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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: 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

A robust framework for protein-protein interaction prediction with multi-objective ensemble learning and

Subhashis Chatterjee1, Shreya Swarnaker2

  • 1Department of Mathematics and Computing, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, 826007, India.

Scientific Reports
|May 29, 2026
PubMed
Summary

This study introduces a novel framework for predicting protein-protein interactions (PPIs) using advanced protein language models and evolutionary algorithms. The method enhances accuracy and efficiency in identifying crucial biological interactions from protein sequences.

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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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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Protein-protein interactions (PPIs) are fundamental to cellular functions.
  • Experimental PPI identification is resource-intensive and often yields incomplete data.
  • Accurate PPI prediction is vital for understanding biological processes.

Purpose of the Study:

  • To develop a robust and efficient framework for predicting protein-protein interactions (PPIs) using sequence data.
  • To integrate protein language models with evolutionary optimization and ensemble learning for enhanced prediction accuracy.
  • To provide a reliable computational tool for large-scale PPI analysis.

Main Methods:

  • Utilized Prot-T5-XL-Uniref-50 protein language model for sequence embedding.
  • Applied Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction.
  • Developed a hybrid approach combining Non-dominated Sorting Genetic Algorithm-II (NSGA-II) with Random Forest for ensemble learning.
  • Employed SHapley Additive exPlanations (SHAP) for feature interpretability.

Main Results:

  • The proposed hybrid framework demonstrated superior performance compared to state-of-the-art methods on benchmark datasets (Human, E. coli, Drosophila, C. elegans).
  • The evolutionary strategy successfully optimized ensemble parameters, balancing prediction accuracy and diversity.
  • Feature importance analysis identified key embedding dimensions influencing PPI predictions.

Conclusions:

  • The developed framework offers a reliable and scalable solution for PPI prediction based solely on protein sequences.
  • Integration of language models and evolutionary computation significantly improves PPI prediction.
  • The method provides insights into the features driving interaction predictions, aiding biological interpretation.