Bagging MSA Learning: Enhancing Low-Quality PSSM with Deep Learning for Accurate Protein Structure Property Prediction
Bagging MSA Learning: Enhancing Low-Quality PSSM with Deep Learning for Accurate Protein Structure Property Prediction
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DOI：
10.1007/978-3-030-45257-5_6
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发表时间：
2020-05
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影响因子：
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通讯作者：
Yuzhi Guo;Jiaxiang Wu;Hehuan Ma;Sheng Wang;Junzhou Huang
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作者：
Yuzhi Guo;Jiaxiang Wu;Hehuan Ma;Sheng Wang;Junzhou Huang
准确预测蛋白质的结构特性，如二级结构和溶剂可及性，对于分析蛋白质的结构和功能至关重要。PSSM（Position-Specific Scoring Matrix）特征在结构性能预测中有着广泛的应用。然而，由于同源序列不足，一些蛋白质可能具有低质量的PSSM特征，导致预测精度有限。为了解决这个问题，我们提出了一个PSSM功能的增强方案。我们引入了“Bagging MSA”方法来计算用于训练我们模型的PSSM特征，并采用卷积网络来捕获局部上下文特征和双向LSTM的长期依赖关系，并将它们集成在无监督框架下。然后，在这种增强的PSSM特征上建立结构性质预测模型，以进行更准确的预测。CB 513，CASP 11和CASP 12数据集的实证评估表明，我们的无监督增强方案确实产生了更多信息的PSSM特征的结构属性预测。
Accurate predictions of protein structure properties,e.g.secondary structure and solvent accessibility, are essential in analyzing the structure and function of a protein. PSSM (Position-Specific Scoring Matrix) features are widely used in the structure property prediction. However, some proteins may have low-quality PSSM features due to insufficient homologous sequences, leading to limited prediction accuracy. To address this limitation, we propose an enhancing scheme for PSSM features. We introduce the “Bagging MSA” method to calculate PSSM features used to train our model, and adopt a convolutional network to capture local context features and bidirectional-LSTM for long-term dependencies, and integrate them under an unsupervised framework. Structure property prediction models are then built upon such enhanced PSSM features for more accurate predictions. Empirical evaluation of CB513, CASP11, and CASP12 datasets indicate that our unsupervised enhancing scheme indeed generates more informative PSSM features for structure property prediction.