用小分子信息指导蛋白序列结构联合设计，生成更稳定且结合精准的蛋白。
Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design
- 在离散令牌空间中联合生成序列与结构，通过几何注意力融合小分子信息。
- 全蛋白设计下TM-score提升至0.802，活性位点残基均方偏差降至1.97Å。
- 适合需要精准配体结合的药物设计场景，尤其关注功能蛋白重构。
蛋白质的功能依赖于其三维结构，而配体结合蛋白的协同设计需在明确配体约束下生成序列-结构兼容的蛋白。尽管连续扩散和基于流的模型可在坐标或隐空间中实现配体感知设计，现有离散扩散蛋白语言模型主要在序列或结构令牌上操作，缺乏对小分子的直接条件控制。本文提出ProtLiD²，一种蛋白质-配体条件离散扩散模型，联合生成氨基酸序列与离散结构令牌，并通过几何感知交叉注意力引入配体化学与几何信息。模型在超过一百万个配体-蛋白复合物上训练，将掩码离散扩散扩展至配体感知的功能蛋白设计。我们进一步提出最大置信度边际引导的ReMask解码策略，在推理时自校正，保留高置信预测并重掩不确定令牌。在全蛋白设计中，相比Complexa，ProtLiD²将整体折叠置信度提升：TM-score从0.672增至0.802，pLDDT从64.55升至73.00。在活性位点协同设计中，残基均方偏差从FAIR/PocketGen的3.46/3.40Å降至1.97Å；在更严格的对接阈值下，配体感知通过率分别从14.86%→59.73%、6.08%→23.49%显著提升。结果表明，配体条件离散扩散是功能性蛋白协同设计的有效令牌空间框架。代码将在https://github.com/auroua/ProtLiD发布。
原文摘要 · Abstract (English)
Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design requires models that generate sequence-structure compatible proteins under explicit ligand constraints. Although continuous diffusion and flow-based models support ligand-aware design in coordinate or latent spaces, existing discrete diffusion protein language models mainly operate over sequence or structure tokens without direct small-molecule conditioning. We introduce \textbf{ProtLiD$^2$}, a \textbf{Prot}ein \textbf{L}igand-conditioned \textbf{D}iscrete \textbf{D}iffusion model for protein sequence-structure co-design. ProtLiD$^2$ jointly generates amino-acid sequence and discrete structure tokens while incorporating ligand chemical and geometric information through geometry-aware cross-attention. Trained on over one million ligand-protein complexes, ProtLiD$^2$ extends masked discrete diffusion to ligand-aware functional protein design. We further propose maximum confidence-margin guided ReMask decoding, an inference-time self-correction strategy that retains confident predictions and remasks uncertain tokens. ProtLiD$^2$ improves global fold confidence over Complexa in whole-protein design, increasing TM-score from 0.672 to 0.802 and pLDDT from 64.55 to 73.00. In pocket co-design, ProtLiD$^2$ reduces active-site BB-RMSD from 3.46/3.40Å for FAIR/PocketGen to 1.97Å, and improves ligand-aware pass rates over PocketGen from 14.86% to 59.73% and from 6.08% to 23.49% under stricter docking thresholds. These results support ligand-conditioned discrete diffusion as an effective token-space framework for functional protein co-design. Code will be available at https://github.com/auroua/ProtLiD.
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