Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind’s AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.

近年来人工智能驱动的蛋白质结构预测进展提出了一个问题：蛋白质结构预测问题是否已经解决？在这里，我们重点关注非球状蛋白，强调 DeepMind 的 AlphaFold2 在其生物和治疗应用中的许多优点和潜在缺点。我们使用预测的局部距离差异测试 (pLDDT) 和预测的对齐误差 (PAE) 值总结了与评估 AlphaFold2 模型质量和可靠性相关的微妙之处。我们重点介绍了 AlphaFold2 可应用的各类蛋白质以及涉及的注意事项。讨论了 AlphaFold2 模型如何与小角 X 射线散射 (SAXS)、溶液 NMR、冷冻电子显微镜 (cryo-EM) 和 X 射线衍射形式的实验数据集成的具体示例。最后，我们强调需要超越刚性、静态结构快照的结构预测，转向构象系综和替代生物学相关状态。总体主题是，在使用 AlphaFold2 生成的模型来生成可测试的假设和结构模型时，应该仔细考虑，而不是将预测模型视为事实上的地面实况结构。