APOLLO: a quality assessment service for single and multiple protein models
Zheng Wang1, Jesse Eickholt, Jianlin Cheng
1Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.
Bioinformatics (Oxford, England)
|May 7, 2011
Summary
APOLLO is a new web server that assesses protein model quality. It uses machine learning for single models and pair-wise comparisons for model pools, achieving high accuracy in predicting true model quality.
Area of Science:
- Computational biology
- Structural bioinformatics
- Protein structure prediction
Background:
- Protein quality assessment is crucial for structural biology.
- Existing methods for evaluating protein models have limitations.
- The APOLLO web server provides advanced quality assessment tools.
Purpose of the Study:
- To develop and evaluate a web server for protein model quality assessment.
- To assess both global and local quality of single protein models.
- To assess global and local qualities of protein model pools using pair-wise comparisons.
Main Methods:
- Machine learning methods for single model quality evaluation.
- Pair-wise comparison approach for assessing pools of models.
- Utilized 107 Critical Assessment of Techniques for Protein Structure Prediction (CASP9) targets for evaluation.
Main Results:
- Machine learning and pair-wise methods achieved high correlations (0.671 and 0.917, respectively) with true model quality scores.
- Predicted absolute local qualities showed an average difference of 2.60 Å from actual distances to native structure.
- The APOLLO server demonstrates significant accuracy in protein model quality prediction.
Conclusions:
- APOLLO provides accurate global and local quality assessment for protein models.
- The web server offers reliable predictions for both single and pooled model evaluations.
- APOLLO is a valuable tool for researchers in protein structure prediction.
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The primary structure of a protein is its amino acid sequence.
The primary structure of a protein is its amino acid sequence.
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