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The transport of solutes across the cell membrane is essential for metabolic processes, like maintaining cell size and volume, generating the action potential, exchanging nutrients and gases, etc. Membrane transport can be either passive or active. It can be simple diffusion, facilitated, or mediated transport aided by transport proteins such as transporters and channels.
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Application of Protein Structure Encodings and Sequence Embeddings for Transporter Substrate Prediction.

Andreas Denger1, Volkhard Helms1

  • 1Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.

Molecules (Basel, Switzerland)
|August 14, 2025
PubMed
Summary

Deep learning models accurately predict membrane transporter substrates using protein sequence and structure data. New deep learning features and feedforward neural networks improve classification performance over traditional methods.

Keywords:
AlphaFolddeep learningfeature extractiongene ontologymachine learningmembrane bioinformaticsmembrane transportprotein function predictionprotein language modelsubstrate prediction

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Membrane transporters are vital cellular components involved in translocating substrates across membranes.
  • Accurate identification of transporter substrates is crucial for metabolomics, pharmacology, and biotechnology.
  • Traditional methods for substrate prediction rely on amino acid k-mer frequencies and evolutionary information.

Purpose of the Study:

  • To evaluate novel deep learning (DL) features derived from protein sequence and structure for predicting membrane transporter substrates.
  • To compare the performance of DL features with traditional methods using Support Vector Machine (SVM) and feedforward neural network (FNN) models.
  • To assess the utility of 3D structure encodings and structural embeddings in substrate prediction.

Main Methods:

  • Leveraged advanced DL techniques including protein language models (pLMs) for amino acid sequence embeddings.
  • Utilized AlphaFold 2 for 3D structure predictions and FoldSeek for structure-encoding 3Di sequences.
  • Compared DL-based features (sequence and structure) against k-mer frequencies and PSSMs.
  • Implemented SVM and FNN models to classify transporter substrates.
  • Evaluated models on sugar and amino acid carriers in *A. thaliana* and human ion channels.

Main Results:

  • DL-based features and the FNN model demonstrated superior and more consistent classification performance.
  • Direct 3D structure encodings (Foldseek) and structural embeddings (ProstT5) matched state-of-the-art sequence embeddings (ProtT5-XL) when used with the FNN.
  • Deep learning approaches significantly outperformed previous methods in substrate prediction accuracy.

Conclusions:

  • Deep learning, integrating sequence and structural information, offers a powerful approach for predicting membrane transporter substrates.
  • The FNN model combined with novel DL features provides a robust and accurate method for transporter substrate identification.
  • This study highlights the potential of advanced computational methods to advance our understanding of membrane transport and its implications in various biological fields.