CORAL：通过协同结合奖励学习淀粉样纤维配体对接
CORAL: Learning Amyloid Fibril Ligand Docking with Cooperative Binding Rewards
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中文总结 AI 辅助
研究针对淀粉样纤维配体对接面临的挑战，提出CORAL强化学习框架，通过纳入协同配体 - 配体堆叠能量及蛋白质 - 配体对接亲和力训练模型，引入评估集，实验证明其在姿态质量和结合亲和力相关性上优于现有基线。
中文摘要 AI 辅助
神经退行性疾病（如阿尔茨海默病和帕金森病）的一个标志是蛋白质异常聚集成淀粉样纤维，能选择性结合这些纤维的小分子有望用作诊断、成像探针和治疗剂。预测此类配体与纤维靶点的结合存在两个基本挑战：一是淀粉样配体复合物的共晶体结构极少，难以进行对接模型的监督训练；二是淀粉样纤维的结合模式与球状蛋白根本不同，现有对接模型无法捕捉。为应对这些挑战，我们提出CORAL，这是一个强化学习框架，训练生成对接模型以生成适合交叉β凹槽几何形状的配体姿态分布。我们的奖励明确纳入了配体 - 配体堆叠能量以及蛋白质 - 配体对接亲和力，直接捕捉淀粉样纤维独特的结合几何形状。我们还引入了由领域专家验证的模型生成姿态构建的淀粉样配体复合物精选评估集。实验表明，与现有对接基线相比，姿态质量和结合亲和力相关性得到了改善。
英文摘要
A hallmark of neurodegenerative diseases such as Alzheimer's and Parkinson's is the aberrant aggregation of proteins into amyloid fibrils, and small molecules that selectively bind to these fibrils hold promise as diagnostics, imaging probes, and therapeutics. Predicting how such ligands bind to fibril targets, however, presents two fundamental challenges. First, resolved co-crystal structures of amyloid-ligand complexes are exceptionally scarce; even with recent advances in cryo-EM only a handful have been structurally characterized, making supervised training of docking models impractical for this target class. Second, amyloid fibrils present a binding mode fundamentally different from globular proteins: ligands intercalate into longitudinal cross-$β$ grooves and stack cooperatively along the fibril axis, a geometry that existing docking models are not designed to capture. To address these challenges, we present CORAL (COopeRative Amyloid Ligand docking), a reinforcement learning framework that trains a generative docking model to produce ligand pose distributions tailored to the cross-$β$ groove geometry. Our reward explicitly incorporates cooperative ligand-ligand stacking energy alongside protein-ligand docking affinity, directly capturing the distinctive binding geometry of amyloid fibrils. We further introduce a curated evaluation set of amyloid-ligand complexes constructed from model-generated poses validated by domain experts. Experiments on both experimentally resolved structures and this evaluation set demonstrate improved pose quality and binding affinity correlation over existing docking baselines.
发表机构
- Center of Excellence for Generative AI, KAUST（沙特阿卜杜拉国王科技大学生成式人工智能卓越中心）
- MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能教育部重点实验室）
- Stern Laboratory, Brigham and Women’s Hospital, Harvard University（哈佛大学布莱根妇女医院斯特恩实验室）
机构由 AI 辅助整理，请以论文原文为准。
