Goulet Adeline, Cambillau Christian
Laboratoire D'Ingénierie des Systèmes Macromoléculaires (LISM), Institut de Microbiologie, Aix-Marseille Université-CNRS, Marseille, France.
School of Microbiology, University College Cork, Cork, Ireland.
Front Mol Biosci. 2022 May 9;9:907452. doi: 10.3389/fmolb.2022.907452. eCollection 2022.
In 2021, the release of AlphaFold2 - the DeepMind's machine-learning protein structure prediction program - revolutionized structural biology. Results of the CASP14 contest were an immense surprise as AlphaFold2 successfully predicted 3D structures of nearly all submitted protein sequences. The AlphaFold2 craze has rapidly spread the life science community since structural biologists as well as untrained biologists have now the possibility to obtain high-confidence protein structures. This revolution is opening new avenues to address challenging biological questions. Moreover, AlphaFold2 is imposing itself as an essential step of any structural biology project, and requires us to revisit our structural biology workflows. On one hand, AlphaFold2 synergizes with experimental methods including X-ray crystallography and cryo-electron microscopy. On the other hand, it is, to date, the only method enabling structural analyses of large and flexible assemblies resistant to experimental approaches. We illustrate this valuable application of AlphaFold2 with the structure prediction of the whole host adhesion device from the bacteriophage J-1. With the ongoing improvement of AlphaFold2 algorithms and notebooks, there is no doubt that AlphaFold2-driven biological stories will increasingly be reported, which questions the future directions of experimental structural biology.
2021年，深度思维公司的机器学习蛋白质结构预测程序AlphaFold2的发布给结构生物学带来了变革。在蛋白质结构预测技术关键评估第14轮（CASP14）竞赛中，AlphaFold2成功预测了几乎所有提交的蛋白质序列的三维结构，其结果令人大为惊讶。自那以来，AlphaFold2热潮迅速在生命科学界蔓延，因为结构生物学家以及非专业生物学家现在都有可能获得高可信度的蛋白质结构。这场变革为解决具有挑战性的生物学问题开辟了新途径。此外，AlphaFold2已成为任何结构生物学项目的关键步骤，这要求我们重新审视结构生物学的工作流程。一方面，AlphaFold2与包括X射线晶体学和冷冻电子显微镜在内的实验方法相辅相成。另一方面，到目前为止，它是唯一一种能够对难以用实验方法分析的大型柔性组装体进行结构分析的方法。我们以噬菌体J-1整个宿主粘附装置的结构预测为例，说明了AlphaFold2的这一重要应用。随着AlphaFold2算法和笔记本的不断改进，毫无疑问，由AlphaFold2推动的生物学研究成果将越来越多地被报道，这也引发了对实验结构生物学未来方向的思考。
