PHIStruct: improving phage–host interaction prediction at low sequence similarity settings using structure-aware protein embeddings
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Abstract
Motivation: Recent computational approaches for predicting phage–host interaction have explored the use of sequence-only protein language models to produce embeddings of phage proteins without manual feature engineering. However, these embeddings do not directly capture protein structure information and structure-informed signals related to host specificity. Results: We present PHIStruct, a multilayer perceptron that takes in structure-aware embeddings of receptor-binding proteins, generated via the structure-aware protein language model SaProt, and then predicts the host from among the ESKAPEE genera. Compared against recent tools, PHIStruct exhibits the best balance of precision and recall, with the highest and most stable F1 score across a wide range of confidence thresholds and sequence similarity settings. The margin in performance is most pronounced when the sequence similarity between the training and test sets drops below 40%, wherein, at a relatively high-confidence threshold of above 50%, PHIStruct presents a 7%–9% increase in class-averaged F1 over machine learning tools that do not directly incorporate structure information, as well as a 5%–6% increase over BLASTp. Availability and implementation: The data and source code for our experiments and analyses are available at https://github.com/bioinfodlsu/PHIStruct.
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Cites
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Method
Flagged first, then by citations
- Authors
- Gonzales, M. E. M., Ureta, J. C., & Shrestha, A. M. S.
- Affiliations
- De La Salle University (PH)
- Walter and Eliza Hall Institute of Medical Research (AU)
- Corresponding author
- Shrestha, Anish M S
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-12-26
- Volume
- 41
- Issue
- 1
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC11783280
- OpenAlex
- W4406308609
- Cited by
- 23
- References
- 11
- Citations by year
- 2026: 17 · 2025: 6
- FWCI
- 6.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Department of Science and Technology Philippine Council for Health Research and Development
- Topics
- Physical Sciences › Environmental Science › Ecology › Bacteriophages and microbial interactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- receptor binding protein, host specificity, low sequence similarity, phage resistance genes, protein structure, multilayer perceptron
- MeSH terms
- Machine Learning, Algorithms, Bacteriophages, Software, Viral Proteins, Computational Biology
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