Estimation of model accuracy in CASP13
Estimation of model accuracy in CASP13
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DOI：
10.1002/prot.25767
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发表时间：
2019-07-16
影响因子：
2.9
通讯作者：
Wallner, Bjorn
中科院分区：
文献类型：
--
作者：
Chene, Jianlin;Choe, Myong-Ho;Wallner, Bjorn
可靠地估计蛋白质3D模型的准确性既是大多数蛋白质折叠管道的基本部分，也是在使用多条管道时可靠地识别最佳模型的重要因素。在这里，我们描述了从CASP12到CASP13在模型精度估计(EMA)领域取得的进展，从CASP13中最成功的方法的进展来看。我们显示了微小但明显的进展，即，当在CASP13 EMA目标上测试时，几种方法的性能优于CASP12中的最佳方法。通过将深度学习和残基-残基接触应用于模型精度预测，推动了一些进展。我们表明，最好的EMA方法选择的模型比CASP13中最好的服务器更好，但这一点存在着进一步改进的巨大潜力。此外，根据基于局部相似性的评估标准，例如lDDT和CAD，现在很明显，单一模型精度方法的表现相对好于基于共识的方法。
Methods to reliably estimate the accuracy of 3D models of proteins are both a fundamental part of most protein folding pipelines and important for reliable identification of the best models when multiple pipelines are used. Here, we describe the progress made from CASP12 to CASP13 in the field of estimation of model accuracy (EMA) as seen from the progress of the most successful methods in CASP13. We show small but clear progress, that is, several methods perform better than the best methods from CASP12 when tested on CASP13 EMA targets. Some progress is driven by applying deep learning and residue-residue contacts to model accuracy prediction. We show that the best EMA methods select better models than the best servers in CASP13, but that there exists a great potential to improve this further. Also, according to the evaluation criteria based on local similarities, such as lDDT and CAD, it is now clear that single model accuracy methods perform relatively better than consensus-based methods.