Hasan A K M Mehedi, Ahmed Ajmain Yasar, Mahbub Sazan, Rahman M Saifur, Bayzid Md Shamsuzzoha
Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh.
Department of Computer Science, University of Maryland, College Park, MD 20742, USA.
Bioinform Adv. 2023 Apr 5;3(1):vbad042. doi: 10.1093/bioadv/vbad042. eCollection 2023.
Protein structure provides insight into how proteins interact with one another as well as their functions in living organisms. Protein backbone torsion angles ( and ) prediction is a key sub-problem in predicting protein structures. However, reliable determination of backbone torsion angles using conventional experimental methods is slow and expensive. Therefore, considerable effort is being put into developing computational methods for predicting backbone angles.
We present SAINT-Angle, a highly accurate method for predicting protein backbone torsion angles using a self-attention-based deep learning network called SAINT, which was previously developed for the protein secondary structure prediction. We extended and improved the existing SAINT architecture as well as used transfer learning to predict backbone angles. We compared the performance of SAINT-Angle with the state-of-the-art methods through an extensive evaluation study on a collection of benchmark datasets, namely, TEST2016, TEST2018, TEST2020-HQ, CAMEO and CASP. The experimental results suggest that our proposed self-attention-based network, together with transfer learning, has achieved notable improvements over the best alternate methods.
SAINT-Angle is freely available as an open-source project at https://github.com/bayzidlab/SAINT-Angle.
Supplementary data are available at online.
蛋白质结构有助于深入了解蛋白质之间的相互作用及其在生物体内的功能。蛋白质主链扭转角（φ和ψ）预测是蛋白质结构预测中的一个关键子问题。然而，使用传统实验方法可靠地确定主链扭转角既缓慢又昂贵。因此，人们正在投入大量精力开发预测主链角度的计算方法。
我们提出了SAINT-Angle，这是一种使用名为SAINT的基于自注意力的深度学习网络来预测蛋白质主链扭转角的高精度方法，SAINT先前是为蛋白质二级结构预测而开发的。我们扩展并改进了现有的SAINT架构，并使用迁移学习来预测主链角度。我们通过对一系列基准数据集（即TEST2016、TEST2018、TEST2020-HQ、CAMEO和CASP）进行广泛的评估研究，将SAINT-Angle的性能与最先进的方法进行了比较。实验结果表明，我们提出的基于自注意力的网络以及迁移学习，相对于最佳替代方法取得了显著改进。
SAINT-Angle作为一个开源项目可在https://github.com/bayzidlab/SAINT-Angle上免费获取。
补充数据可在网上获取。
