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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,...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
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: May 31, 2026

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

SAKE-PP: A Spatial-Attention Equivariant Network for Accurate Ranking of Protein-Protein Interaction Models.

Yuzhi Xu1,2, Wei Xia1,2, Chao Zhang3

  • 1NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200126, China.

JACS Au
|May 29, 2026
PubMed
Summary

SAKE-PP, a new graph neural network, accurately scores protein-protein interaction models without native references. This method improves decoy selection and streamlines structure-guided drug design.

Keywords:
Deep LearningMolecular DynamicsProtein−Protein ComplexesScoring FunctioniRMSD Prediction

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Mapping Dysfunctional Protein-Protein Interactions in Disease
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Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Related Experiment Videos

Last Updated: May 31, 2026

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

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Prioritizing near-native protein-protein interaction (PPI) models is crucial but challenging in structural biology.
  • Current methods often require native references, limiting their applicability.

Purpose of the Study:

  • To develop SAKE-PP, a novel scoring function for accurate prioritization of PPI models.
  • To directly regress interface Root Mean Square Deviation (iRMSD) without relying on native structures.

Main Methods:

  • SAKE-PP utilizes a physics-inspired, spatial-attention equivariant graph neural network.
  • It integrates force-field-like attention with Laplacian-eigenvector orientation.
  • Training employed a hierarchical iRMSD-guided sampling strategy on PDBBind.

Main Results:

  • SAKE-PP improved AF3-decoy selection by 13.75% (iRMSD) and 12.5% (DockQ) on the 2024PDB benchmark.
  • It outperformed AF3 ranking metrics in overlap, hit-rate, and correlation.
  • Zero-shot evaluation on antibody-antigen complexes showed a 0.4 increase in correlation.

Conclusions:

  • SAKE-PP effectively promotes geometrically near-native and energetically plausible PPI interfaces.
  • It reduces wasted molecular dynamics (MD) trajectories and enhances refinement reliability.
  • SAKE-PP offers a robust scoring function to accelerate PPI evaluation and structure-guided drug design.