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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,...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...

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Related Experiment Video

Updated: Jul 14, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures.

Xinyu Wang1, Debswapna Bhattacharya1

  • 1Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States.

Journal of Chemical Information and Modeling
|July 13, 2026
PubMed
Summary

ORIGAMI, a novel graph neural network, enhances protein complex structure assessment by incorporating orientation-aware geometric features. This deep learning approach improves the accuracy of predicting multimeric protein interfaces, a critical challenge in structural biology.

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Multimer-PAGE: A Method for Capturing and Resolving Protein Complexes in Biological Samples
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Multimer-PAGE: A Method for Capturing and Resolving Protein Complexes in Biological Samples

Published on: May 5, 2017

Related Experiment Videos

Last Updated: Jul 14, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Multimer-PAGE: A Method for Capturing and Resolving Protein Complexes in Biological Samples
07:40

Multimer-PAGE: A Method for Capturing and Resolving Protein Complexes in Biological Samples

Published on: May 5, 2017

Area of Science:

  • Computational structural biology
  • Deep learning applications in protein structure prediction
  • Bioinformatics and computational biophysics

Background:

  • Assessing the quality of computationally predicted protein complex structures is a significant challenge.
  • Existing graph neural network methods for protein interfaces overlook crucial 3D geometric and orientational information.
  • Current approaches often rely solely on scalar features, limiting their ability to capture complex spatial relationships.

Purpose of the Study:

  • To introduce ORIGAMI, an orientation-aware graph neural network designed for superior assessment of multimeric protein interfaces.
  • To leverage both scalar and 3D vector representations for capturing fine-grained orientational relationships between residues.
  • To improve the accuracy of estimating protein-protein interface quality using deep learning.

Main Methods:

  • Development of ORIGAMI, an SO(3)-equivariant graph neural network incorporating scalar and 3D vector node features.
  • Application of symmetry-aware geometric operations to capture orientational relationships across protein-protein interfaces.
  • Training and evaluation on targets from Critical Assessment of Structure Prediction (CASP) challenges, including CASP16.

Main Results:

  • ORIGAMI demonstrates superior performance in assessing multimeric protein interfaces compared to existing methods.
  • Significant performance gains were observed in the CASP16 interface-level evaluation.
  • The model shows robust cross-metric generalization, accurately reproducing DockQ scores despite being trained on iLDDT.

Conclusions:

  • ORIGAMI represents a significant advancement in protein complex interface quality assessment by integrating orientation-aware geometric features.
  • The method's ability to capture fine-grained orientational relationships enhances prediction accuracy.
  • ORIGAMI offers a powerful, freely available tool for the computational structural biology community.