Protein inverse folding-that is, predicting an amino acid sequence that will fold into the desired 3D structure-is an important problem for structure-based protein design. Machine learning based methods for inverse folding typically use recovery of the original sequence as the optimization objective. However, inverse folding is a one-to-many problem where several sequences can fold to the same structure. Moreover, for many practical applications, it is often desirable to have multiple, diverse sequences that fold into the target structure since it allows for more candidate sequences for downstream optimizations. Here, we demonstrate that although recent inverse folding methods show increased sequence recovery, their "foldable diversity"-i.e. their ability to generate multiple non-similar sequences that fold into the structures consistent with the target-does not increase. To address this, we present RL-DIF, a categorical diffusion model for inverse folding that is pre-trained on sequence recovery and tuned via reinforcement learning on structural consistency. We find that RL-DIF achieves comparable sequence recovery and structural consistency to benchmark models but shows greater foldable diversity: experiments show RL-DIF can achieve an foldable diversity of 29% on CATH 4.2, compared to 23% from models trained on the same dataset. The PyTorch model weights and sampling code are available on GitHub.

翻译：蛋白质逆折叠——即预测能够折叠成所需三维结构的氨基酸序列——是基于结构的蛋白质设计中的一个重要问题。基于机器学习的逆折叠方法通常以恢复原始序列作为优化目标。然而，逆折叠是一个一对多问题，多个序列可能折叠为同一结构。此外，在许多实际应用中，通常期望获得多个能够折叠成目标结构且具有多样性的序列，因为这能为下游优化提供更多候选序列。本文研究表明，尽管近期的逆折叠方法显示出更高的序列恢复率，但其“可折叠多样性”——即生成多个非相似序列且这些序列能折叠成与目标一致结构的能力——并未提升。为解决此问题，我们提出了RL-DIF，这是一种用于逆折叠的分类扩散模型，该模型在序列恢复任务上进行预训练，并通过强化学习在结构一致性目标上进行微调。我们发现RL-DIF在序列恢复率和结构一致性方面与基准模型相当，但展现出更高的可折叠多样性：实验表明RL-DIF在CATH 4.2数据集上可实现29%的可折叠多样性，而相同数据集上训练的其他模型仅为23%。PyTorch模型权重与采样代码已在GitHub开源。