Hovan Ladislav, Oleinikovas Vladimiras, Yalinca Havva, Kryshtafovych Andriy, Saladino Giorgio, Gervasio Francesco Luigi
Department of Chemistry, University College London, WC1E 6BT, United Kingdom.
Genome Center, University of California, Davis, California, 95616.
Proteins. 2018 Mar;86 Suppl 1(Suppl 1):152-167. doi: 10.1002/prot.25409. Epub 2017 Nov 29.
We here report on the assessment of the model refinement predictions submitted to the 12th Experiment on the Critical Assessment of Protein Structure Prediction (CASP12). This is the fifth refinement experiment since CASP8 (2008) and, as with the previous experiments, the predictors were invited to refine selected server models received in the regular (nonrefinement) stage of the CASP experiment. We assessed the submitted models using a combination of standard CASP measures. The coefficients for the linear combination of Z-scores (the CASP12 score) have been obtained by a machine learning algorithm trained on the results of visual inspection. We identified eight groups that improve both the backbone conformation and the side chain positioning for the majority of targets. Albeit the top methods adopted distinctively different approaches, their overall performance was almost indistinguishable, with each of them excelling in different scores or target subsets. What is more, there were a few novel approaches that, while doing worse than average in most cases, provided the best refinements for a few targets, showing significant latitude for further innovation in the field.
我们在此报告对提交至第十二届蛋白质结构预测关键评估实验（CASP12）的模型优化预测结果的评估。这是自2008年CASP8以来的第五次优化实验，与之前的实验一样，预测者被邀请对在CASP实验常规（非优化）阶段收到的选定服务器模型进行优化。我们使用标准CASP指标的组合来评估提交的模型。Z分数线性组合（CASP12分数）的系数是通过基于视觉检查结果训练的机器学习算法获得的。我们识别出八组方法，它们对大多数目标的主链构象和侧链定位都有改善。尽管顶级方法采用了截然不同的方法，但其总体性能几乎难以区分，每种方法在不同的分数或目标子集中表现出色。此外，有一些新颖的方法，虽然在大多数情况下表现不如平均水平，但对少数目标提供了最佳的优化结果，显示出该领域进一步创新的巨大空间。
