Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh.
Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh.
Bioinformatics. 2020 Nov 1;36(17):4599-4608. doi: 10.1093/bioinformatics/btaa531.
Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g. X-ray crystallography and nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the SS of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of SS contains more useful information and is much more challenging than the Q3 prediction.
We present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception network in order to effectively capture both the short- and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step toward the accurate and reliable prediction of SSs of proteins.
SAINT is freely available as an open-source project at https://github.com/SAINTProtein/SAINT.
蛋白质结构为研究它们与其他蛋白质的相互作用、在生物体中的功能和生物学作用提供了基本的认识。预测蛋白质二级结构（SS）的实验方法（例如 X 射线晶体学和核磁共振波谱学）非常昂贵且耗时。因此，开发有效的计算方法来预测蛋白质的 SS 至关重要。开发高度准确的 SS 预测方法的进展主要集中在 3 类（Q3）结构预测上。然而，8 类（Q8）分辨率的 SS 包含更多有用的信息，比 Q3 预测更具挑战性。
我们提出了 SAINT，这是一种用于 Q8 结构预测的高度准确的方法，它将自注意力机制（来自自然语言处理的概念）与 Deep Inception-Inception 网络相结合，以有效捕捉氨基酸残基之间的短程和长程相互作用。SAINT 提供了比典型的黑盒深度神经网络方法更具可解释性的框架。通过广泛的评估研究，我们报告了 SAINT 在一系列基准数据集（即 TEST2016、TEST2018、CASP12 和 CASP13）上与现有最佳方法的性能比较。我们的结果表明，自注意力机制提高了预测精度，优于现有的最佳替代方法。SAINT 是首创的，提供了已知的最佳 Q8 精度。因此，我们相信 SAINT 代表了朝着准确可靠地预测蛋白质 SS 迈出的重要一步。
SAINT 作为一个开源项目免费提供，可在 https://github.com/SAINTProtein/SAINT 上获得。
