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.

Advancing protein design is crucial for breakthroughs in medicine and biotechnology, yet traditional approaches often fall short by focusing solely on representing protein sequences using the 20 canonical amino acids. This thesis explores discrete diffusion models for generating novel protein sequences with an all-atom representation, specifically SELFIES a widely used molecular string representation. This all-atom approach considers the atomic composition of each amino acid in the protein. Enabling the inclusion of non-canonical amino acids and post-translational modifications. Using a modified ByteNet architecture and the D3PM framework, we compare the effects of this all-atom representation to the standard amino acid representation on the generated proteins' quality, diversity and novelty. Additionally, we see how a uniform or absorbing noise process affects the results. While models trained on the all-atom representation struggle to generate fully valid proteins consistently, those successfully designed showed improved novelty and diversity. Moreover, the all-atom representation can achieve comparable structural reliability results from OmegaFold to the amino acid models. Lastly, our results show that the use of an absorbing noise schedule is the most effective for both the all-atom and amino acid representation.