School of Biological Sciences, University of Reading, Reading, UK.
Methods Mol Biol. 2025;2867:121-139. doi: 10.1007/978-1-0716-4196-5_7.
Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularly with the development of AlphaFold2 (AF2), which can analyze sequences of amino acids and predict 3D structures with near experimental accuracy. Since the release of AF2, numerous studies have been conducted, either using AF2 directly for large-scale modeling or building upon the software for other use cases. Many reviews have been published discussing the impact of AF2 in the field of protein bioinformatics, particularly in relation to neural networks, which have highlighted what AF2 can and cannot do. It is evident that AF2 and similar approaches are open to further development and several new approaches have emerged, in addition to older refinement approaches, for improving the quality of predictions. Here we provide a brief overview, aimed at the general biologist, of how machine learning techniques have been used for improvement of 3D models of proteins following AF2, and we highlight the impacts of these approaches. In the most recent experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP15), the most successful groups all developed their own tools for protein structure modeling that were based at least in some part on AF2. This improvement involved employing techniques such as generative modeling, changing parameters such as dropout to generate more AF2 structures, and data-driven approaches including using alternative templates and MSAs.
蛋白质结构预测是分子生物学的基础，在药物发现和蛋白质工程等领域有许多应用。机器学习技术近年来极大地推动了蛋白质 3D 建模的发展，特别是随着 AlphaFold2（AF2）的发展，它可以分析氨基酸序列并以接近实验精度预测 3D 结构。自 AF2 发布以来，已经进行了许多研究，要么直接使用 AF2 进行大规模建模，要么在该软件的基础上构建其他用例。许多评论已经发表，讨论了 AF2 在蛋白质生物信息学领域的影响，特别是在神经网络方面，这些评论强调了 AF2 能做什么和不能做什么。显然，AF2 和类似的方法还有进一步发展的空间，除了旧的细化方法外，还有一些新的方法已经出现，用于提高预测的质量。在这里，我们为一般生物学家提供了一个简要的概述，介绍了机器学习技术如何在 AF2 之后用于改进蛋白质 3D 模型，并且强调了这些方法的影响。在最近的蛋白质结构预测技术评估（CASP15）实验中，最成功的小组都开发了自己的蛋白质结构建模工具，这些工具至少部分基于 AF2。这种改进涉及采用生成式建模等技术，改变诸如辍学率等参数以生成更多的 AF2 结构，以及包括使用替代模板和多序列比对在内的数据驱动方法。
