Abstract
Evaluating or predicting the quality of protein models (i.e., predicted protein tertiary structures) without knowing their native structures is important for selecting and appropriately using protein models. We describe an iterative approach that improves the performances of protein Model Quality Assurance Programs (MQAPs). Given the initial quality scores of a list of models assigned by a MQAP, the method iteratively refines the scores until the ranking of the models does not change. We applied the method to the model quality assessment data generated by 30 MQAPs during the Eighth Critical Assessment of Techniques for Protein Structure Prediction. To various degrees, our method increased the average correlation between predicted and real quality scores of 25 out of 30 MQAPs and reduced the average loss (i.e., the difference between the top ranked model and the best model) for 28 MQAPs. Particularly, for MQAPs with low average correlations (<0.4), the correlation can be increased by several times. Similar experiments conducted on the CASP9 MQAPs also demonstrated the effectiveness of the method. Our method is a hybrid method that combines the original method of a MQAP and the pair-wise comparison clustering method. It can achieve a high accuracy similar to a full pair-wise clustering method, but with much less computation time when evaluating hundreds of models. Furthermore, without knowing native structures, the iterative refining method can evaluate the performance of a MQAP by analyzing its model quality predictions. Published by Wiley-Blackwell.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 142-151 |
| Number of pages | 10 |
| Journal | Protein Science |
| Volume | 21 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2012 |
| Externally published | Yes |
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Keywords
- Iterative algorithm
- Model ranking
- Protein model quality assessment
- Protein structure prediction
ASJC Scopus subject areas
- Biochemistry
- Molecular Biology
Cite this
An iterative self-refining and self-evaluating approach for protein model quality estimation. / Wang, Zheng; Cheng, Jianlin.
In: Protein Science, Vol. 21, No. 1, 01.01.2012, p. 142-151.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An iterative self-refining and self-evaluating approach for protein model quality estimation
AU - Wang, Zheng
AU - Cheng, Jianlin
PY - 2012/1/1
Y1 - 2012/1/1
N2 - Evaluating or predicting the quality of protein models (i.e., predicted protein tertiary structures) without knowing their native structures is important for selecting and appropriately using protein models. We describe an iterative approach that improves the performances of protein Model Quality Assurance Programs (MQAPs). Given the initial quality scores of a list of models assigned by a MQAP, the method iteratively refines the scores until the ranking of the models does not change. We applied the method to the model quality assessment data generated by 30 MQAPs during the Eighth Critical Assessment of Techniques for Protein Structure Prediction. To various degrees, our method increased the average correlation between predicted and real quality scores of 25 out of 30 MQAPs and reduced the average loss (i.e., the difference between the top ranked model and the best model) for 28 MQAPs. Particularly, for MQAPs with low average correlations (<0.4), the correlation can be increased by several times. Similar experiments conducted on the CASP9 MQAPs also demonstrated the effectiveness of the method. Our method is a hybrid method that combines the original method of a MQAP and the pair-wise comparison clustering method. It can achieve a high accuracy similar to a full pair-wise clustering method, but with much less computation time when evaluating hundreds of models. Furthermore, without knowing native structures, the iterative refining method can evaluate the performance of a MQAP by analyzing its model quality predictions. Published by Wiley-Blackwell.
AB - Evaluating or predicting the quality of protein models (i.e., predicted protein tertiary structures) without knowing their native structures is important for selecting and appropriately using protein models. We describe an iterative approach that improves the performances of protein Model Quality Assurance Programs (MQAPs). Given the initial quality scores of a list of models assigned by a MQAP, the method iteratively refines the scores until the ranking of the models does not change. We applied the method to the model quality assessment data generated by 30 MQAPs during the Eighth Critical Assessment of Techniques for Protein Structure Prediction. To various degrees, our method increased the average correlation between predicted and real quality scores of 25 out of 30 MQAPs and reduced the average loss (i.e., the difference between the top ranked model and the best model) for 28 MQAPs. Particularly, for MQAPs with low average correlations (<0.4), the correlation can be increased by several times. Similar experiments conducted on the CASP9 MQAPs also demonstrated the effectiveness of the method. Our method is a hybrid method that combines the original method of a MQAP and the pair-wise comparison clustering method. It can achieve a high accuracy similar to a full pair-wise clustering method, but with much less computation time when evaluating hundreds of models. Furthermore, without knowing native structures, the iterative refining method can evaluate the performance of a MQAP by analyzing its model quality predictions. Published by Wiley-Blackwell.
KW - Iterative algorithm
KW - Model ranking
KW - Protein model quality assessment
KW - Protein structure prediction
UR - http://www.scopus.com/inward/record.url?scp=84455168000&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84455168000&partnerID=8YFLogxK
U2 - 10.1002/pro.764
DO - 10.1002/pro.764
M3 - Article
C2 - 22057943
AN - SCOPUS:84455168000
VL - 21
SP - 142
EP - 151
JO - Protein Science
JF - Protein Science
SN - 0961-8368
IS - 1
ER -