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Deep Networks and Continuous Distributed Representation of Protein Sequences for Protein Quality Assessment

机译：用于蛋白质质量评估的蛋白质序列的深层网络和连续分布表示

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摘要

Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Although many single-model quality assessment (QA) methods have been developed, their accuracy is not good enough for most real applications. In this paper, a new approach, called DeepCon-QA, based on deep convolutional neural networks and continuous distributed representation of protein sequences is proposed for single-model QA problem. By combining novel features of protein vector representation with existing features of profile information, together with the power of deep networks, our method shows promising results in solving the problem of protein quality assessment. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DeepCon-QA obtained similar performance with the best state-of-the-art QA method MQAPmulti while outperforming other QA method.
机译：计算蛋白质结构预测对于生物信息学中的许多应用非常重要。在预测蛋白质结构的过程中，准确评估生成模型的质量至关重要。尽管已经开发了许多单模型质量评估（QA）方法，但对于大多数实际应用而言，它们的准确性仍不够好。本文针对单模型QA问题，提出了一种基于深度卷积神经网络和蛋白质序列连续分布表示的新方法DeepCon-QA。通过将蛋白质载体表示的新颖特征与简档信息的现有特征相结合，再加上深层网络的强大功能，我们的方法在解决蛋白质质量评估问题方面显示出令人鼓舞的结果。在对来自结构预测的关键评估（CASP）竞争中的选定目标进行的实验中，DeepCon-QA使用最佳的最新QA方法MQAPmulti获得了相似的性能，同时胜过其他QA方法。

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