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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 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,...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Related Experiment Video

Updated: Jul 14, 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

Improving the performance of an SVM-based method for predicting protein-protein interactions.

Shinsuke Dohkan1, Asako Koike, Toshihisa Takagi

  • 1Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, (CB01) 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8581, Japan. dohkan@cb.k.u-tokyo.ac.jp

In Silico Biology
|May 24, 2007
PubMed
Summary

This study introduces a novel bioinformatics method using multiple Support Vector Machines (SVMs) with more negative protein interactions for training. This approach enhances accuracy and efficiency in predicting protein-protein interactions and assessing high-throughput data reliability.

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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: Jul 14, 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:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Predicting protein-protein interactions (PPIs) is vital for understanding cellular mechanisms.
  • Existing supervised machine learning methods often use balanced positive/negative datasets, leading to numerous false positives.
  • The representation of negative interaction data is critical for accurate PPI prediction.

Purpose of the Study:

  • To develop an improved method for predicting protein-protein interactions using Support Vector Machines (SVMs).
  • To address the limitations of traditional methods by incorporating a larger number of negative interaction pairs during training.
  • To enhance the reliability assessment of high-throughput PPI data.

Main Methods:

  • Developed a novel method employing multiple Support Vector Machines (SVMs).
  • Utilized datasets with a higher ratio of non-interacting protein pairs (negatives) to interacting pairs (positives) for training.
  • Tested the approach on yeast and human protein interaction data.
  • Evaluated the impact of increasing negative data on classifier performance.

Main Results:

  • Increasing the number of negative samples significantly improved the performance of individual SVM classifiers.
  • A multiple SVM approach demonstrated enhanced classifier performance and reduced training time, especially with multiple CPUs.
  • The developed method shows potential for assessing the reliability of high-throughput PPI data.

Conclusions:

  • The proposed method effectively improves the accuracy and efficiency of protein-protein interaction prediction.
  • Incorporating a larger set of negative interaction data is crucial for robust machine learning models in bioinformatics.
  • This approach offers a valuable tool for building comprehensive protein-protein interaction maps and evaluating experimental data.