OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning

• URL: http://arxiv.org/abs/2506.17963v1
• Date: Sun, 22 Jun 2025 09:40:40 GMT
• Title: OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning
• Authors: Zhiwei Nie, Hongyu Zhang, Hao Jiang, Yutian Liu, Xiansong Huang, Fan Xu, Jie Fu, Zhixiang Ren, Yonghong Tian, Wen-Bin Zhang, Jie Chen,
• Abstract summary: We introduce a two-stage progressive framework, OmniESI, for enzyme-substrate interaction prediction through conditional deep learning.<n>We show that OmniESI consistently delivered superior performance than state-of-the-art specialized methods.<n>Overall, OmniESI represents a unified predictive approach for enzyme-substrate interactions.
• Score: 46.402707495664174
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding and modeling enzyme-substrate interactions is crucial for catalytic mechanism research, enzyme engineering, and metabolic engineering. Although a large number of predictive methods have emerged, they do not incorporate prior knowledge of enzyme catalysis to rationally modulate general protein-molecule features that are misaligned with catalytic patterns. To address this issue, we introduce a two-stage progressive framework, OmniESI, for enzyme-substrate interaction prediction through conditional deep learning. By decomposing the modeling of enzyme-substrate interactions into a two-stage progressive process, OmniESI incorporates two conditional networks that respectively emphasize enzymatic reaction specificity and crucial catalysis-related interactions, facilitating a gradual feature modulation in the latent space from general protein-molecule domain to catalysis-aware domain. On top of this unified architecture, OmniESI can adapt to a variety of downstream tasks, including enzyme kinetic parameter prediction, enzyme-substrate pairing prediction, enzyme mutational effect prediction, and enzymatic active site annotation. Under the multi-perspective performance evaluation of in-distribution and out-of-distribution settings, OmniESI consistently delivered superior performance than state-of-the-art specialized methods across seven benchmarks. More importantly, the proposed conditional networks were shown to internalize the fundamental patterns of catalytic efficiency while significantly improving prediction performance, with only negligible parameter increases (0.16%), as demonstrated by ablation studies on key components. Overall, OmniESI represents a unified predictive approach for enzyme-substrate interactions, providing an effective tool for catalytic mechanism cracking and enzyme engineering with strong generalization and broad applicability.

Related papers

• KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction [60.23701115249195]
  We propose KEPLA, a novel deep learning framework that integrates prior knowledge from Gene Ontology and protein properties.<n> Experiments on two benchmark datasets demonstrate that KEPLA consistently outperforms state-of-the-art baselines.<n> Furthermore, interpretability analyses based on knowledge graph relations and cross attention maps provide valuable insights into the underlying predictive mechanisms.
  arXiv  Detail & Related papers  (2025-06-16T08:02:42Z)
• Docking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration [22.154465616964263]
  We present Docking-Aware Attention (DAA), a novel architecture that generates dynamic, context-dependent protein representations.<n>We evaluate our method on enzymatic reaction prediction, where it outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-02-03T15:52:38Z)
• Reaction-conditioned De Novo Enzyme Design with GENzyme [64.14088142258498]
  textscGENzyme is a textitde novo enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex.<n>textscGENzyme is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes.
  arXiv  Detail & Related papers  (2024-11-10T00:37:26Z)
• Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model [3.4494754789770186]
  Deep learning methods for protein modeling have demonstrated superior results at lower costs compared to traditional approaches. In mutation effect prediction, the key to pre-training deep learning models lies in accurately interpreting the complex relationships among protein sequence, structure, and function. This study introduces a retrieval-enhanced protein language model for comprehensive analysis of native properties from sequence and local structural interactions.
  arXiv  Detail & Related papers  (2024-10-28T15:28:51Z)
• EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics [51.47520281819253]
  Enzyme design is a critical area in biotechnology, with applications ranging from drug development to synthetic biology. Traditional methods for enzyme function prediction or protein binding pocket design often fall short in capturing the dynamic and complex nature of enzyme-substrate interactions. We introduce EnzymeFlow, a generative model that employs flow matching with hierarchical pre-training and enzyme-reaction co-evolution to generate catalytic pockets.
  arXiv  Detail & Related papers  (2024-10-01T02:04:01Z)
• Autoregressive Enzyme Function Prediction with Multi-scale Multi-modality Fusion [11.278610817877578]
  We introduce MAPred, a novel multi-modality and multi-scale model designed to autoregressively predict the EC number of proteins. MAPred integrates both the primary amino acid sequence and the 3D tokens of proteins, employing a dual-pathway approach to capture comprehensive protein characteristics. Evaluations on benchmark datasets, including New-392, Price, and New-815, demonstrate that our method outperforms existing models.
  arXiv  Detail & Related papers  (2024-08-11T08:28:43Z)
• PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction [63.50967073653953]
  Compound-Protein Interaction prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. Existing deep learning-based methods utilize only the single modality of protein sequences or structures. We propose a novel multi-scale Protein Sequence-structure Contrasting framework for CPI prediction.
  arXiv  Detail & Related papers  (2024-02-13T03:51:10Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Machine learning modeling of family wide enzyme-substrate specificity screens [2.276367922551686]
  Biocatalysis is a promising approach to synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. The adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates.
  arXiv  Detail & Related papers  (2021-09-08T19:44:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.