AlphaFold two years on: Validation and impact

• 成果类型: Article
• 署名作者: Kovalevskiy, Oleg; Garcia, Juan Mateos-; Tunyasuvunakool, Kathryn
• 署名单位: Alphabet Inc.; DeepMind; Google Incorporated
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-13445
• DOI: 10.1073/pnas.2315002121
• 发表日期: 2024-08-20
• 关键词: protein-structure; prediction; MODEL; refinement

摘要

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.