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Updated: May 20, 2026
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
Published on: January 26, 2024
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 21218.
Summary
We developed PiCAP, a neural network predicting protein-carbohydrate interactions, finding ~35-40% of proteins bind carbohydrates, significantly higher than previous estimates.
Area of Science:
- Biochemistry
- Computational Biology
- Proteomics
Background:
- Noncovalent interactions between proteins and carbohydrates (glycans) are crucial for biological processes like metabolism and cell recognition.
- Identifying the full scope of protein-carbohydrate interactions (interactomes) within organisms is a significant challenge.
- Current estimates suggest less than 5% of proteins bind carbohydrates, a figure lacking robust validation.
Purpose of the Study:
- To develop computational tools for predicting protein-carbohydrate binding and interaction sites.
- To re-evaluate the prevalence of carbohydrate-binding proteins across multiple proteomes.
- To identify biological functions associated with predicted carbohydrate-binding proteins.
Main Methods:
- Development of a neural network, Protein interaction of Carbohydrates Predictor (PiCAP), for predicting protein-carbohydrate binding.
- Training PiCAP on a curated dataset of known binders and non-binders (transcription factors, cytoskeletal, small-molecule binders).
- Development of Carbohydrate Protein Site Identifier 2 (CAPSIF2) for predicting carbohydrate-interacting residues.
Main Results:
- PiCAP achieved 90% balanced accuracy in predicting protein-level carbohydrate binding.
- CAPSIF2 demonstrated a Dice coefficient of 0.57 in residue-level predictions, outperforming prior models.
- PiCAP predicts 35-40% of proteins in six diverse proteomes bind carbohydrates, with 75% of extracellular/cell surface proteins binding.
Conclusions:
- Computational prediction offers a viable alternative to experimental screening for identifying protein-carbohydrate interactions.
- A significantly higher proportion of proteins, particularly those on the cell surface, engage in carbohydrate binding than previously estimated.
- Predicted carbohydrate binders are functionally enriched in processes like growth factor signaling and cell adhesion, highlighting their biological importance.
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