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PHIStruct: improving phage-host interaction prediction at low sequence similarity settings using structure-aware

Mark Edward M Gonzales1,2, Jennifer C Ureta1,2,3, Anish M S Shrestha1,2

  • 1Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila 1004, Philippines.

Bioinformatics (Oxford, England)
|January 13, 2025
PubMed
Summary
This summary is machine-generated.

PHIStruct accurately predicts phage hosts using structure-aware protein embeddings. This computational tool outperforms existing methods, especially with low sequence similarity, advancing phage-host interaction prediction.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Microbiology

Background:

  • Current sequence-only models for phage-host interaction prediction lack structural information.
  • Protein embeddings from these models do not capture crucial structure-informed signals for host specificity.

Purpose of the Study:

  • To develop a novel computational tool, PHIStruct, for predicting phage hosts.
  • To incorporate protein structure information into phage-host interaction prediction models.

Main Methods:

  • PHIStruct utilizes structure-aware embeddings from the SaProt protein language model.
  • A multilayer perceptron in PHIStruct predicts host genera (ESKAPEE) based on receptor-binding protein structure-aware embeddings.

Main Results:

  • PHIStruct demonstrates superior precision and recall compared to existing tools.
  • It achieves the highest and most stable F1 score across various confidence thresholds and sequence similarities.
  • PHIStruct shows a 7%-9% increase in class-averaged F1 over non-structure-informed ML tools and a 5%-6% increase over BLASTp when sequence similarity is below 40%.

Conclusions:

  • PHIStruct effectively predicts phage-host interactions by integrating protein structure information.
  • The tool offers improved performance, particularly in scenarios with limited sequence similarity.
  • PHIStruct represents a significant advancement in computational prediction of phage-host specificity.