UniSite：首个用于端到端配体结合位点检测的跨结构数据集与学习框架
UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection
AI总结：
针对配体结合位点检测存在的统计偏差、工作流不连续及评估指标不足等问题，提出首个跨结构数据集UniSite-DS和端到端框架UniSite，引入基于IoU的评估指标，实验证明其性能优于现有方法。
摘要：
蛋白质配体结合位点检测是基于结构的药物设计中的基础步骤。尽管近年来取得显著进展，现有方法、数据集和评估指标面临若干关键挑战：（1）当前数据集和方法以单个蛋白质-配体复合物为中心，忽略同一蛋白质的多个复合物中可能存在的多样结合位点，引入显著统计偏差；（2）配体结合位点检测通常被建模为不连续工作流，采用二值分割和后续聚类算法；（3）传统评估指标未能充分反映不同结合位点预测方法的实际性能。为解决这些问题，我们首先引入UniSite-DS——首个以UniProt（唯一蛋白质）为中心的配体结合位点数据集，其多站点数据比之前最广泛使用的数据集多4.81倍，整体数据多2.08倍。接着，我们提出UniSite——首个由带双射匹配的集合预测损失监督的端到端配体结合位点检测框架。此外，我们引入基于交并比（IoU）的平均精度作为更准确的配体结合位点预测评估指标。在UniSite-DS和多个代表性基准数据集上的大量实验表明，基于IoU的平均精度更准确反映预测质量，且UniSite在配体结合位点检测中优于当前最先进方法。数据集和代码将在https://github.com/quanlin-wu/unisite公开。
英文摘要：
The detection of ligand binding sites for proteins is a fundamental step in Structure-Based Drug Design. Despite notable advances in recent years, existing methods, datasets, and evaluation metrics are confronted with several key challenges: (1) current datasets and methods are centered on individual protein-ligand complexes and neglect that diverse binding sites may exist across multiple complexes of the same protein, introducing significant statistical bias; (2) ligand binding site detection is typically modeled as a discontinuous workflow, employing binary segmentation and subsequent clustering algorithms; (3) traditional evaluation metrics do not adequately reflect the actual performance of different binding site prediction methods. To address these issues, we first introduce UniSite-DS, the first UniProt (Unique Protein)-centric ligand binding site dataset, which contains 4.81 times more multi-site data and 2.08 times more overall data compared to the previously most widely used datasets. We then propose UniSite, the first end-to-end ligand binding site detection framework supervised by set prediction loss with bijective matching. In addition, we introduce Average Precision based on Intersection over Union (IoU) as a more accurate evaluation metric for ligand binding site prediction. Extensive experiments on UniSite-DS and several representative benchmark datasets demonstrate that IoU-based Average Precision provides a more accurate reflection of prediction quality, and that UniSite outperforms current state-of-the-art methods in ligand binding site detection. The dataset and codes will be made publicly available at https://github.com/quanlin-wu/unisite.
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