解码酶-底物相互作用拓扑揭示催化效率和突变结果背后的原理
Decoding enzyme-substrate interaction topology reveals principles underlying catalytic efficiency and mutational outcomes
浏览论文内容
中文总结 AI 辅助
本研究提出可解释框架Interkcat，通过酶-底物相互作用拓扑预测催化效率（R2=0.701），并揭示突变结果由高阶拓扑特征决定，为理解酶功能与进化提供统一原理。
中文摘要 AI 辅助
酶的转换数（kcat）定义了催化效率，并约束了代谢的定量模型，然而控制kcat及其对突变响应的分子决定因素仍然知之甚少。测量稀疏且劳动密集，大多数计算方法提供数值预测，却不解释酶-底物相互作用如何塑造催化结果。因此，一个核心挑战是识别决定突变作用位置以及其功能结果如何在酶-底物相互作用网络中被编码的拓扑原理。在此，我们表明催化效率和突变效应可以通过酶-底物相互作用拓扑来解释。我们开发了Interkcat，一个可解释的双向交叉注意力框架，它捕捉蛋白质残基和底物原子之间的相互协调。在统一基准上优化后，Interkcat实现了最先进的预测性能（R2 = 0.701）。从其学习到的表示中，我们推导出一个相互作用拓扑评分（ITS），该评分识别在序列区域中统计富集的突变敏感位点，而无需显式结构输入。我们进一步证明，高阶拓扑特征区分相反的突变结果：致死突变破坏协调网络，而保留或增强活性的突变则保留稀疏、全局组织的耦合。这些发现确立了相互作用拓扑作为连接酶序列、催化效率和进化扰动的统一原理。
英文摘要
The enzyme turnover number (kcat) defines catalytic efficiency and constrains quantitative models of metabolism, yet the molecular determinants governing kcat and its response to mutation remain poorly understood. Measurements are sparse and labor-intensive, and most computational approaches provide numerical predictions without explaining how enzyme-substrate interactions shape catalytic outcomes. A central challenge is therefore to identify the topological principles that determine where mutations act and how their functional outcomes are encoded within the enzyme-substrate interaction network. Here, we show that catalytic efficiency and mutational effects can be interpreted through enzyme-substrate interaction topology. We developed Interkcat, an interpretable bidirectional cross-attention framework that captures reciprocal coordination between protein residues and substrate atoms. Optimized on a unified benchmark, Interkcat achieves state-of-the-art predictive performance (R2 = 0.701). From its learned representations, we derive an Interaction Topology Score (ITS) that identifies sequence regions statistically enriched for mutation-sensitive sites without explicit structural inputs. We further demonstrate that higher-order topological features distinguish opposing mutational outcomes: lethal mutations disrupt coordinated networks, whereas activity-preserving or enhancing mutations retain sparse, globally organized coupling. These findings establish interaction topology as a unifying principle linking enzyme sequence, catalytic efficiency, and evolutionary perturbation.
发表机构
- Institute for Chemical Research, Kyoto University（京都大学化学研究所）
- Graduate School of Informatics, Kyoto University（京都大学情报学研究科）
机构由 AI 辅助整理，请以论文原文为准。
