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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

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  1. Home
  3. Predictions From Deep Learning Propose Substantial Protein-carbohydrate Interplay.
  1. Home
  3. Predictions From Deep Learning Propose Substantial Protein-carbohydrate Interplay.

Related Experiment Video

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

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.

Proceedings of the National Academy of Sciences of the United States of America
|May 18, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed PiCAP, a neural network predicting protein-carbohydrate interactions, finding ~35-40% of proteins bind carbohydrates, significantly higher than previous estimates.

Keywords:
carbohydrateglycaninteractomeprotein–carbohydrate interactomeproteome

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Protein Target Prediction and Validation of Small Molecule Compound
10:21

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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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

Protein Target Prediction and Validation of Small Molecule Compound
10:21

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

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.