论文标题
蛋白质设计的α蒸馏
AlphaFold Distillation for Protein Design
论文作者
论文摘要
逆蛋白折叠，设计折叠成特定3D结构的序列的过程对于生物工程和药物发现至关重要。传统方法依赖于实验解析的结构，但仅涵盖了一小部分蛋白质序列。诸如AlphaFold之类的正向折叠模型通过准确预测序列的结构来提供潜在的解决方案。但是，这些模型太慢，无法集成到训练过程中反向折叠模型的优化循环中。为了解决这个问题，我们建议在折叠模型置信度指标（例如PTM或PLDDT分数）上使用知识蒸馏，以创建更快，端到端的可区分蒸馏模型。然后，该模型可以用作训练反折叠模型的结构一致性正常化程序。我们的技术具有通用性，可以应用于其他设计任务，例如基于序列的蛋白质填充。实验结果表明，我们的方法的表现优于非规范基准，序列恢复的提高3％，蛋白质多样性提高了45％，同时保持了生成序列的结构一致性。代码可在https://github.com/ibm/afdistill上找到
Inverse protein folding, the process of designing sequences that fold into a specific 3D structure, is crucial in bio-engineering and drug discovery. Traditional methods rely on experimentally resolved structures, but these cover only a small fraction of protein sequences. Forward folding models like AlphaFold offer a potential solution by accurately predicting structures from sequences. However, these models are too slow for integration into the optimization loop of inverse folding models during training. To address this, we propose using knowledge distillation on folding model confidence metrics, such as pTM or pLDDT scores, to create a faster and end-to-end differentiable distilled model. This model can then be used as a structure consistency regularizer in training the inverse folding model. Our technique is versatile and can be applied to other design tasks, such as sequence-based protein infilling. Experimental results show that our method outperforms non-regularized baselines, yielding up to 3% improvement in sequence recovery and up to 45% improvement in protein diversity while maintaining structural consistency in generated sequences. Code is available at https://github.com/IBM/AFDistill
