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Estimating Protein Structure Prediction Models Quality Using Convolutional Neural Networks

机译：使用卷积神经网络估算蛋白质结构预测模型的质量

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Methods for estimating the accuracy of structure prediction models (EMA) are crucial in modern protein structure prediction pipelines. State-of-art EMA methods use Support Vector Machines as an inference engine. Convolutional Neural Networks (CNN) are widely used in pattern recognition tasks like image classification and speech recognition. We approach the EMA problem as a classification task and perform training of CNNs to estimate GDT TS and lDDT class ranges from the secondary structure and relative solvent exposure consensus as one-dimensional information using several datasets built from CASP assessments data. Our results show that CNNs models can achieve accuracies near 80.0% classifying proteins structures of the same sequence, and accuracies near 30.0% for structures of different sequences. This potentially indicates a data scarcity problem and a deficiency of transferability of the consensus information. However, the results strongly suggest the applicability of CNNs to the EMA problem.
机译：估计结构预测模型（EMA）准确性的方法在现代蛋白质结构预测管道中至关重要。最新的EMA方法使用支持向量机作为推理引擎。卷积神经网络（CNN）被广泛用于模式识别任务，例如图像分类和语音识别。我们使用EMA问题作为分类任务，并使用来自CASP评估数据的数个数据集对CNN进行训练，以从二级结构和相对溶剂暴露共识中将GDT TS和lDDT分类范围估计为一维信息。我们的结果表明，CNNs模型可以对同一序列的蛋白质结构进行分类的准确度接近80.0％，而对不同序列的蛋白质结构进行分类的准确度则接近30.0％。这潜在地表明数据短缺问题和共识信息的可传递性不足。但是，结果强烈暗示了CNN在EMA问题上的适用性。

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