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Survey of Protein Sequence Embedding Models.

Chau Tran1, Siddharth Khadkikar2, Aleksey Porollo3,4,5

  • 1Department of Computer Science, University of Cincinnati, Cincinnati, OH 45219, USA.

International Journal of Molecular Sciences
|February 25, 2023
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Summary
This summary is machine-generated.

Protein language models generate numerical embeddings for diverse protein sequences. This study evaluates models for yeast proteome analysis, gene ontology annotation, and human disease variant prediction, finding significant differences in pathogenic mutations.

Keywords:
deep learningnatural language processingprotein annotationprotein language modelprotein sequence embeddingsurvey of embedding models

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Protein science

Background:

  • Natural language processing (NLP) algorithms have led to protein language models (PLMs) that encode protein sequences into fixed-size numerical vectors (embeddings).
  • These PLMs are crucial for analyzing diverse protein lengths and amino acid compositions in various biological contexts.
  • Representative models include Esm, Esm1b, ProtT5, SeqVec, and their derivatives like GoPredSim and PLAST.

Purpose of the Study:

  • To survey and evaluate representative protein embedding models for diverse computational biology tasks.
  • To assess the performance of these models in embedding the Saccharomyces cerevisiae proteome.
  • To investigate the utility of PLMs for gene ontology (GO) annotation, human disease variant analysis, antimicrobial resistance correlation, and fungal mating factor analysis.

Main Methods:

  • Surveyed and applied established protein embedding models (Esm, Esm1b, ProtT5, SeqVec) and their derivatives.
  • Embedded the Saccharomyces cerevisiae proteome using these models.
  • Utilized embeddings for GO annotation of uncharacterized proteins.
  • Analyzed embeddings of human protein variants in relation to disease status.
  • Correlated embeddings of Escherichia coli beta-lactamase TEM-1 mutants with experimental antimicrobial resistance data.
  • Examined embeddings of fungal mating factors.

Main Results:

  • All surveyed models indicated that uncharacterized yeast proteins are typically short (<200 amino acids), less rich in aspartate and glutamate, and enriched in cysteine.
  • High-confidence GO term annotation was achieved for less than half of the uncharacterized yeast proteins.
  • A statistically significant difference was observed in cosine similarity scores between benign and pathogenic human protein mutations.
  • Embeddings of TEM-1 mutants showed low to no correlation with experimentally measured minimal inhibitory concentrations (MICs).

Conclusions:

  • Protein language models offer valuable insights into protein characteristics and functions across different organisms and contexts.
  • PLMs demonstrate potential for identifying patterns in uncharacterized proteins and predicting disease associations, though with limitations.
  • The correlation between protein embeddings and quantitative functional measures like MICs requires further investigation and model refinement.