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Updated: Oct 16, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Deep learning allows genome-scale prediction of Michaelis constants from structural features.

Alexander Kroll¹, Martin K M Engqvist², David Heckmann¹

¹Institute for Computer Science and Department of Biology, Heinrich Heine University, Düsseldorf, Germany.

|October 19, 2021

Summary

This study introduces a machine learning model to predict enzyme-substrate affinity (KM). The model accurately estimates KM values across organisms, aiding metabolic research and kinetic modeling.

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Area of Science:

Biochemistry
Computational Biology
Systems Biology

Background:

The Michaelis constant (KM) quantifies enzyme-substrate affinity, crucial for understanding enzyme kinetics and cellular physiology.
Experimental KM determination is challenging and time-consuming, limiting available data for many enzyme-substrate pairs.
Existing KM data is sparse, hindering comprehensive analysis of metabolic pathways in model organisms.

Purpose of the Study:

To develop an organism-independent computational model for predicting enzyme-substrate KM values.
To leverage machine and deep learning techniques for accurate KM prediction.
To generate genome-scale KM predictions for 47 model organisms.

Main Methods:

Utilized graph neural networks to generate molecular fingerprints for substrates.
Employed deep numerical representations of enzyme amino acid sequences.
Trained an organism-independent machine and deep learning model on existing KM data.

Main Results:

The developed model successfully predicts KM values for natural enzyme-substrate combinations.
Achieved accurate, genome-scale KM predictions across 47 diverse model organisms.
Demonstrated the model's ability to generalize across different species.

Conclusions:

The predictive model significantly expands the availability of KM data for numerous enzyme-substrate pairs.
These predictions can inform the parameterization of kinetic models of cellular metabolism.
Facilitates a better understanding of the relationship between metabolite concentrations and cellular physiology.