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Updated: May 8, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure
Renxiang Yan1, Dong Xu, Jianyi Yang
1Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Ave, Ann Arbor, MI 48109.
Profile-profile alignment methods significantly outperform sequence-based approaches for protein fold recognition. Incorporating structural features further enhances accuracy, but the fold-recognition problem remains unsolved.
Area of Science:
- Computational Biology
- Bioinformatics
- Structural Biology
Background:
- Protein sequence alignment is crucial for predicting protein structure and function.
- Accurate alignment is key for template-based structure prediction.
Purpose of the Study:
- To benchmark 20 sequence alignment algorithms for protein fold recognition.
- To compare the performance of sequence-based, sequence-profile, and profile-profile alignment methods.
Main Methods:
- Benchmarking 20 diverse sequence alignment algorithms on 538 non-redundant proteins.
- Utilizing a uniform template library for fold-recognition assessment.
- Evaluating alignment methods based on profile generation (PSI-BLAST PSSM vs. HMM) and structure feature incorporation.
Main Results:
- Profile-profile alignment methods showed a dominant advantage, yielding TM-scores 26.5% and 49.8% higher than sequence-profile and sequence-sequence methods, respectively.
- No significant difference was observed between PSI-BLAST PSSM and HMM-based profile generation.
- Incorporating predicted or native structure features improved profile-profile alignment accuracy by 9.6% or 21.4%.
Conclusions:
- Profile-profile methods are superior for protein fold recognition.
- While structural features enhance accuracy, they do not fully solve the fold-recognition challenge.
- Current methods, even with structural data, fall short of TM-align performance, indicating limitations in solely improving feature prediction accuracy.
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