蛋白质性质的持久流形学习
Persistent Manifold Learning of Protein Properties
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中文总结 AI 辅助
该研究针对生物分子结合预测难题，提出持久流形学习框架，利用边界诱导图拉普拉斯算子提取信息，结合相关模型表示与梯度提升决策树，在金属蛋白-配体和蛋白质-蛋白质基准测试中表现优于现有方法。
中文摘要 AI 辅助
预测两个生物分子的结合紧密程度仍是一项重大挑战，部分原因是不同相互作用类别呈现不同界面。我们引入持久流形学习（PML），这是一个将结合界面描述为多尺度流形族的新型计算框架。边界诱导图拉普拉斯算子提取拓扑不变量和非调和谱信息。这些流形嵌入与蛋白质和分子语言模型表示相结合，并与梯度提升决策树配对。我们的PML在金属蛋白-配体和蛋白质-蛋白质基准测试中优于现有方法。
英文摘要
Predicting how tightly two biomolecules bind remains a major challenge, in part because different interaction classes present dissimilar interfaces, from compact metal-coordinated pockets to broad, featureless protein surfaces. We introduce persistent manifold learning (PML), a novel computational framework that describes a binding interface as a family of multiscale manifolds. Boundary-Induced Graph Laplacian, a discrete realization of de Rham-Hodge theory, then extracts topological invariants together with nonharmonic spectral information, capturing the geometry of an interface as well as its topology. These manifold embeddings are combined with protein and molecular language model representations and paired with gradient boosting decision trees. Our PML outperforms state-of-the-art methods on metalloprotein-ligand and protein-protein benchmarks.
