Coarse-grained (CG) modeling has emerged as an essential tool in computational biology, offering a simplified yet effective representation of proteins and their interactions. By reducing atomic detail, CG models enhance computational efficiency while preserving critical physical and chemical properties, enabling the study of large-scale biological phenomena. These models facilitate the exploration of complex processes—such as protein folding, conformational dynamics, molecular interactions, and supramolecular assemblies—that are often inaccessible to all-atom simulations. This chapter provides an overview of CG protein modeling, tracing its historical development from early simplified representations to advanced coarse-graining techniques. We discuss fundamental principles, including bottom-up and top-down parameterization strategies, statistical potentials, and structure-based approaches like elastic network models and Gō-like models. Key applications are explored, including insights into protein folding mechanisms, protein-protein interactions, phase separation, and protein-lipid interactions in complex cellular environments. Recent advances in CG-based drug discovery are also highlighted. The chapter concludes with a discussion on future directions for CG modeling, emphasizing hybrid approaches, artificial intelligence-driven parameterization, and enhanced force fields to improve accuracy and broaden applicability in computational biology.