Decoding enzyme-substrate interaction topology reveals principles underlying catalytic efficiency and mutational outcomes

arXiv:2609.24157 · q-bio.BM · Submitted 2026-09-21 · Read on arXiv

q-bio.BM

Submitted: 2026-09-21

Updated: 2026-09-21

Code: https://github.com/ZWR0/Interkcat

Terminology

• Embeddings — 12× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Alignment — 8× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Core — 7× in this paper · explained in 2 episodes
• Attention Mechanism — 6× in this paper · explained in 3 episodes
• Cross-Attention Mechanism — 5× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Interaction Topology Score — 3× in this paper
• Decode — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• ESM-2 and Graph Neural Networks — 1× in this paper
• Enzyme Commission — 1× in this paper
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Singular Value Decomposition — 1× in this paper
• Singular Value Decomposition (SVD) — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Fast Graph Representation Learning with PyTorch Geometric
• PyG 2.0: Scalable Learning on Real World Graphs
• Semi-Supervised Classification with Graph Convolutional Networks
• Graph Attention Networks

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