De Novo Design of Large Polypeptides Using a Lightweight Diffusion Model Integrating LSTM and Attention Mechanism Under Per-Residue Secondary Structure Constraints

This study presents PolypeptideDesigner (PPD), a novel conditional diffusion-based model for de novo polypeptide sequence design and generation based on per-residue secondary structure conditions. By integrating a lightweight LSTM-attention neural network as the denoiser within a diffusion framework, PPD offers an innovative and efficient approach to polypeptide generation. Evaluations demonstrate that the PPD model can generate diverse and novel polypeptide sequences across various testing conditions, achieving high pLDDT scores when folded by ESMFold. In comparison to the ProteinDiffusionGenerator B (PDG-B) model, a relevant benchmark in the field, PPD exhibits the ability to produce longer and more diverse polypeptide sequences. This improvement is attributed to PPD's optimized architecture and expanded training dataset, which enhance its understanding of protein structural pattern. The PPD model shows significant potential for optimizing functional polypeptides with known structures, paving the way for advancements in biomaterial design. Future work will focus on further refining the model and exploring its broader applications in polypeptide engineering.