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DeepMolecules: a web server for predicting enzyme and transporter-small molecule interactions.

Alexander Kroll1, Yvan Rousset1, Thomas Spitzlei1

  • 1Heinrich-Heine-University, Institute for Computer Science and Department of Biology, Universitätsstraße 1, 40225 Düsseldorf, Germany.

Nucleic Acids Research
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Summary
This summary is machine-generated.

DeepMolecules predicts protein-small molecule interactions using advanced deep learning models. This web server aids drug discovery and biocatalyst optimization by identifying substrates and predicting enzyme kinetics.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Understanding protein-small molecule interactions is crucial for various biological processes and drug development.
  • Accurate prediction of enzyme kinetics (kcat, KM) and substrate identification is essential for optimizing biocatalysts and drug efficacy.

Purpose of the Study:

  • To develop and provide an accessible web server, DeepMolecules, for predicting protein-small molecule interactions and enzyme kinetic parameters.
  • To integrate multiple state-of-the-art predictive models into a user-friendly platform.

Main Methods:

  • Utilized deep learning for numerical representations of proteins and small molecules.
  • Employed gradient-boosted decision tree models for interaction and kinetic predictions.
  • Developed a web interface supporting various input formats (SMILES, InChI, KEGG ID) and submission types (single, batch).

Main Results:

  • Achieved high predictive performance for enzyme substrate identification, transporter substrate identification, enzyme turnover number (kcat), and Michaelis constant (KM).
  • Integrated experimental data for a comprehensive view of protein-small molecule relationships.

Conclusions:

  • DeepMolecules offers a powerful, freely accessible tool for researchers in metabolic engineering, drug discovery, and biocatalysis.
  • The server facilitates the identification of potential substrates and the quantification of their catalytic properties, accelerating research and development.