Kovalevskiy Oleg, Mateos-Garcia Juan, Tunyasuvunakool Kathryn
Google DeepMind, London N1C 4DN, United Kingdom.
Proc Natl Acad Sci U S A. 2024 Aug 20;121(34):e2315002121. doi: 10.1073/pnas.2315002121. Epub 2024 Aug 12.
Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model's capabilities and limitations.
自AlphaFold首次发布两年以来,我们见证了它作为一种结构预测工具被广泛采用。在此,我们讨论一些基于AlphaFold的最新工作,特别关注其在结构生物学领域的应用。这包括加速结构确定本身、开展新的计算研究以及构建新工具和工作流程等用例。我们还审视了AlphaFold正在进行的验证情况,因为其预测结果仍在与大量实验结构进行比较,以进一步明确该模型的能力和局限性。