The value of protein models obtained with automated protein structure prediction depends primarily on their accuracy. Protein model quality assessment is thus critical to select the model that can best answer biologically relevant questions from an ensemble of predictions. However, despite many advances in the field, different methods capture different types of errors, begging the question of which method to use. We introduce TopScore, a meta Model Quality Assessment Program (meta-MQAP) that uses deep neural networks to combine scores from 15 different primary predictors to predict accurate residue-wise and whole-protein error estimates. The predictions on six large independent data sets are highly correlated to superposition-independent errors in the model, achieving a Pearson’s R_all² of 0.93 and 0.78 for whole-protein and residue-wise error predictions, respectively. This is a significant improvement over any of the investigated primary MQAPs, demonstrating that much can be gained by optimally combining different methods and using different and very large data sets.

中文翻译：

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TopScore：使用深度神经网络和大量多样的数据集进行准确的蛋白质模型质量评估

通过自动蛋白质结构预测获得的蛋白质模型的价值主要取决于其准确性。因此，蛋白质模型质量评估对于选择能够从一组预测中最好地回答生物学相关问题的模型至关重要。但是，尽管在该领域取得了许多进步，但是不同的方法捕获了不同类型的错误，这引出了使用哪种方法的问题。我们介绍了TopScore，这是一个元模型质量评估程序（meta-MQAP），该程序使用深度神经网络来组合来自15个不同主要预测变量的分数，以预测准确的残基和全蛋白质错误估计值。对六个大型独立数据集的预测与模型中与叠加无关的误差高度相关，从而实现了Pearson's R _all ²全蛋白预测和残基预测误差分别为0.93和0.78。这是对任何已研究的主要MQAP的重大改进，表明可以通过最佳地组合不同的方法以及使用不同的非常大的数据集来获得很多收益。