ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search

• URL: http://arxiv.org/abs/2506.00925v1
• Date: Sun, 01 Jun 2025 09:34:20 GMT
• Title: ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search
• Authors: Mengdi Liu, Xiaoxue Cheng, Zhangyang Gao, Hong Chang, Cheng Tan, Shiguang Shan, Xilin Chen,
• Abstract summary: ProtInvTree is a reward-guided tree-search framework for protein inverse folding.<n>It reformulates sequence generation as a deliberate, step-wise decision-making process.<n>It supports flexible test-time scaling by expanding the search depth and breadth without retraining.
• Score: 77.55575655986252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing protein sequences that fold into a target 3D structure, known as protein inverse folding, is a fundamental challenge in protein engineering. While recent deep learning methods have achieved impressive performance by recovering native sequences, they often overlook the one-to-many nature of the problem: multiple diverse sequences can fold into the same structure. This motivates the need for a generative model capable of designing diverse sequences while preserving structural consistency. To address this trade-off, we introduce ProtInvTree, the first reward-guided tree-search framework for protein inverse folding. ProtInvTree reformulates sequence generation as a deliberate, step-wise decision-making process, enabling the exploration of multiple design paths and exploitation of promising candidates through self-evaluation, lookahead, and backtracking. We propose a two-stage focus-and-grounding action mechanism that decouples position selection and residue generation. To efficiently evaluate intermediate states, we introduce a jumpy denoising strategy that avoids full rollouts. Built upon pretrained protein language models, ProtInvTree supports flexible test-time scaling by expanding the search depth and breadth without retraining. Empirically, ProtInvTree outperforms state-of-the-art baselines across multiple benchmarks, generating structurally consistent yet diverse sequences, including those far from the native ground truth.

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