Abstract：
Protein secondary structure prediction (PSSP) is an important and challenging task in protein bioinformatics. Protein secondary structures (SSs) are categorized in regular and irregular structure classes. Regular SSs, representing nearly 50% of amino acids consist of helices and sheets, whereas the remaining amino acids represent irregular SSs. [Formula: see text]-turns and [Formula: see text]-turns are the most abundant irregular SSs present in proteins. Existing methods are well developed for separate prediction of regular and irregular SSs. However, for more comprehensive PSSP, it is essential to develop a uniform model to predict all types of SSs simultaneously. In this work, using a novel dataset comprising dictionary of secondary structure of protein (DSSP)-based SSs and PROMOTIF-based [Formula: see text]-turns and [Formula: see text]-turns, we propose a unified deep learning model consisting of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) for simultaneous prediction of regular and irregular SSs. To the best of our knowledge, this is the first study in PSSP covering both regular and irregular structures. The protein sequences in our constructed datasets, RiR6069 and RiR513, have been borrowed from benchmark CB6133 and CB513 datasets, respectively. The results are indicative of increased PSSP accuracy.
摘要：
蛋白质二级结构预测（PSSP）是蛋白质生物信息学中一项重要而具有挑战性的任务。蛋白质二级结构（SS）分为规则和不规则结构类别。常规SS,代表近50%的氨基酸由螺旋和薄片组成，而其余的氨基酸代表不规则的SS。[公式：见文本]-turns和[公式：见文本]-turns是蛋白质中存在的最丰富的不规则SS。现有方法已很好地开发用于规则和不规则SS的单独预测。然而,对于更全面的PSSP，开发一个统一的模型来同时预测所有类型的SS是至关重要的。在这项工作中,使用一个新的数据集，包括基于蛋白质二级结构（DSSP）的SS和基于PROMOTIF的[公式：见文本]-turns和[公式：见文本]-turns，我们提出了一个由卷积神经网络（CNN）和长短期记忆网络（LSTM）组成的统一深度学习模型，用于同时预测规则和不规则SS。据我们所知,这是PSSP中首次涵盖规则和不规则结构的研究.我们构建的数据集中的蛋白质序列，RiR6069和RiR513是从基准CB6133和CB513数据集中借用的，分别。结果表明PSSP准确度提高。
