Proteins are fundamental biological macromolecules essential for cellular structure, enzymatic catalysis, and immune defense, making the generation of novel proteins crucial for advancements in medicine, biotechnology, and material sciences. This study explores protein design using deep generative models, specifically Denoising Diffusion Probabilistic Models (DDPMs). While traditional methods often focus on either protein structure or sequence design independently, recent trends emphasize a co-design approach addressing both aspects simultaneously. We propose a novel methodology utilizing Equivariant Graph Neural Networks (EGNNs) within the diffusion framework to co-design protein structures and sequences. We modify the EGNN architecture to improve its effectiveness in learning intricate data patterns. Experimental results show that our approach effectively generates high-quality protein sequences, although challenges remain in producing plausible protein backbones and ensuring strong sequence-structure correlation.