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Related Experiment Video

Updated: Jun 29, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

A deep learning multi-attention Bi-GRU framework for kcat prediction with segmentation-based insights.

Priyanka1, Ramesh Chandra1, Md Shah Fahad1

  • 1BIT Mesra, India.

Enzyme and Microbial Technology
|June 6, 2026
PubMed
Summary

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We developed KcatNeuroCortex, a deep learning framework to predict enzyme catalytic efficiency. This interpretable tool accelerates enzyme engineering and synthetic biology by accurately estimating enzyme kinetics from sequence data.

Area of Science:

  • Biochemistry
  • Computational Biology
  • Machine Learning

Background:

  • Enzyme catalysis is vital for metabolism, but experimental measurement of catalytic constants (kcat) lags behind rapid sequence data generation.
  • This bottleneck hinders progress in metabolic engineering and synthetic biology.

Purpose of the Study:

  • To introduce KcatNeuroCortex, an interpretable deep learning framework for predicting enzyme catalytic efficiency.
  • To address the challenge of rapid enzyme sequence data growth versus slow experimental kcat determination.

Main Methods:

  • Developed a novel deep learning architecture combining Bi-directional Gated Recurrent Units (Bi-GRU) with a multi-attention mechanism.
  • Employed a segmentation-based strategy to capture local functional motifs and integrated them into a global representation.
Keywords:
Deep learningEnzyme catalysisEnzyme-substrate interactionsMetabolic pathwaysProtein engineeringSequence-based featuresk(cat) prediction

Related Experiment Videos

Last Updated: Jun 29, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Focused on modeling long-range interactions influencing enzyme catalysis.

    • Main Results:

    • KcatNeuroCortex achieved R² of 0.74 and RMSE of 0.77, a 57% improvement over DLKcat.
    • Demonstrated competitive performance, especially on diverse and low-similarity enzyme sequences.
    • Showcased the framework's robustness, scalability, and interpretability.

    Conclusions:

    • KcatNeuroCortex provides accurate and interpretable enzyme catalytic efficiency predictions.
    • The framework enhances enzyme engineering and kinetic parameter estimation.
    • Deep learning can offer biological insights into enzyme catalysis beyond mere prediction.