Zhang Jian, Qian Jingjing, Zou Quan, Zhou Feng, Kurgan Lukasz
School of Computer and Information Technology, Xinyang Normal University, Xinyang, China.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Methods Mol Biol. 2025;2870:1-19. doi: 10.1007/978-1-0716-4213-9_1.
The secondary structures (SSs) and supersecondary structures (SSSs) underlie the three-dimensional structure of proteins. Prediction of the SSs and SSSs from protein sequences enjoys high levels of use and finds numerous applications in the development of a broad range of other bioinformatics tools. Numerous sequence-based predictors of SS and SSS were developed and published in recent years. We survey and analyze 45 SS predictors that were released since 2018, focusing on their inputs, predictive models, scope of their prediction, and availability. We also review 32 sequence-based SSS predictors, which primarily focus on predicting coiled coils and beta-hairpins and which include five methods that were published since 2018. Substantial majority of these predictive tools rely on machine learning models, including a variety of deep neural network architectures. They also frequently use evolutionary sequence profiles. We discuss details of several modern SS and SSS predictors that are currently available to the users and which were published in higher impact venues.
二级结构（SSs）和超二级结构（SSSs）构成了蛋白质的三维结构。从蛋白质序列预测SSs和SSSs的方法得到了广泛应用，并在众多其他生物信息学工具的开发中有着大量应用。近年来，人们开发并发表了许多基于序列的SS和SSS预测器。我们调查并分析了自2018年以来发布的45个SS预测器，重点关注它们的输入、预测模型、预测范围和可用性。我们还回顾了32个基于序列的SSS预测器，这些预测器主要侧重于预测卷曲螺旋和β-发夹结构，其中包括2018年以来发表的五种方法。这些预测工具绝大多数依赖机器学习模型，包括各种深度神经网络架构。它们还经常使用进化序列概况。我们讨论了目前用户可以使用的、发表在影响力较大的期刊上的几种现代SS和SSS预测器的细节。
