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Updated: Dec 1, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
ReFold-MAP: Protein remote homology detection and fold recognition based on features extracted from profiles
Yichen Guo1, Ke Yan1, Hao Wu1
1School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
A new method, ReFold-MAP, improves protein remote homology detection and fold recognition. It uses comprehensive MAP features, combining structural motifs, evolutionary, and sequence information for superior performance.
Area of Science:
- Computational Biology
- Bioinformatics
- Structural Biology
Background:
- Protein remote homology detection and fold recognition are crucial for predicting protein structure and function.
- Existing methods include discriminative, alignment, and ranking approaches.
Purpose of the Study:
- To propose a novel discriminative method, ReFold-MAP, for enhanced protein remote homology detection and fold recognition.
Main Methods:
- ReFold-MAP extracts comprehensive features, termed MAP features.
- MAP features integrate structural motif kernel information, evolutionary information (via PSSM), and sequence data.
Main Results:
- ReFold-MAP demonstrated superior performance compared to existing methods in experimental evaluations.
- The proposed method effectively utilizes combined feature sets for improved accuracy.
Conclusions:
- ReFold-MAP offers a significant advancement in protein homology detection and fold recognition.
- This method is a valuable tool for researchers in structural and functional genomics.
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