期刊
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
卷 71, 期 3, 页码 1211-1218出版社
WILEY
DOI: 10.1002/prot.21813
关键词
model quality assessment prediction; TASSER; SP3
资金
- NIGMS NIH HHS [GM-37408, R01 GM048835-15, R01 GM037408, R01 GM037408-20, GM-48835, R01 GM048835] Funding Source: Medline
In this work, we develop a fully automated method for the quality assessment prediction of protein structural models generated by structure prediction approaches such as fold recognition servers, or ab initio methods. The approach is based on fragment comparisons and a consensus C-alpha contact potential derived from the set of models to be assessed and was tested on CASP7 server models. The average Pearson linear correlation coefficient between predicted quality and model GDT-score per target is 0.83 for the 98 targets, which is better than those of other quality assessment methods that participated in CASP7. Our method also outperforms the other methods by about 3% as assessed by the total GDT-score of the selected top models.
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