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Tagging and Fusion Proteins01:24

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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Protein Folding01:25

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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
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Related Experiment Video

Updated: Dec 9, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm.

Jiangyi Shao1, Bin Liu2

  • 1School of Computer Science and Technology, Beijing Institute of Technology, China.

Briefings in Bioinformatics
|September 6, 2020
PubMed
Summary

We introduce ProtFold-DFG, a novel network-based predictor for protein fold recognition. This method improves accuracy by fusing ranking lists using a Directed Fusion Graph (DFG) and PageRank, outperforming 35 existing methods.

Keywords:
Directed Fusion GraphKL divergencePageRankprotein fold recognitiontransitive closure

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural biology

Background:

  • Protein fold recognition is crucial for protein structure prediction.
  • Existing computational methods have limitations.
  • Machine learning and AI have advanced prediction techniques.

Purpose of the Study:

  • To develop a novel and accurate computational predictor for protein fold recognition.
  • To address limitations of current protein fold recognition methods.

Main Methods:

  • Proposed a network-based predictor, ProtFold-DFG.
  • Introduced Directed Fusion Graph (DFG) to fuse ranking lists from multiple methods.
  • Utilized transitive closure and KL divergence within the DFG for enhanced generalization.
  • Applied the PageRank algorithm on the DFG to leverage global protein interactions.

Main Results:

  • ProtFold-DFG demonstrated superior performance on the LINDAHL dataset.
  • Outperformed 35 competing protein fold recognition methods.
  • Indicated improved generalization ability and accuracy in fold recognition.

Conclusions:

  • ProtFold-DFG is a highly effective method for protein fold recognition.
  • The Directed Fusion Graph approach enhances prediction accuracy by considering global protein interactions.
  • ProtFold-DFG offers a valuable tool for the scientific community.