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Related Concept Videos

Protein Networks02:26

Protein Networks

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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,...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Related Experiment Video

Updated: Aug 7, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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Evaluating network-based missing protein prediction using p-values, Bayes Factors, and probabilities.

Wilson Wen Bin Goh1,2,3, Weijia Kong1,3, Limsoon Wong4

  • 1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore.

Journal of Bioinformatics and Computational Biology
|March 9, 2023
PubMed
Summary
This summary is machine-generated.

Comparing prediction methods is challenging due to differing output formats. This study introduces two novel strategies, false discovery rate (FDR) estimation and "home ground testing," outperforming traditional Bayes Factor upper Bound (BFB) conversions for missing protein prediction.

Keywords:
Bayes factorsPROTRECStatistics[Formula: see text]-valuesdata sciencemachine learningmissing proteinsnetworks

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Prediction methods in bioinformatics often use different output metrics (probabilities vs. p-values), hindering direct comparison.
  • Existing conversion methods like Bayes Factor upper Bound (BFB) may rely on flawed assumptions for cross-method validation.

Purpose of the Study:

  • To develop and evaluate robust strategies for comparing diverse prediction methods in proteomics.
  • To address the challenge of cross-comparing methods that output probabilities versus those that output p-values.

Main Methods:

  • Utilized a renal cancer proteomics dataset for missing protein prediction.
  • Implemented and compared two novel comparison strategies: false discovery rate (FDR) estimation and "home ground testing."
  • Evaluated performance against the traditional Bayes Factor upper Bound (BFB) conversion method.

Main Results:

  • Both FDR estimation and "home ground testing" demonstrated superior performance compared to BFB conversions.
  • The proposed strategies offer more reliable cross-method comparisons without making the same assumptions as BFB.

Conclusions:

  • Standardizing prediction method comparison to a common benchmark, such as global FDR, is recommended.
  • When standardization is not feasible, reciprocal "home ground testing" provides a viable alternative for comparing prediction methods.