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A single-model quality assessment method for poor quality protein structure.
Ouyang, Jianquan; Huang, Ningqiao; Jiang, Yunqi.
Afiliação
  • Ouyang J; key Laboratory of Intelligent Computing & Information Processing, Ministry of Education, Xiangtan University, Xiangtan, China. 27894827@qq.com.
  • Huang N; key Laboratory of Intelligent Computing & Information Processing, Ministry of Education, Xiangtan University, Xiangtan, China.
  • Jiang Y; College of Chemistry, Xiangtan University, Xiangtan, China.
BMC Bioinformatics ; 21(1): 157, 2020 Apr 25.
Article em En | MEDLINE | ID: mdl-32334508
ABSTRACT

BACKGROUND:

Quality assessment of protein tertiary structure prediction models, in which structures of the best quality are selected from decoys, is a major challenge in protein structure prediction, and is crucial to determine a model's utility and potential applications. Estimating the quality of a single model predicts the model's quality based on the single model itself. In general, the Pearson correlation value of the quality assessment method increases in tandem with an increase in the quality of the model pool. However, there is no consensus regarding the best method to select a few good models from the poor quality model pool.

RESULTS:

We introduce a novel single-model quality assessment method for poor quality models that uses simple linear combinations of six features. We perform weighted search and linear regression on a large dataset of models from the 12th Critical Assessment of Protein Structure Prediction (CASP12) and benchmark the results on CASP13 models. We demonstrate that our method achieves outstanding performance on poor quality models.

CONCLUSIONS:

According to results of poor protein structure assessment based on six features, contact prediction and relying on fewer prediction features can improve selection accuracy.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas / Modelos Moleculares Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas / Modelos Moleculares Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article