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Recent studies have shown competitive performance in protein design that aimsto find the amino acid sequence folding into the desired structure. However,most of them disregard the importance of predictive confidence, fail to coverthe vast protein space, and do not incorporate common protein knowledge. Afterwitnessing the great success of pretrained models on diverse protein-relatedtasks and the fact that recovery is highly correlated with confidence, wewonder whether this knowledge can push the limits of protein design further. Asa solution, we propose a knowledge-aware module that refines low-qualityresidues. We also introduce a memory-retrieval mechanism to save more than 50\%of the training time. We extensively evaluate our proposed method on the CATH,TS50, and TS500 datasets and our results show that our Knowledge-Design methodoutperforms the previous PiFold method by approximately 9\% on the CATHdataset. Specifically, Knowledge-Design is the first method that achieves 60+\%recovery on CATH, TS50 and TS500 benchmarks. We also provide additionalanalysis to demonstrate the effectiveness of our proposed method. The code willbe publicly available.

Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement