Decoding enzyme-substrate interaction topology reveals principles underlying catalytic efficiency and mutational outcomes
Abstract
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.
Related
- The Surrogate Substrate p-Nitrophenyl Phosphate (pNPP) for the Phosphatidylinositol-3-Phosphatase SapM from Mycobacterium tuberculosis: Mutational Analysis of pNPP Hydrolysis and Preliminary Single-Gating Observations
- DeepEnzyKat: Turnover number prediction through enzyme-substrate interaction modeling and adaptive expert ensembles
- Chemical foundation models reveal molecular principles underlying glycobiology
- Conserved and divergent modes of substrate interaction define selective localizations and functions of a Cdc14 phosphatase
- An inverse-protein-folding pre-trained model for predicting the mutational effects in protein-protein interactions
BibTeX
@misc{zhao2026decoding,
title = {{Decoding enzyme-substrate interaction topology reveals principles underlying catalytic efficiency and mutational outcomes}},
author = {Weiren Zhao and Takeyuki Tamura},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.24157},
eprint = {2609.24157},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.24157},
}
RIS
TY - UNPB
TI - Decoding enzyme-substrate interaction topology reveals principles underlying catalytic efficiency and mutational outcomes
AU - Weiren Zhao
AU - Takeyuki Tamura
PY - 2026
DA - 2026/09/21
PB - arXiv
DO - 10.48550/arxiv.2609.24157
UR - https://arxiv.org/abs/2609.24157
AB - 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.
ER -