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Improving protein function prediction by learning and integrating representations of protein sequences and function

Frimpong Boadu1, Jianlin Cheng1

  • 1Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, United States.

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|September 5, 2024
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Summary

Predicting protein function is challenging, especially for rare terms. TransFew, a novel transformer model, improves protein function prediction accuracy by integrating protein sequences and Gene Ontology (GO) terms.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Experimentally determining protein function is feasible for less than 1% of proteins, necessitating computational prediction for most.
  • Current protein function prediction methods struggle with accuracy, particularly for rare Gene Ontology (GO) terms with limited annotations in databases like UniProt.

Purpose of the Study:

  • To introduce TransFew, a novel transformer model designed to enhance protein function prediction accuracy.
  • To improve the prediction of rare protein function terms by leveraging integrated representations of protein sequences and GO terms.

Main Methods:

  • TransFew utilizes a pre-trained protein language model (ESM2-t48) for protein sequence representation.
  • It employs a biological natural language model (BioBert) and a graph convolutional neural network-based autoencoder for semantic representation of GO terms, considering their definitions and hierarchical relationships.
  • Cross-attention mechanisms integrate protein sequence and GO term representations for function prediction.

Main Results:

  • The integrated approach in TransFew significantly enhances overall protein function prediction accuracy.
  • TransFew demonstrates robust performance in predicting rare function terms with limited annotations.
  • The model facilitates annotation transfer between GO terms, improving prediction for underrepresented functions.

Conclusions:

  • TransFew offers a powerful new approach for accurate protein function prediction, addressing limitations of existing methods.
  • The model's ability to handle rare function terms has significant implications for annotating the vast majority of proteins with unknown functions.
  • The integration of sequence and label representations is key to TransFew's improved performance.