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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 Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
Protein and Protein Structure02:15

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme can...
Protein Folding01:22

Protein Folding

Overview
Protein Folding01:25

Protein Folding

Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...

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Updated: May 14, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

RIMGOGraph: integrating AlphaFold-derived residue interaction graphs and protein language embeddings for

Tong Chang1, Nguyen Quoc Khanh Le2, Matthew Chin Heng Chua3

  • 1Institute of System Science, National University of Singapore, 119077, Singapore; AIBioMed Research Group, Taipei Medical University, Taipei, 110, Taiwan.

International Journal of Biological Macromolecules
|May 12, 2026
PubMed
Summary
This summary is machine-generated.

RIMGOGraph integrates protein structure and sequence data using deep learning for accurate function prediction. This computational biology tool enhances understanding of protein roles, especially molecular functions and cellular components.

Keywords:
AlphaFold2Biological macromoleculesExplainable artificial intelligenceGraph neural networksProtein function annotationResidue interaction graphsStructure-informed deep learning

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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

Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural biology

Background:

  • Protein function annotation is a significant challenge due to rapid database growth outpacing experimental data.
  • Advances in protein structure prediction (e.g., AlphaFold2) offer new avenues for incorporating 3D structural information.
  • Integrating structural topology with sequence data for prediction remains an open research problem.

Purpose of the Study:

  • To introduce RIMGOGraph, a multimodal deep learning framework for protein function prediction.
  • To effectively combine predicted protein structures with sequence information for enhanced annotation.
  • To evaluate the framework's performance on human protein function prediction tasks.

Main Methods:

  • RIMGOGraph utilizes residue interaction graphs from predicted protein structures and sequence embeddings from the ESM-2 model.
  • Graph neural networks encode structural relationships, while a co-attention module fuses sequence and structural representations.
  • The framework was tested on a human protein dataset with Gene Ontology annotations.

Main Results:

  • RIMGOGraph achieved competitive performance in predicting Biological Process, Cellular Component, and Molecular Function.
  • The model demonstrated strong results, outperforming baseline methods in Fmax, AUC, and AUPR scores.
  • Ablation studies confirmed the value of structural modeling and cross-modal fusion, particularly for Molecular Function and Cellular Component prediction.

Conclusions:

  • RIMGOGraph offers an effective and scalable approach for structure-guided protein function prediction in the era of advanced structure prediction.
  • The framework highlights the importance of structural context for predicting biochemical activity and subcellular localization.
  • Future work may involve multi-species training and low-homology benchmarking to improve generalizability.