Department Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA 19004, USA.
Biomolecules. 2022 Oct 28;12(11):1589. doi: 10.3390/biom12111589.
This paper presents HBcompare, a method that classifies protein structures according to ligand binding preference categories by analyzing hydrogen bond topology. HBcompare excludes other characteristics of protein structure so that, in the event of accurate classification, it can implicate the involvement of hydrogen bonds in selective binding. This approach contrasts from methods that represent many aspects of protein structure because holistic representations cannot associate classification with just one characteristic. To our knowledge, HBcompare is the first technique with this capability. On five datasets of proteins that catalyze similar reactions with different preferred ligands, HBcompare correctly categorized proteins with similar ligand binding preferences 89.5% of the time. Using only hydrogen bond topology, classification accuracy with HBcompare surpassed standard structure-based comparison algorithms that use atomic coordinates. As a tool for implicating the role of hydrogen bonds in protein function categories, HBcompare represents a first step towards the automatic explanation of biochemical mechanisms.
本文提出了 HBcompare 方法，该方法通过分析氢键拓扑结构，根据配体结合偏好类别对蛋白质结构进行分类。HBcompare 排除了蛋白质结构的其他特征，因此，如果分类准确，就可以暗示氢键参与了选择性结合。这种方法与代表蛋白质结构许多方面的方法形成对比，因为整体表示形式不能将分类与仅一个特征相关联。据我们所知，HBcompare 是第一种具有这种能力的技术。在五个催化具有不同首选配体的相似反应的蛋白质数据集上，HBcompare 正确地对具有相似配体结合偏好的蛋白质进行了 89.5%的分类。仅使用氢键拓扑结构，HBcompare 的分类准确性就超过了使用原子坐标的标准基于结构的比较算法。作为一种暗示氢键在蛋白质功能类别中作用的工具，HBcompare 代表了自动解释生化机制的第一步。
