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Updated: Jul 25, 2026
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
CASP2 knowledge-based approach to distant homology recognition and fold prediction in CASP4
1Centre for Protein Engineering, MRC Centre, Cambridge, United Kingdom. agm@mrc-lmb.cam.ac.uk
Proteins
|February 9, 2002
Summary
This study refines protein structure prediction using a knowledge-based approach, enhancing distant homology recognition by leveraging extensive SCOP database information and improved alignment accuracy for better fold prediction.
Area of Science:
- Structural bioinformatics
- Computational biology
- Protein structure prediction
Background:
- The study builds upon a semimanual approach for protein structure prediction presented in CASP2 (1996), focusing on recognizing distant homology.
- This method relied on the Structural Classification of Proteins (SCOP) database for known structural and evolutionary relationships.
Purpose of the Study:
- To improve distant homology recognition in protein structure prediction using an enhanced knowledge-based approach.
- To refine the accuracy and completeness of protein structure models by leveraging increased data and expertise.
Main Methods:
- Utilized a knowledge-based approach centered on the SCOP database.
- Improved model completeness and alignment accuracy by using multiple distantly related structures and superfamily features.
- Applied the refined method to CASP4 (2000) for distant homology recognition and fold prediction.
Main Results:
- Predictions for distant homology recognition ranked among the top for most targets, with notable success for HI0065 and MalK (T0121C).
- Fold predictions for targets in new superfamilies showed lower average quality but achieved top rankings for chorismate lyase (T0086) and Appr>p cyclic phosphodiesterase (T0094).
Conclusions:
- The enhanced knowledge-based approach significantly improved distant homology recognition capabilities.
- The method demonstrates the value of comprehensive structural data and refined alignment techniques for accurate protein structure prediction.
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