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Related Concept Videos

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,...
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
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 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: Jun 18, 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

Predicting protein complex geometries with a neural network.

Myong-Ho Chae1, Florian Krull, Stephan Lorenzen

  • 1Department of Biology, University of Science, Unjong-District, Pyongyang, DPR Korea.

Proteins
|November 26, 2009
PubMed
Summary
This summary is machine-generated.

A new neural network model effectively predicts protein complex geometries by analyzing atom-pair distances in decoys. This approach improves protein docking accuracy, outperforming existing methods in unbound docking scenarios.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 18, 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

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

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Biophysics

Background:

  • Protein docking is crucial for understanding biological processes, but accurately predicting complex structures from unbound components (unbound docking) remains challenging.
  • Distinguishing near-native protein complex geometries from numerous non-native decoys requires robust scoring functions.

Purpose of the Study:

  • To develop and evaluate a novel neural network-based scoring function for predicting protein complex geometries in unbound docking.
  • To improve the accuracy of protein docking predictions by leveraging atom-pair distance distributions.

Main Methods:

  • A neural network was constructed using atom-pair distance distributions from a large set of decoys for 185 protein complexes.
  • The network was trained using 2000 near-native decoys per complex, incorporating a protein complex identity input neuron for normalization.
  • Parameters were optimized to mimic a scoring funnel, and a distance-dependent atom pair potential was employed.

Main Results:

  • The neural network approach significantly improved docking prediction accuracy, particularly with the inclusion of polar hydrogen atoms.
  • A distance-dependent atom pair potential demonstrated superior performance compared to a simple atom-pair contact potential.
  • The developed scoring function achieved reasonable performance in rigid-body unbound docking, comparable to established methods like ZDOCK and RosettaDock.

Conclusions:

  • The neural network-based scoring function offers a promising and relatively simple method for improving protein docking predictions.
  • This approach effectively addresses the reference state problem inherent in knowledge-based energy function derivation.
  • The study highlights the potential of machine learning in enhancing the accuracy and efficiency of computational protein structure prediction.