Assessment of the model refinement category in CASP12.
Assessment of the model refinement category in CASP12.
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DOI：
10.1002/prot.25409
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发表时间：
2018-03
期刊：
影响因子：
2.9
通讯作者：
Gervasio FL
中科院分区：
文献类型：
--
作者：
Hovan L;Oleinikovas V;Yalinca H;Kryshtafovych A;Saladino G;Gervasio FL
我们在这里报告的评估提交给第12次实验对蛋白质结构预测的关键评估（CASP12）的模型改进预测。这是自CASP 8（2008）以来的第五次细化实验，与之前的实验一样，预测者被邀请细化在CASP实验的常规（非细化）阶段收到的选定服务器模型。我们使用标准CASP措施的组合来评估提交的模型。Z分数的线性组合的系数（CASP12分数）已经通过在视觉检查结果上训练的机器学习算法获得。我们确定了8个组，提高了大多数目标的骨架构象和侧链定位。尽管顶级方法采用了截然不同的方法，但它们的整体性能几乎没有区别，每个方法在不同的分数或目标子集中表现出色。更重要的是，有一些新的方法，虽然在大多数情况下做得比平均水平差，但为一些目标提供了最好的改进，显示出该领域进一步创新的巨大空间。
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.
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通讯作者：
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