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Predictions from Deep Learning Propose Substantial Protein-Carbohydrate Interplay.

Samuel W Canner1, Ronald L Schnaar2,3, Jeffrey J Gray1,4

  • 1Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, United States.

Biorxiv : the Preprint Server for Biology
|March 31, 2025
PubMed
Summary
This summary is machine-generated.

We developed computational tools, Protein interaction of Carbohydrates Predictor (PiCAP) and Carbohydrate Protein Site Identifier 2 (CAPSIF2), to identify protein-carbohydrate interactions. These tools predict carbohydrate binding proteins and their interaction sites with high accuracy, revealing widespread interactions in key proteomes.

Keywords:
Glycancarbohydrateglycomeinteractomelectomeproteome

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Identifying protein-carbohydrate interactions is crucial for understanding biological processes, yet experimental methods are resource-intensive.
  • Current estimates suggest only 1.5-5% of proteins bind carbohydrates, contrasting with the 50-70% of proteins known to be glycosylated.
  • A significant gap exists in accurately predicting carbohydrate-binding proteins and their interaction sites.

Purpose of the Study:

  • To develop novel computational tools for predicting protein-carbohydrate binding and identifying specific interaction sites.
  • To establish a robust dataset for training and validating predictive models for protein-carbohydrate interactions.
  • To assess the prevalence of carbohydrate-binding proteins across different proteomes and investigate their biological relevance.

Main Methods:

  • Developed the Protein interaction of Carbohydrates Predictor (PiCAP), a neural network model trained on known carbohydrate binders and non-binders.
  • Created the Carbohydrate Protein Site Identifier 2 (CAPSIF2) model to predict specific protein residues involved in carbohydrate binding.
  • Utilized a curated dataset including known binders, DNA-binding proteins, cytoskeletal components, antibodies, and small-molecule binders for training and validation.

Main Results:

  • PiCAP achieved 90% balanced accuracy in predicting protein-level carbohydrate binding/non-binding.
  • CAPSIF2 demonstrated a Dice coefficient of 0.57 for residue-level predictions, outperforming existing models.
  • Application to human neural cells and proteomes (E. coli, M. musculus, H. sapiens) predicted 35-40% of proteins bind carbohydrates.

Conclusions:

  • PiCAP and CAPSIF2 are effective computational tools for predicting protein-carbohydrate interactions and binding sites.
  • A substantial portion of proteins across various proteomes are predicted to engage in carbohydrate binding.
  • These findings highlight the significant role of protein-carbohydrate interactions in cellular functions and open avenues for further research.