges remain in predicting complex protein structures and dynamics. Future research aims to enhance deep learning models for predicting alternative conformations and improve the accuracy of protein-small molecule interactions. The combination of experimental and computational approaches is expected to further advance structural biology, enabling a comprehensive understanding of protein structures and functions.