This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure prediction. Previous studies showed that deep neural networks had uplifted the accuracy of three-state secondary structure prediction to more than 80%. Favored deep learning methods, such as convolutional neural networks, recurrent neural networks, inception networks, and graph neural networks, have been implemented in protein secondary structure prediction. Methods adapted from natural language processing and computer vision are also employed, including attention mechanism, ResNet, and U-shape networks. In the post-AlphaFold era, PSSP studies focus on different objectives, such as enhancing the quality of evolutionary information and exploiting protein language models as the PSSP input. The recent trend to utilize pre-trained language models as input features for secondary structure prediction provides a new direction for PSSP studies. Moreover, the state-of-the-art accuracy achieved by previous PSSP models is still below its theoretical limit. There are still rooms for improvement to be made in the field.

本文旨在全面回顾深度神经网络用于蛋白质二级结构预测（PSSP）的趋势和挑战。近年来，深度神经网络已成为蛋白质二级结构预测的主要方法。此前的研究表明，深度神经网络已将三态二级结构预测的准确率提升至80%以上。受欢迎的深度学习方法，如卷积神经网络、循环神经网络、初始网络和图神经网络，已在蛋白质二级结构预测中得到应用。还采用了自然语言处理和计算机视觉的方法，包括注意力机制、ResNet 和 U 形网络。在后 AlphaFold 时代，PSSP 研究侧重于不同的目标，例如提高进化信息的质量和利用蛋白质语言模型作为 PSSP 输入。最近利用预训练语言模型作为二级结构预测的输入特征的趋势为 PSSP 研究提供了新的方向。此外，以前的 PSSP 模型所达到的最先进的精度仍然低于其理论极限。该领域仍有改进的空间。