Reinforcement learning on structure-conditioned categorical diffusion for protein inverse folding

• URL: http://arxiv.org/abs/2410.17173v1
• Date: Tue, 22 Oct 2024 16:50:34 GMT
• Title: Reinforcement learning on structure-conditioned categorical diffusion for protein inverse folding
• Authors: Yasha Ektefaie, Olivia Viessmann, Siddharth Narayanan, Drew Dresser, J. Mark Kim, Armen Mkrtchyan,
• Abstract summary: inverse folding is a one-to-many problem where several sequences can fold to the same structure. We present RL-DIF, a categorical diffusion model for inverse folding that is pre-trained on sequence recovery and tuned via reinforcement learning. 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.
• Score: 0.0
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• Abstract: 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.

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